instruction
stringlengths 621
4.03k
| input
stringclasses 1
value | output
stringclasses 4
values |
|---|---|---|
Evaluate the creditworthiness of a customer with the following financial profile. Respond with only either 'good' or 'bad'. For instance, 'The client has a stable income, no previous debts, and owns a property.' should be classified as 'good'.
Text: 'The state of Status of existing checking account is smaller than 0 DM. The state of Duration in month is 27. The state of Credit history is no credits taken or all credits paid back duly. The state of Purpose is business. The state of Credit amount is 5293. The state of Savings account or bonds is smaller than 100 DM. The state of Present employment since is unemployed. The state of Installment rate in percentage of disposable income is 2. The state of Personal status and sex is male and single. The state of Other debtors or guarantors is none. The state of Present residence since is 4. The state of Property is building society savings agreement or life insurance. The state of Age in years is 50. The state of Other installment plans is stores. The state of Housing is own. The state of Number of existing credits at this bank is 2. The state of Job is skilled employee or official. The state of Number of people being liable to provide maintenance for is 1. The state of Telephone is yes, registered under the customers name. The state of foreign worker is yes. '
Answer:
|
bad
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 2.086, V2: 0.036, V3: -1.802, V4: 0.396, V5: 0.343, V6: -0.832, V7: 0.068, V8: -0.132, V9: 0.757, V10: -0.329, V11: -0.992, V12: -0.792, V13: -1.577, V14: -0.522, V15: 0.400, V16: 0.440, V17: 0.512, V18: -0.052, V19: 0.123, V20: -0.260, V21: -0.395, V22: -1.110, V23: 0.341, V24: 0.333, V25: -0.293, V26: 0.187, V27: -0.074, V28: -0.036, Amount: 0.890.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -1.977, V2: 2.022, V3: 0.384, V4: -0.561, V5: -0.932, V6: -0.517, V7: -1.131, V8: -1.764, V9: -0.371, V10: -1.287, V11: -1.116, V12: 0.592, V13: -0.422, V14: 0.895, V15: -0.424, V16: 0.389, V17: 0.186, V18: -0.459, V19: -0.425, V20: 0.539, V21: -1.396, V22: -0.035, V23: 0.263, V24: 0.385, V25: -0.192, V26: 0.286, V27: 0.122, V28: 0.150, Amount: 4.000.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.131, V2: -0.787, V3: -0.042, V4: -0.468, V5: -0.848, V6: -0.290, V7: -0.624, V8: 0.149, V9: -0.677, V10: 0.251, V11: 1.523, V12: -0.610, V13: -1.521, V14: -0.820, V15: -0.215, V16: 1.095, V17: 1.422, V18: -1.007, V19: 0.581, V20: 0.153, V21: 0.035, V22: -0.149, V23: -0.054, V24: -0.079, V25: 0.292, V26: -0.294, V27: 0.008, V28: 0.037, Amount: 100.210.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 6, the number of cards is 1, the credit balance is 1220, the number of transactions is 8, the number of international transactions is 15, the credit limit is 5.'
Answer:
|
good
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 3, the number of cards is 1, the credit balance is 5000, the number of transactions is 48, the number of international transactions is 1, the credit limit is 4.'
Answer:
|
good
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 5, Destination: ROMANIA, Net Sales: 12.0, Commission: 0.0, Age: 36.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: 1 way Comprehensive Plan, Duration: 9, Destination: SINGAPORE, Net Sales: 29.0, Commission: 0.0, Age: 36.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 36, the number of cards is 1, the credit balance is 7166, the number of transactions is 20, the number of international transactions is 1, the credit limit is 10.'
Answer:
|
good
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: CWT, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Rental Vehicle Excess Insurance, Duration: 40, Destination: AUSTRALIA, Net Sales: 99.0, Commission: 59.4, Age: 45.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 39, the number of cards is 1, the credit balance is 8000, the number of transactions is 5, the number of international transactions is 0, the credit limit is 7.'
Answer:
|
good
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.371, V2: -0.441, V3: -1.545, V4: -2.543, V5: 1.819, V6: 2.719, V7: -0.489, V8: 0.674, V9: 0.987, V10: -0.948, V11: 0.181, V12: 0.477, V13: 0.002, V14: 0.515, V15: 1.800, V16: -0.178, V17: -0.615, V18: 0.075, V19: 1.174, V20: 0.058, V21: -0.235, V22: -0.752, V23: -0.009, V24: 1.028, V25: 0.590, V26: -0.110, V27: -0.008, V28: 0.008, Amount: 12.280.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 5, Destination: INDIA, Net Sales: 16.0, Commission: 0.0, Age: 36.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.949, V2: -0.429, V3: 0.146, V4: 0.362, V5: -0.718, V6: 0.099, V7: -1.023, V8: 0.065, V9: 2.312, V10: -0.246, V11: 1.562, V12: -1.161, V13: 2.643, V14: 1.120, V15: -0.733, V16: 0.904, V17: -0.340, V18: 0.819, V19: -0.086, V20: -0.131, V21: -0.077, V22: 0.175, V23: 0.255, V24: -0.417, V25: -0.574, V26: 0.444, V27: -0.039, V28: -0.055, Amount: 15.950.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: C2B, Agency Type: Airlines, Distribution Channel: Online, Product Name: Bronze Plan, Duration: 54, Destination: SINGAPORE, Net Sales: 60.0, Commission: 15.0, Age: 46.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -0.530, V2: 0.025, V3: 1.165, V4: -1.439, V5: -0.392, V6: -1.022, V7: -0.006, V8: 0.092, V9: -0.983, V10: -0.283, V11: 0.314, V12: -1.036, V13: -0.422, V14: -1.392, V15: 0.607, V16: 1.449, V17: 1.166, V18: -1.267, V19: -0.842, V20: 0.279, V21: 0.161, V22: 0.306, V23: 0.122, V24: 0.274, V25: -0.118, V26: -0.433, V27: 0.266, V28: 0.105, Amount: 49.900.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -0.427, V2: -3.637, V3: -1.231, V4: 0.166, V5: -1.664, V6: -0.493, V7: 0.942, V8: -0.459, V9: -0.679, V10: 0.159, V11: -1.103, V12: -1.174, V13: -0.788, V14: 0.201, V15: 0.042, V16: 0.847, V17: 0.530, V18: -1.173, V19: 0.697, V20: 1.982, V21: 0.697, V22: -0.202, V23: -1.074, V24: -0.042, V25: 0.461, V26: -0.135, V27: -0.201, V28: 0.167, Amount: 987.000.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 21, the number of cards is 1, the credit balance is 7000, the number of transactions is 7, the number of international transactions is 14, the credit limit is 6.'
Answer:
|
good
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.500, ROA(A) before interest and % after tax: 0.563, ROA(B) before interest and depreciation after tax: 0.547, Operating Gross Margin: 0.598, Realized Sales Gross Margin: 0.598, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.797, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.303, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 328000000.000, Research and development expense rate: 0.000, Cash flow rate: 0.460, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.255, Net Value Per Share (B): 0.181, Net Value Per Share (A): 0.181, Net Value Per Share (C): 0.181, Persistent EPS in the Last Four Seasons: 0.231, Cash Flow Per Share: 0.310, Revenue Per Share (Yuan ¥): 0.147, Operating Profit Per Share (Yuan ¥): 0.125, Per Share Net profit before tax (Yuan ¥): 0.187, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.690, Regular Net Profit Growth Rate: 0.690, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 9200000000.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.365, Current Ratio: 0.009, Quick Ratio: 0.007, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.017, Debt ratio %: 0.192, Net worth/Assets: 0.808, Long-term fund suitability ratio (A): 0.005, Borrowing dependency: 0.379, Contingent liabilities/Net worth: 0.005, Operating profit/Paid-in capital: 0.125, Net profit before tax/Paid-in capital: 0.186, Inventory and accounts receivable/Net value: 0.422, Total Asset Turnover: 0.447, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.006, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 0.002, Net Worth Turnover Rate (times): 0.136, Revenue per person: 0.142, Operating profit per person: 0.412, Allocation rate per person: 0.016, Working Capital to Total Assets: 0.828, Quick Assets/Total Assets: 0.666, Current Assets/Total Assets: 0.797, Cash/Total Assets: 0.018, Quick Assets/Current Liability: 0.008, Cash/Current Liability: 0.001, Current Liability to Assets: 0.159, Operating Funds to Liability: 0.336, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.005, Current Liabilities/Liability: 0.792, Working Capital/Equity: 0.741, Current Liabilities/Equity: 0.338, Long-term Liability to Current Assets: 0.005, Retained Earnings to Total Assets: 0.936, Total income/Total expense: 0.002, Total expense/Assets: 0.022, Current Asset Turnover Rate: 7100000000.000, Quick Asset Turnover Rate: 6270000000.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 5080000.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.178, Current Liability to Liability: 0.792, Current Liability to Equity: 0.338, Equity to Long-term Liability: 0.121, Cash Flow to Total Assets: 0.609, Cash Flow to Liability: 0.455, CFO to Assets: 0.537, Cash Flow to Equity: 0.306, Current Liability to Current Assets: 0.031, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.810, Total assets to GNP price: 0.001, No-credit Interval: 0.624, Gross Profit to Sales: 0.598, Net Income to Stockholder's Equity: 0.842, Liability to Equity: 0.287, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.566, Net Income Flag: 1.000, Equity to Liability: 0.018.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 25, Destination: SINGAPORE, Net Sales: 28.0, Commission: 0.0, Age: 36.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.539, ROA(A) before interest and % after tax: 0.595, ROA(B) before interest and depreciation after tax: 0.589, Operating Gross Margin: 0.612, Realized Sales Gross Margin: 0.612, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.798, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.304, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 0.000, Research and development expense rate: 5870000000.000, Cash flow rate: 0.477, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.092, Net Value Per Share (B): 0.231, Net Value Per Share (A): 0.231, Net Value Per Share (C): 0.231, Persistent EPS in the Last Four Seasons: 0.243, Cash Flow Per Share: 0.335, Revenue Per Share (Yuan ¥): 0.032, Operating Profit Per Share (Yuan ¥): 0.119, Per Share Net profit before tax (Yuan ¥): 0.195, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.689, Regular Net Profit Growth Rate: 0.689, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 0.000, Net Value Growth Rate: 0.001, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.389, Current Ratio: 0.021, Quick Ratio: 0.017, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.002, Debt ratio %: 0.055, Net worth/Assets: 0.945, Long-term fund suitability ratio (A): 0.006, Borrowing dependency: 0.370, Contingent liabilities/Net worth: 0.005, Operating profit/Paid-in capital: 0.119, Net profit before tax/Paid-in capital: 0.194, Inventory and accounts receivable/Net value: 0.398, Total Asset Turnover: 0.124, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.007, Inventory Turnover Rate (times): 9420000000.000, Fixed Assets Turnover Frequency: 0.000, Net Worth Turnover Rate (times): 0.027, Revenue per person: 0.013, Operating profit per person: 0.399, Allocation rate per person: 0.006, Working Capital to Total Assets: 0.862, Quick Assets/Total Assets: 0.490, Current Assets/Total Assets: 0.571, Cash/Total Assets: 0.355, Quick Assets/Current Liability: 0.017, Cash/Current Liability: 0.036, Current Liability to Assets: 0.053, Operating Funds to Liability: 0.381, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.010, Current Liabilities/Liability: 0.877, Working Capital/Equity: 0.738, Current Liabilities/Equity: 0.328, Long-term Liability to Current Assets: 0.001, Retained Earnings to Total Assets: 0.942, Total income/Total expense: 0.003, Total expense/Assets: 0.018, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 0.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.200, Current Liability to Liability: 0.877, Current Liability to Equity: 0.328, Equity to Long-term Liability: 0.112, Cash Flow to Total Assets: 0.760, Cash Flow to Liability: 0.511, CFO to Assets: 0.640, Cash Flow to Equity: 0.328, Current Liability to Current Assets: 0.014, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.827, Total assets to GNP price: 0.001, No-credit Interval: 0.624, Gross Profit to Sales: 0.612, Net Income to Stockholder's Equity: 0.842, Liability to Equity: 0.276, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.071.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 35, the number of cards is 1, the credit balance is 4000, the number of transactions is 100, the number of international transactions is 0, the credit limit is 3.'
Answer:
|
good
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.338, V2: -0.473, V3: 0.393, V4: -0.782, V5: -0.860, V6: -0.518, V7: -0.473, V8: -0.091, V9: -1.212, V10: 0.794, V11: 1.113, V12: 0.880, V13: 0.997, V14: 0.145, V15: 0.213, V16: -0.861, V17: -0.479, V18: 1.122, V19: -0.226, V20: -0.377, V21: -0.689, V22: -1.603, V23: 0.235, V24: -0.001, V25: -0.101, V26: 0.639, V27: -0.056, V28: 0.007, Amount: 22.750.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 109, Destination: THAILAND, Net Sales: -19.0, Commission: 0.0, Age: 32.'
Answer:
|
no
|
|
Evaluate the creditworthiness of a customer with the following financial profile. Respond with only either 'good' or 'bad'. For instance, 'The client has a stable income, no previous debts, and owns a property.' should be classified as 'good'.
Text: 'The state of Status of existing checking account is bigger than 0 DM but smaller than 200 DM. The state of Duration in month is 36. The state of Credit history is existing credits paid back duly till now. The state of Purpose is radio or television. The state of Credit amount is 2323. The state of Savings account or bonds is smaller than 100 DM. The state of Present employment since is bigger than 4 smaller than 7 years. The state of Installment rate in percentage of disposable income is 4. The state of Personal status and sex is male and single. The state of Other debtors or guarantors is none. The state of Present residence since is 4. The state of Property is car or other. The state of Age in years is 24. The state of Other installment plans is none. The state of Housing is rent. The state of Number of existing credits at this bank is 1. The state of Job is skilled employee or official. The state of Number of people being liable to provide maintenance for is 1. The state of Telephone is none. The state of foreign worker is yes. '
Answer:
|
good
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 5, the number of cards is 1, the credit balance is 3000, the number of transactions is 90, the number of international transactions is 0, the credit limit is 2.'
Answer:
|
good
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 8, Destination: INDONESIA, Net Sales: 28.0, Commission: 0.0, Age: 48.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: CWT, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Rental Vehicle Excess Insurance, Duration: 12, Destination: NETHERLANDS, Net Sales: 19.8, Commission: 11.88, Age: 48.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.300, V2: 0.234, V3: 0.064, V4: 0.222, V5: 0.227, V6: 0.021, V7: -0.004, V8: -0.035, V9: -0.272, V10: 0.078, V11: 0.594, V12: 1.155, V13: 1.341, V14: 0.186, V15: 0.317, V16: 0.797, V17: -1.150, V18: 0.222, V19: 0.604, V20: 0.014, V21: -0.272, V22: -0.773, V23: -0.031, V24: -0.818, V25: 0.374, V26: 0.146, V27: -0.029, V28: -0.001, Amount: 1.980.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.470, ROA(A) before interest and % after tax: 0.534, ROA(B) before interest and depreciation after tax: 0.523, Operating Gross Margin: 0.598, Realized Sales Gross Margin: 0.598, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.797, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.303, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 6230000000.000, Research and development expense rate: 289000000.000, Cash flow rate: 0.466, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.341, Net Value Per Share (B): 0.175, Net Value Per Share (A): 0.175, Net Value Per Share (C): 0.175, Persistent EPS in the Last Four Seasons: 0.214, Cash Flow Per Share: 0.320, Revenue Per Share (Yuan ¥): 0.020, Operating Profit Per Share (Yuan ¥): 0.097, Per Share Net profit before tax (Yuan ¥): 0.170, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.689, Regular Net Profit Growth Rate: 0.689, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 5900000000.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.379, Current Ratio: 0.008, Quick Ratio: 0.004, Interest Expense Ratio: 0.634, Total debt/Total net worth: 0.004, Debt ratio %: 0.094, Net worth/Assets: 0.906, Long-term fund suitability ratio (A): 0.005, Borrowing dependency: 0.373, Contingent liabilities/Net worth: 0.006, Operating profit/Paid-in capital: 0.097, Net profit before tax/Paid-in capital: 0.169, Inventory and accounts receivable/Net value: 0.397, Total Asset Turnover: 0.109, Accounts Receivable Turnover: 0.002, Average Collection Days: 0.003, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 0.000, Net Worth Turnover Rate (times): 0.027, Revenue per person: 0.023, Operating profit per person: 0.394, Allocation rate per person: 0.020, Working Capital to Total Assets: 0.765, Quick Assets/Total Assets: 0.114, Current Assets/Total Assets: 0.204, Cash/Total Assets: 0.027, Quick Assets/Current Liability: 0.004, Cash/Current Liability: 0.003, Current Liability to Assets: 0.048, Operating Funds to Liability: 0.348, Inventory/Working Capital: 0.278, Inventory/Current Liability: 0.010, Current Liabilities/Liability: 0.475, Working Capital/Equity: 0.733, Current Liabilities/Equity: 0.328, Long-term Liability to Current Assets: 0.025, Retained Earnings to Total Assets: 0.932, Total income/Total expense: 0.002, Total expense/Assets: 0.012, Current Asset Turnover Rate: 6260000000.000, Quick Asset Turnover Rate: 3510000000.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 2560000000.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.388, Current Liability to Liability: 0.475, Current Liability to Equity: 0.328, Equity to Long-term Liability: 0.118, Cash Flow to Total Assets: 0.635, Cash Flow to Liability: 0.457, CFO to Assets: 0.587, Cash Flow to Equity: 0.314, Current Liability to Current Assets: 0.036, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.796, Total assets to GNP price: 0.004, No-credit Interval: 0.623, Gross Profit to Sales: 0.598, Net Income to Stockholder's Equity: 0.840, Liability to Equity: 0.278, Degree of Financial Leverage (DFL): 0.029, Interest Coverage Ratio (Interest expense to EBIT): 0.569, Net Income Flag: 1.000, Equity to Liability: 0.041.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 4, the number of cards is 2, the credit balance is 14459, the number of transactions is 8, the number of international transactions is 3, the credit limit is 32.'
Answer:
|
bad
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -0.496, V2: 1.460, V3: 1.311, V4: 4.412, V5: -0.110, V6: 1.219, V7: -0.242, V8: 0.677, V9: -1.553, V10: 1.309, V11: -2.009, V12: -0.508, V13: 0.957, V14: -0.034, V15: 0.603, V16: 0.356, V17: -0.053, V18: 0.807, V19: 1.175, V20: 0.364, V21: 0.320, V22: 1.036, V23: -0.098, V24: -0.624, V25: -0.519, V26: 0.542, V27: 0.365, V28: 0.191, Amount: 47.780.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: TST, Agency Type: Travel Agency, Distribution Channel: Offline, Product Name: Travel Cruise Protect, Duration: 33, Destination: THAILAND, Net Sales: 30.0, Commission: 10.5, Age: 40.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 17, the number of cards is 1, the credit balance is 5000, the number of transactions is 9, the number of international transactions is 0, the credit limit is 4.'
Answer:
|
good
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: RAB, Agency Type: Airlines, Distribution Channel: Online, Product Name: Value Plan, Duration: 4, Destination: BRUNEI DARUSSALAM, Net Sales: 15.0, Commission: 6.0, Age: 32.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 43, the number of cards is 1, the credit balance is 414, the number of transactions is 8, the number of international transactions is 0, the credit limit is 1.'
Answer:
|
good
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.580, ROA(A) before interest and % after tax: 0.630, ROA(B) before interest and depreciation after tax: 0.612, Operating Gross Margin: 0.622, Realized Sales Gross Margin: 0.622, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.798, After-tax net Interest Rate: 0.810, Non-industry income and expenditure/revenue: 0.304, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 0.000, Research and development expense rate: 6840000000.000, Cash flow rate: 0.456, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.250, Net Value Per Share (B): 0.195, Net Value Per Share (A): 0.195, Net Value Per Share (C): 0.195, Persistent EPS in the Last Four Seasons: 0.248, Cash Flow Per Share: 0.314, Revenue Per Share (Yuan ¥): 0.024, Operating Profit Per Share (Yuan ¥): 0.131, Per Share Net profit before tax (Yuan ¥): 0.207, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.689, Regular Net Profit Growth Rate: 0.689, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 8970000000.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.368, Current Ratio: 0.028, Quick Ratio: 0.026, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.004, Debt ratio %: 0.084, Net worth/Assets: 0.916, Long-term fund suitability ratio (A): 0.008, Borrowing dependency: 0.372, Contingent liabilities/Net worth: 0.005, Operating profit/Paid-in capital: 0.131, Net profit before tax/Paid-in capital: 0.206, Inventory and accounts receivable/Net value: 0.398, Total Asset Turnover: 0.118, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.009, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 0.001, Net Worth Turnover Rate (times): 0.026, Revenue per person: 0.011, Operating profit per person: 0.403, Allocation rate per person: 0.002, Working Capital to Total Assets: 0.886, Quick Assets/Total Assets: 0.590, Current Assets/Total Assets: 0.626, Cash/Total Assets: 0.066, Quick Assets/Current Liability: 0.026, Cash/Current Liability: 0.008, Current Liability to Assets: 0.042, Operating Funds to Liability: 0.333, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.007, Current Liabilities/Liability: 0.457, Working Capital/Equity: 0.740, Current Liabilities/Equity: 0.328, Long-term Liability to Current Assets: 0.007, Retained Earnings to Total Assets: 0.956, Total income/Total expense: 0.003, Total expense/Assets: 0.020, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 6910000000.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.084, Current Liability to Liability: 0.457, Current Liability to Equity: 0.328, Equity to Long-term Liability: 0.117, Cash Flow to Total Assets: 0.634, Cash Flow to Liability: 0.457, CFO to Assets: 0.544, Cash Flow to Equity: 0.313, Current Liability to Current Assets: 0.010, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.846, Total assets to GNP price: 0.002, No-credit Interval: 0.625, Gross Profit to Sales: 0.622, Net Income to Stockholder's Equity: 0.843, Liability to Equity: 0.277, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.046.'
Answer:
|
no
|
|
Assess the creditworthiness of a customer using the following table attributes for financial status. Respond with either 'good' or 'bad'. And all the table attribute names including 8 categorical attributes and 6 numerical attributes and values have been changed to meaningless symbols to protect confidentiality of the data. For instance, 'The client has attributes: A1: 0, A2: 21.67, A3: 11.5, A4: 1, A5: 5, A6: 3, A7: 0, A8: 1, A9: 1, A10: 11, A11: 1, A12: 2, A13: 0, A14: 1.', should be classified as 'good'.
Text: The client has attributes: A1: 1.0, A2: 48.58, A3: 6.5, A4: 2.0, A5: 11.0, A6: 8.0, A7: 6.0, A8: 1.0, A9: 0.0, A10: 0.0, A11: 1.0, A12: 2.0, A13: 350.0, A14: 1.0.
Answer:
|
good
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 43, Destination: MALAYSIA, Net Sales: 0.0, Commission: 0.0, Age: 36.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.353, ROA(A) before interest and % after tax: 0.402, ROA(B) before interest and depreciation after tax: 0.391, Operating Gross Margin: 0.600, Realized Sales Gross Margin: 0.600, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.794, After-tax net Interest Rate: 0.806, Non-industry income and expenditure/revenue: 0.299, Continuous interest rate (after tax): 0.778, Operating Expense Rate: 0.000, Research and development expense rate: 3740000000.000, Cash flow rate: 0.464, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.000, Net Value Per Share (B): 0.161, Net Value Per Share (A): 0.161, Net Value Per Share (C): 0.161, Persistent EPS in the Last Four Seasons: 0.186, Cash Flow Per Share: 0.319, Revenue Per Share (Yuan ¥): 0.002, Operating Profit Per Share (Yuan ¥): 0.093, Per Share Net profit before tax (Yuan ¥): 0.145, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.611, Regular Net Profit Growth Rate: 0.611, Continuous Net Profit Growth Rate: 0.215, Total Asset Growth Rate: 4850000000.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.262, Cash Reinvestment %: 0.378, Current Ratio: 0.005, Quick Ratio: 0.002, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.002, Debt ratio %: 0.063, Net worth/Assets: 0.937, Long-term fund suitability ratio (A): 0.005, Borrowing dependency: 0.371, Contingent liabilities/Net worth: 0.007, Operating profit/Paid-in capital: 0.093, Net profit before tax/Paid-in capital: 0.148, Inventory and accounts receivable/Net value: 0.395, Total Asset Turnover: 0.016, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.010, Inventory Turnover Rate (times): 8460000000.000, Fixed Assets Turnover Frequency: 2800000000.000, Net Worth Turnover Rate (times): 0.011, Revenue per person: 0.007, Operating profit per person: 0.386, Allocation rate per person: 0.031, Working Capital to Total Assets: 0.743, Quick Assets/Total Assets: 0.077, Current Assets/Total Assets: 0.088, Cash/Total Assets: 0.006, Quick Assets/Current Liability: 0.004, Cash/Current Liability: 0.001, Current Liability to Assets: 0.037, Operating Funds to Liability: 0.345, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.002, Current Liabilities/Liability: 0.536, Working Capital/Equity: 0.732, Current Liabilities/Equity: 0.327, Long-term Liability to Current Assets: 0.021, Retained Earnings to Total Assets: 0.903, Total income/Total expense: 0.002, Total expense/Assets: 0.056, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 3280000000.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.332, Current Liability to Liability: 0.536, Current Liability to Equity: 0.327, Equity to Long-term Liability: 0.113, Cash Flow to Total Assets: 0.639, Cash Flow to Liability: 0.458, CFO to Assets: 0.575, Cash Flow to Equity: 0.314, Current Liability to Current Assets: 0.063, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.711, Total assets to GNP price: 0.003, No-credit Interval: 0.623, Gross Profit to Sales: 0.600, Net Income to Stockholder's Equity: 0.835, Liability to Equity: 0.277, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.061.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 16, the number of cards is 1, the credit balance is 3000, the number of transactions is 37, the number of international transactions is 0, the credit limit is 2.'
Answer:
|
good
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 43, Destination: VIET NAM, Net Sales: 10.0, Commission: 0.0, Age: 35.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 0.745, V2: -1.467, V3: 1.266, V4: 0.856, V5: -1.536, V6: 1.068, V7: -1.082, V8: 0.375, V9: 2.183, V10: -0.675, V11: -2.334, V12: 0.239, V13: -0.315, V14: -1.210, V15: -1.195, V16: -0.041, V17: 0.051, V18: 0.245, V19: 0.685, V20: 0.365, V21: 0.117, V22: 0.332, V23: -0.434, V24: -0.456, V25: 0.412, V26: 0.881, V27: 0.004, V28: 0.056, Amount: 240.040.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 0.091, V2: 0.836, V3: -0.003, V4: -0.745, V5: 0.719, V6: -0.724, V7: 1.005, V8: -0.159, V9: 0.018, V10: -0.410, V11: -1.563, V12: -0.149, V13: 0.023, V14: 0.093, V15: -0.341, V16: 0.114, V17: -0.654, V18: -0.508, V19: 0.176, V20: -0.017, V21: -0.306, V22: -0.707, V23: -0.007, V24: -0.665, V25: -0.400, V26: 0.181, V27: 0.245, V28: 0.090, Amount: 6.540.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -0.547, V2: 0.524, V3: 2.057, V4: -0.711, V5: 0.004, V6: -0.156, V7: 0.350, V8: -0.068, V9: 1.601, V10: -0.733, V11: 1.582, V12: -2.798, V13: -0.192, V14: 1.536, V15: -1.029, V16: 0.484, V17: -0.022, V18: 0.559, V19: -0.158, V20: -0.202, V21: -0.306, V22: -0.589, V23: -0.062, V24: -0.091, V25: -0.645, V26: 0.189, V27: -0.250, V28: -0.121, Amount: 12.990.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 31, the number of cards is 1, the credit balance is 0, the number of transactions is 11, the number of international transactions is 0, the credit limit is 7.'
Answer:
|
good
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.549, ROA(A) before interest and % after tax: 0.595, ROA(B) before interest and depreciation after tax: 0.591, Operating Gross Margin: 0.602, Realized Sales Gross Margin: 0.602, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.798, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.304, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 6000000000.000, Research and development expense rate: 5090000000.000, Cash flow rate: 0.467, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.182, Net Value Per Share (B): 0.205, Net Value Per Share (A): 0.205, Net Value Per Share (C): 0.205, Persistent EPS in the Last Four Seasons: 0.243, Cash Flow Per Share: 0.323, Revenue Per Share (Yuan ¥): 0.048, Operating Profit Per Share (Yuan ¥): 0.114, Per Share Net profit before tax (Yuan ¥): 0.197, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.689, Regular Net Profit Growth Rate: 0.689, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 0.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.378, Current Ratio: 0.026, Quick Ratio: 0.022, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.007, Debt ratio %: 0.124, Net worth/Assets: 0.876, Long-term fund suitability ratio (A): 0.006, Borrowing dependency: 0.376, Contingent liabilities/Net worth: 0.005, Operating profit/Paid-in capital: 0.114, Net profit before tax/Paid-in capital: 0.196, Inventory and accounts receivable/Net value: 0.400, Total Asset Turnover: 0.175, Accounts Receivable Turnover: 0.002, Average Collection Days: 0.004, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 0.000, Net Worth Turnover Rate (times): 0.038, Revenue per person: 0.070, Operating profit per person: 0.411, Allocation rate per person: 0.032, Working Capital to Total Assets: 0.880, Quick Assets/Total Assets: 0.537, Current Assets/Total Assets: 0.615, Cash/Total Assets: 0.392, Quick Assets/Current Liability: 0.022, Cash/Current Liability: 0.046, Current Liability to Assets: 0.045, Operating Funds to Liability: 0.346, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.011, Current Liabilities/Liability: 0.339, Working Capital/Equity: 0.741, Current Liabilities/Equity: 0.328, Long-term Liability to Current Assets: 0.014, Retained Earnings to Total Assets: 0.941, Total income/Total expense: 0.003, Total expense/Assets: 0.012, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 0.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.270, Current Liability to Liability: 0.339, Current Liability to Equity: 0.328, Equity to Long-term Liability: 0.125, Cash Flow to Total Assets: 0.731, Cash Flow to Liability: 0.477, CFO to Assets: 0.587, Cash Flow to Equity: 0.329, Current Liability to Current Assets: 0.011, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.825, Total assets to GNP price: 0.014, No-credit Interval: 0.626, Gross Profit to Sales: 0.602, Net Income to Stockholder's Equity: 0.842, Liability to Equity: 0.280, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.566, Net Income Flag: 1.000, Equity to Liability: 0.030.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 18, the number of cards is 1, the credit balance is 6000, the number of transactions is 66, the number of international transactions is 0, the credit limit is 5.'
Answer:
|
bad
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: CWT, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Rental Vehicle Excess Insurance, Duration: 14, Destination: MALAYSIA, Net Sales: 39.6, Commission: 23.76, Age: 42.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 41, the number of cards is 1, the credit balance is 0, the number of transactions is 13, the number of international transactions is 14, the credit limit is 16.'
Answer:
|
good
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 10, the number of cards is 1, the credit balance is 4000, the number of transactions is 7, the number of international transactions is 0, the credit limit is 3.'
Answer:
|
good
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.907, V2: -0.275, V3: -0.794, V4: 1.448, V5: -0.019, V6: 0.319, V7: -0.284, V8: 0.222, V9: 1.050, V10: 0.188, V11: 0.061, V12: 0.688, V13: -1.733, V14: 0.219, V15: -2.218, V16: -0.623, V17: 0.036, V18: -0.300, V19: 0.525, V20: -0.359, V21: -0.299, V22: -0.600, V23: 0.282, V24: 0.633, V25: -0.018, V26: -0.865, V27: 0.024, V28: -0.049, Amount: 9.000.'
Answer:
|
no
|
|
Evaluate the creditworthiness of a customer with the following financial profile. Respond with only either 'good' or 'bad'. For instance, 'The client has a stable income, no previous debts, and owns a property.' should be classified as 'good'.
Text: 'The state of Status of existing checking account is smaller than 0 DM. The state of Duration in month is 12. The state of Credit history is existing credits paid back duly till now. The state of Purpose is furniture or equipment. The state of Credit amount is 1620. The state of Savings account or bonds is smaller than 100 DM. The state of Present employment since is bigger than 1 smaller than 4 years. The state of Installment rate in percentage of disposable income is 2. The state of Personal status and sex is female: divorced or separated or married. The state of Other debtors or guarantors is co-applicant. The state of Present residence since is 3. The state of Property is building society savings agreement or life insurance. The state of Age in years is 30. The state of Other installment plans is none. The state of Housing is own. The state of Number of existing credits at this bank is 1. The state of Job is skilled employee or official. The state of Number of people being liable to provide maintenance for is 1. The state of Telephone is none. The state of foreign worker is yes. '
Answer:
|
good
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.953, V2: 0.171, V3: -1.540, V4: 1.302, V5: 0.468, V6: -0.858, V7: 0.591, V8: -0.306, V9: -0.131, V10: 0.408, V11: 0.876, V12: 1.272, V13: 0.074, V14: 0.652, V15: -1.271, V16: -0.586, V17: -0.371, V18: -0.243, V19: 0.027, V20: -0.238, V21: 0.121, V22: 0.531, V23: -0.021, V24: 0.036, V25: 0.426, V26: -0.515, V27: -0.018, V28: -0.068, Amount: 22.390.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 44, the number of cards is 1, the credit balance is 7000, the number of transactions is 14, the number of international transactions is 18, the credit limit is 6.'
Answer:
|
good
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -0.448, V2: -0.262, V3: 0.531, V4: -1.730, V5: 0.221, V6: -1.384, V7: -0.309, V8: -0.133, V9: -0.563, V10: -0.645, V11: -0.456, V12: -0.796, V13: 0.505, V14: -2.412, V15: -0.385, V16: 1.898, V17: 1.042, V18: -0.484, V19: -0.530, V20: -0.087, V21: 0.248, V22: 0.749, V23: -0.341, V24: -0.036, V25: 0.119, V26: -0.186, V27: -0.044, V28: -0.054, Amount: 19.950.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 118, Destination: THAILAND, Net Sales: 39.0, Commission: 0.0, Age: 35.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 27, Destination: HONG KONG, Net Sales: 30.0, Commission: 0.0, Age: 20.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.556, ROA(A) before interest and % after tax: 0.613, ROA(B) before interest and depreciation after tax: 0.600, Operating Gross Margin: 0.597, Realized Sales Gross Margin: 0.597, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.797, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.304, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 258000000.000, Research and development expense rate: 1040000000.000, Cash flow rate: 0.462, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.155, Net Value Per Share (B): 0.340, Net Value Per Share (A): 0.340, Net Value Per Share (C): 0.340, Persistent EPS in the Last Four Seasons: 0.323, Cash Flow Per Share: 0.325, Revenue Per Share (Yuan ¥): 0.266, Operating Profit Per Share (Yuan ¥): 0.141, Per Share Net profit before tax (Yuan ¥): 0.273, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.690, Regular Net Profit Growth Rate: 0.690, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 9220000000.000, Net Value Growth Rate: 0.001, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.375, Current Ratio: 0.009, Quick Ratio: 0.006, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.007, Debt ratio %: 0.126, Net worth/Assets: 0.874, Long-term fund suitability ratio (A): 0.008, Borrowing dependency: 0.371, Contingent liabilities/Net worth: 0.005, Operating profit/Paid-in capital: 0.141, Net profit before tax/Paid-in capital: 0.272, Inventory and accounts receivable/Net value: 0.405, Total Asset Turnover: 0.337, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.005, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 0.003, Net Worth Turnover Rate (times): 0.075, Revenue per person: 0.567, Operating profit per person: 0.462, Allocation rate per person: 0.029, Working Capital to Total Assets: 0.806, Quick Assets/Total Assets: 0.390, Current Assets/Total Assets: 0.550, Cash/Total Assets: 0.040, Quick Assets/Current Liability: 0.007, Cash/Current Liability: 0.002, Current Liability to Assets: 0.109, Operating Funds to Liability: 0.343, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.008, Current Liabilities/Liability: 0.814, Working Capital/Equity: 0.736, Current Liabilities/Equity: 0.331, Long-term Liability to Current Assets: 0.004, Retained Earnings to Total Assets: 0.959, Total income/Total expense: 0.003, Total expense/Assets: 0.010, Current Asset Turnover Rate: 6410000000.000, Quick Asset Turnover Rate: 4710000000.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 1490000000.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.060, Current Liability to Liability: 0.814, Current Liability to Equity: 0.331, Equity to Long-term Liability: 0.114, Cash Flow to Total Assets: 0.648, Cash Flow to Liability: 0.460, CFO to Assets: 0.575, Cash Flow to Equity: 0.315, Current Liability to Current Assets: 0.031, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.837, Total assets to GNP price: 0.449, No-credit Interval: 0.624, Gross Profit to Sales: 0.597, Net Income to Stockholder's Equity: 0.843, Liability to Equity: 0.280, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.029.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -0.618, V2: 1.543, V3: 1.680, V4: 2.526, V5: 0.114, V6: 0.317, V7: 0.432, V8: 0.243, V9: -1.381, V10: 1.343, V11: 0.824, V12: -0.247, V13: -0.648, V14: 0.522, V15: 0.706, V16: 0.328, V17: -0.358, V18: 0.575, V19: 1.240, V20: 0.367, V21: -0.206, V22: -0.508, V23: -0.015, V24: -0.065, V25: -0.369, V26: -0.029, V27: 0.482, V28: 0.247, Amount: 10.020.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 29, the number of cards is 1, the credit balance is 0, the number of transactions is 100, the number of international transactions is 0, the credit limit is 40.'
Answer:
|
bad
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 39, the number of cards is 1, the credit balance is 4000, the number of transactions is 41, the number of international transactions is 0, the credit limit is 3.'
Answer:
|
good
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.875, V2: -0.193, V3: -1.618, V4: 0.187, V5: 0.364, V6: -0.723, V7: 0.444, V8: -0.253, V9: 0.071, V10: 0.103, V11: 1.215, V12: 1.319, V13: 0.471, V14: 0.719, V15: -0.055, V16: -0.046, V17: -0.737, V18: -0.063, V19: 0.269, V20: -0.000, V21: -0.030, V22: -0.243, V23: 0.172, V24: 0.756, V25: -0.033, V26: -0.559, V27: -0.051, V28: -0.041, Amount: 89.000.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.586, ROA(A) before interest and % after tax: 0.657, ROA(B) before interest and depreciation after tax: 0.641, Operating Gross Margin: 0.614, Realized Sales Gross Margin: 0.614, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.798, After-tax net Interest Rate: 0.810, Non-industry income and expenditure/revenue: 0.304, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 9230000000.000, Research and development expense rate: 5380000000.000, Cash flow rate: 0.475, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.071, Net Value Per Share (B): 0.205, Net Value Per Share (A): 0.205, Net Value Per Share (C): 0.205, Persistent EPS in the Last Four Seasons: 0.269, Cash Flow Per Share: 0.343, Revenue Per Share (Yuan ¥): 0.042, Operating Profit Per Share (Yuan ¥): 0.143, Per Share Net profit before tax (Yuan ¥): 0.217, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.689, Regular Net Profit Growth Rate: 0.689, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 8500000000.000, Net Value Growth Rate: 0.001, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.400, Current Ratio: 0.011, Quick Ratio: 0.009, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.004, Debt ratio %: 0.094, Net worth/Assets: 0.906, Long-term fund suitability ratio (A): 0.005, Borrowing dependency: 0.371, Contingent liabilities/Net worth: 0.005, Operating profit/Paid-in capital: 0.143, Net profit before tax/Paid-in capital: 0.216, Inventory and accounts receivable/Net value: 0.400, Total Asset Turnover: 0.166, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.005, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 0.000, Net Worth Turnover Rate (times): 0.035, Revenue per person: 0.023, Operating profit per person: 0.410, Allocation rate per person: 0.013, Working Capital to Total Assets: 0.824, Quick Assets/Total Assets: 0.447, Current Assets/Total Assets: 0.575, Cash/Total Assets: 0.274, Quick Assets/Current Liability: 0.009, Cash/Current Liability: 0.015, Current Liability to Assets: 0.095, Operating Funds to Liability: 0.380, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.008, Current Liabilities/Liability: 0.956, Working Capital/Equity: 0.736, Current Liabilities/Equity: 0.330, Long-term Liability to Current Assets: 0.000, Retained Earnings to Total Assets: 0.956, Total income/Total expense: 0.003, Total expense/Assets: 0.016, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 0.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.349, Current Liability to Liability: 0.956, Current Liability to Equity: 0.330, Equity to Long-term Liability: 0.111, Cash Flow to Total Assets: 0.664, Cash Flow to Liability: 0.465, CFO to Assets: 0.686, Cash Flow to Equity: 0.317, Current Liability to Current Assets: 0.026, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.862, Total assets to GNP price: 0.002, No-credit Interval: 0.624, Gross Profit to Sales: 0.614, Net Income to Stockholder's Equity: 0.845, Liability to Equity: 0.278, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.041.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 23, the number of cards is 1, the credit balance is 0, the number of transactions is 12, the number of international transactions is 0, the credit limit is 10.'
Answer:
|
good
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -2.376, V2: -0.316, V3: 0.809, V4: -0.820, V5: 2.310, V6: -0.970, V7: 1.062, V8: -0.734, V9: 0.699, V10: 0.189, V11: 0.725, V12: 0.056, V13: -0.374, V14: -2.077, V15: -1.455, V16: 0.232, V17: -0.129, V18: 0.548, V19: -0.063, V20: -0.131, V21: -0.357, V22: 0.172, V23: -0.686, V24: -0.498, V25: 1.074, V26: 0.608, V27: -0.590, V28: -0.051, Amount: 13.600.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -1.071, V2: 1.588, V3: -0.652, V4: -0.808, V5: 0.232, V6: -0.501, V7: 0.296, V8: 0.834, V9: -0.601, V10: -0.448, V11: 0.479, V12: 1.481, V13: 0.681, V14: 0.738, V15: -1.532, V16: 0.092, V17: -0.285, V18: -0.243, V19: 0.280, V20: -0.045, V21: -0.049, V22: -0.146, V23: 0.174, V24: 0.791, V25: -0.724, V26: 0.104, V27: 0.248, V28: 0.201, Amount: 3.470.'
Answer:
|
no
|
|
Evaluate the creditworthiness of a customer with the following financial profile. Respond with only either 'good' or 'bad'. For instance, 'The client has a stable income, no previous debts, and owns a property.' should be classified as 'good'.
Text: 'The state of Status of existing checking account is bigger than 0 DM but smaller than 200 DM. The state of Duration in month is 24. The state of Credit history is critical account or other credits existing (not at this bank). The state of Purpose is radio or television. The state of Credit amount is 1216. The state of Savings account or bonds is bigger than 100 smaller than 500 DM. The state of Present employment since is smaller than 1 year. The state of Installment rate in percentage of disposable income is 4. The state of Personal status and sex is male and single. The state of Other debtors or guarantors is none. The state of Present residence since is 4. The state of Property is unknown or no property. The state of Age in years is 38. The state of Other installment plans is bank. The state of Housing is own. The state of Number of existing credits at this bank is 2. The state of Job is skilled employee or official. The state of Number of people being liable to provide maintenance for is 2. The state of Telephone is none. The state of foreign worker is yes. '
Answer:
|
bad
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -0.849, V2: 1.071, V3: 0.577, V4: 0.089, V5: 1.785, V6: -0.504, V7: 0.847, V8: -0.015, V9: -0.787, V10: -1.434, V11: -0.437, V12: 0.117, V13: 1.254, V14: -1.740, V15: 0.624, V16: 0.630, V17: 0.553, V18: 0.364, V19: -1.211, V20: 0.069, V21: 0.039, V22: 0.085, V23: -0.358, V24: -0.877, V25: 0.405, V26: -0.406, V27: 0.114, V28: 0.143, Amount: 1.000.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.554, ROA(A) before interest and % after tax: 0.617, ROA(B) before interest and depreciation after tax: 0.609, Operating Gross Margin: 0.605, Realized Sales Gross Margin: 0.605, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.798, After-tax net Interest Rate: 0.810, Non-industry income and expenditure/revenue: 0.304, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 0.000, Research and development expense rate: 5160000000.000, Cash flow rate: 0.458, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.073, Net Value Per Share (B): 0.197, Net Value Per Share (A): 0.197, Net Value Per Share (C): 0.197, Persistent EPS in the Last Four Seasons: 0.254, Cash Flow Per Share: 0.308, Revenue Per Share (Yuan ¥): 0.036, Operating Profit Per Share (Yuan ¥): 0.102, Per Share Net profit before tax (Yuan ¥): 0.205, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.690, Regular Net Profit Growth Rate: 0.690, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 9950000000.000, Net Value Growth Rate: 0.001, Total Asset Return Growth Rate Ratio: 0.265, Cash Reinvestment %: 0.364, Current Ratio: 0.008, Quick Ratio: 0.007, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.008, Debt ratio %: 0.135, Net worth/Assets: 0.865, Long-term fund suitability ratio (A): 0.006, Borrowing dependency: 0.375, Contingent liabilities/Net worth: 0.006, Operating profit/Paid-in capital: 0.102, Net profit before tax/Paid-in capital: 0.202, Inventory and accounts receivable/Net value: 0.404, Total Asset Turnover: 0.132, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.012, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 0.001, Net Worth Turnover Rate (times): 0.038, Revenue per person: 0.018, Operating profit per person: 0.394, Allocation rate per person: 0.005, Working Capital to Total Assets: 0.785, Quick Assets/Total Assets: 0.472, Current Assets/Total Assets: 0.517, Cash/Total Assets: 0.046, Quick Assets/Current Liability: 0.007, Cash/Current Liability: 0.002, Current Liability to Assets: 0.122, Operating Funds to Liability: 0.332, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.003, Current Liabilities/Liability: 0.855, Working Capital/Equity: 0.735, Current Liabilities/Equity: 0.332, Long-term Liability to Current Assets: 0.002, Retained Earnings to Total Assets: 0.945, Total income/Total expense: 0.003, Total expense/Assets: 0.022, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 4420000000.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.113, Current Liability to Liability: 0.855, Current Liability to Equity: 0.332, Equity to Long-term Liability: 0.113, Cash Flow to Total Assets: 0.650, Cash Flow to Liability: 0.461, CFO to Assets: 0.527, Cash Flow to Equity: 0.316, Current Liability to Current Assets: 0.037, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.838, Total assets to GNP price: 0.002, No-credit Interval: 0.624, Gross Profit to Sales: 0.605, Net Income to Stockholder's Equity: 0.844, Liability to Equity: 0.280, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.027.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 41, the number of cards is 2, the credit balance is 0, the number of transactions is 16, the number of international transactions is 0, the credit limit is 15.'
Answer:
|
good
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.444, ROA(A) before interest and % after tax: 0.470, ROA(B) before interest and depreciation after tax: 0.494, Operating Gross Margin: 0.591, Realized Sales Gross Margin: 0.591, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.797, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.303, Continuous interest rate (after tax): 0.781, Operating Expense Rate: 4910000000.000, Research and development expense rate: 0.000, Cash flow rate: 0.462, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.000, Net Value Per Share (B): 0.144, Net Value Per Share (A): 0.144, Net Value Per Share (C): 0.144, Persistent EPS in the Last Four Seasons: 0.210, Cash Flow Per Share: 0.318, Revenue Per Share (Yuan ¥): 0.003, Operating Profit Per Share (Yuan ¥): 0.094, Per Share Net profit before tax (Yuan ¥): 0.165, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.689, Regular Net Profit Growth Rate: 0.689, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 8160000000.000, Net Value Growth Rate: 0.001, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.377, Current Ratio: 0.010, Quick Ratio: 0.008, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.002, Debt ratio %: 0.062, Net worth/Assets: 0.938, Long-term fund suitability ratio (A): 0.006, Borrowing dependency: 0.371, Contingent liabilities/Net worth: 0.005, Operating profit/Paid-in capital: 0.094, Net profit before tax/Paid-in capital: 0.165, Inventory and accounts receivable/Net value: 0.397, Total Asset Turnover: 0.066, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.009, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 0.000, Net Worth Turnover Rate (times): 0.018, Revenue per person: 0.007, Operating profit per person: 0.390, Allocation rate per person: 0.005, Working Capital to Total Assets: 0.778, Quick Assets/Total Assets: 0.187, Current Assets/Total Assets: 0.242, Cash/Total Assets: 0.047, Quick Assets/Current Liability: 0.008, Cash/Current Liability: 0.006, Current Liability to Assets: 0.045, Operating Funds to Liability: 0.342, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.007, Current Liabilities/Liability: 0.660, Working Capital/Equity: 0.733, Current Liabilities/Equity: 0.328, Long-term Liability to Current Assets: 1680000000.000, Retained Earnings to Total Assets: 0.828, Total income/Total expense: 0.002, Total expense/Assets: 0.019, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 8540000000.000, Cash Flow to Sales: 0.671, Fixed Assets to Assets: 0.177, Current Liability to Liability: 0.660, Current Liability to Equity: 0.328, Equity to Long-term Liability: 0.111, Cash Flow to Total Assets: 0.562, Cash Flow to Liability: 0.427, CFO to Assets: 0.567, Cash Flow to Equity: 0.305, Current Liability to Current Assets: 0.029, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.763, Total assets to GNP price: 0.001, No-credit Interval: 0.624, Gross Profit to Sales: 0.591, Net Income to Stockholder's Equity: 0.838, Liability to Equity: 0.277, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.062.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 27, Destination: CANADA, Net Sales: 72.0, Commission: 0.0, Age: 26.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 29, the number of cards is 1, the credit balance is 0, the number of transactions is 25, the number of international transactions is 0, the credit limit is 5.'
Answer:
|
good
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 1, Destination: SINGAPORE, Net Sales: 10.0, Commission: 0.0, Age: 40.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: LWC, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Single Trip Travel Protect Silver, Duration: 10, Destination: AUSTRALIA, Net Sales: 26.5, Commission: 17.23, Age: 80.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -1.037, V2: 1.306, V3: 2.461, V4: 2.722, V5: -0.464, V6: 1.691, V7: -0.723, V8: 1.163, V9: -1.173, V10: 0.523, V11: 1.090, V12: 0.352, V13: -0.426, V14: 0.334, V15: 0.762, V16: -0.223, V17: 0.601, V18: 0.130, V19: 0.323, V20: 0.098, V21: 0.261, V22: 0.889, V23: -0.205, V24: -0.264, V25: -0.018, V26: 0.454, V27: 0.271, V28: 0.079, Amount: 1.000.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.521, ROA(A) before interest and % after tax: 0.573, ROA(B) before interest and depreciation after tax: 0.564, Operating Gross Margin: 0.605, Realized Sales Gross Margin: 0.606, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.797, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.304, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 0.000, Research and development expense rate: 0.000, Cash flow rate: 0.467, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.235, Net Value Per Share (B): 0.238, Net Value Per Share (A): 0.238, Net Value Per Share (C): 0.238, Persistent EPS in the Last Four Seasons: 0.246, Cash Flow Per Share: 0.337, Revenue Per Share (Yuan ¥): 0.081, Operating Profit Per Share (Yuan ¥): 0.118, Per Share Net profit before tax (Yuan ¥): 0.203, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.689, Regular Net Profit Growth Rate: 0.689, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 675000000.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.389, Current Ratio: 0.010, Quick Ratio: 0.009, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.006, Debt ratio %: 0.115, Net worth/Assets: 0.885, Long-term fund suitability ratio (A): 0.006, Borrowing dependency: 0.372, Contingent liabilities/Net worth: 0.007, Operating profit/Paid-in capital: 0.118, Net profit before tax/Paid-in capital: 0.201, Inventory and accounts receivable/Net value: 0.401, Total Asset Turnover: 0.186, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.008, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 0.001, Net Worth Turnover Rate (times): 0.043, Revenue per person: 0.026, Operating profit per person: 0.397, Allocation rate per person: 0.005, Working Capital to Total Assets: 0.825, Quick Assets/Total Assets: 0.625, Current Assets/Total Assets: 0.655, Cash/Total Assets: 0.201, Quick Assets/Current Liability: 0.010, Cash/Current Liability: 0.009, Current Liability to Assets: 0.120, Operating Funds to Liability: 0.356, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.002, Current Liabilities/Liability: 0.984, Working Capital/Equity: 0.737, Current Liabilities/Equity: 0.331, Long-term Liability to Current Assets: 0.000, Retained Earnings to Total Assets: 0.952, Total income/Total expense: 0.002, Total expense/Assets: 0.030, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 0.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.153, Current Liability to Liability: 0.984, Current Liability to Equity: 0.331, Equity to Long-term Liability: 0.111, Cash Flow to Total Assets: 0.634, Cash Flow to Liability: 0.457, CFO to Assets: 0.624, Cash Flow to Equity: 0.313, Current Liability to Current Assets: 0.028, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.820, Total assets to GNP price: 0.010, No-credit Interval: 0.624, Gross Profit to Sales: 0.605, Net Income to Stockholder's Equity: 0.842, Liability to Equity: 0.279, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.033.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.497, ROA(A) before interest and % after tax: 0.551, ROA(B) before interest and depreciation after tax: 0.555, Operating Gross Margin: 0.606, Realized Sales Gross Margin: 0.606, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.797, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.303, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 0.000, Research and development expense rate: 2160000000.000, Cash flow rate: 0.464, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.000, Net Value Per Share (B): 0.151, Net Value Per Share (A): 0.139, Net Value Per Share (C): 0.139, Persistent EPS in the Last Four Seasons: 0.217, Cash Flow Per Share: 0.327, Revenue Per Share (Yuan ¥): 0.020, Operating Profit Per Share (Yuan ¥): 0.101, Per Share Net profit before tax (Yuan ¥): 0.172, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.690, Regular Net Profit Growth Rate: 0.690, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 7350000000.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.265, Cash Reinvestment %: 0.383, Current Ratio: 0.004, Quick Ratio: 0.002, Interest Expense Ratio: 0.632, Total debt/Total net worth: 0.023, Debt ratio %: 0.212, Net worth/Assets: 0.788, Long-term fund suitability ratio (A): 0.005, Borrowing dependency: 0.386, Contingent liabilities/Net worth: 0.006, Operating profit/Paid-in capital: 0.101, Net profit before tax/Paid-in capital: 0.172, Inventory and accounts receivable/Net value: 0.403, Total Asset Turnover: 0.099, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.005, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 9460000000.000, Net Worth Turnover Rate (times): 0.043, Revenue per person: 0.004, Operating profit per person: 0.393, Allocation rate per person: 0.006, Working Capital to Total Assets: 0.709, Quick Assets/Total Assets: 0.261, Current Assets/Total Assets: 0.323, Cash/Total Assets: 0.019, Quick Assets/Current Liability: 0.003, Cash/Current Liability: 0.001, Current Liability to Assets: 0.147, Operating Funds to Liability: 0.347, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.002, Current Liabilities/Liability: 0.660, Working Capital/Equity: 0.726, Current Liabilities/Equity: 0.340, Long-term Liability to Current Assets: 0.015, Retained Earnings to Total Assets: 0.911, Total income/Total expense: 0.002, Total expense/Assets: 0.020, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 2150000000.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.488, Current Liability to Liability: 0.660, Current Liability to Equity: 0.340, Equity to Long-term Liability: 0.127, Cash Flow to Total Assets: 0.650, Cash Flow to Liability: 0.460, CFO to Assets: 0.606, Cash Flow to Equity: 0.317, Current Liability to Current Assets: 0.070, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.804, Total assets to GNP price: 0.001, No-credit Interval: 0.622, Gross Profit to Sales: 0.606, Net Income to Stockholder's Equity: 0.841, Liability to Equity: 0.291, Degree of Financial Leverage (DFL): 0.028, Interest Coverage Ratio (Interest expense to EBIT): 0.567, Net Income Flag: 1.000, Equity to Liability: 0.016.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.438, V2: -1.392, V3: -0.673, V4: 0.550, V5: -1.144, V6: -0.666, V7: -0.231, V8: -0.146, V9: 1.148, V10: -0.057, V11: -0.966, V12: -0.131, V13: -0.442, V14: 0.119, V15: 0.992, V16: 0.620, V17: -0.615, V18: 0.222, V19: -0.446, V20: 0.382, V21: 0.347, V22: 0.433, V23: -0.070, V24: 0.026, V25: -0.476, V26: 0.624, V27: -0.097, V28: 0.002, Amount: 305.950.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -0.618, V2: 0.695, V3: 1.393, V4: 1.143, V5: 0.279, V6: 0.731, V7: 0.825, V8: 0.188, V9: -0.338, V10: -0.452, V11: -1.570, V12: -0.927, V13: -1.368, V14: 0.266, V15: 0.529, V16: -0.895, V17: 0.451, V18: -0.205, V19: 1.132, V20: 0.146, V21: -0.066, V22: -0.171, V23: -0.230, V24: -0.796, V25: 0.548, V26: -0.077, V27: 0.069, V28: 0.067, Amount: 86.870.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 10, the number of cards is 1, the credit balance is 10000, the number of transactions is 28, the number of international transactions is 0, the credit limit is 9.'
Answer:
|
good
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: 2 way Comprehensive Plan, Duration: 146, Destination: THAILAND, Net Sales: 28.0, Commission: 0.0, Age: 36.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.506, ROA(A) before interest and % after tax: 0.462, ROA(B) before interest and depreciation after tax: 0.531, Operating Gross Margin: 0.561, Realized Sales Gross Margin: 0.561, Operating Profit Rate: 0.998, Pre-tax net Interest Rate: 0.796, After-tax net Interest Rate: 0.808, Non-industry income and expenditure/revenue: 0.303, Continuous interest rate (after tax): 0.781, Operating Expense Rate: 0.000, Research and development expense rate: 0.000, Cash flow rate: 0.463, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.000, Net Value Per Share (B): 0.139, Net Value Per Share (A): 0.139, Net Value Per Share (C): 0.139, Persistent EPS in the Last Four Seasons: 0.191, Cash Flow Per Share: 0.320, Revenue Per Share (Yuan ¥): 0.005, Operating Profit Per Share (Yuan ¥): 0.078, Per Share Net profit before tax (Yuan ¥): 0.151, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.690, Regular Net Profit Growth Rate: 0.690, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 4610000000.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.266, Cash Reinvestment %: 0.379, Current Ratio: 0.001, Quick Ratio: 0.001, Interest Expense Ratio: 0.630, Total debt/Total net worth: 0.033, Debt ratio %: 0.233, Net worth/Assets: 0.767, Long-term fund suitability ratio (A): 0.005, Borrowing dependency: 0.403, Contingent liabilities/Net worth: 0.010, Operating profit/Paid-in capital: 0.078, Net profit before tax/Paid-in capital: 0.151, Inventory and accounts receivable/Net value: 0.397, Total Asset Turnover: 0.031, Accounts Receivable Turnover: 0.004, Average Collection Days: 0.002, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 2420000000.000, Net Worth Turnover Rate (times): 0.023, Revenue per person: 0.022, Operating profit per person: 0.342, Allocation rate per person: 0.101, Working Capital to Total Assets: 0.654, Quick Assets/Total Assets: 0.039, Current Assets/Total Assets: 0.075, Cash/Total Assets: 0.018, Quick Assets/Current Liability: 0.000, Cash/Current Liability: 0.001, Current Liability to Assets: 0.133, Operating Funds to Liability: 0.343, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.001, Current Liabilities/Liability: 0.542, Working Capital/Equity: 0.714, Current Liabilities/Equity: 0.342, Long-term Liability to Current Assets: 0.138, Retained Earnings to Total Assets: 0.884, Total income/Total expense: 0.002, Total expense/Assets: 0.014, Current Asset Turnover Rate: 82200000.000, Quick Asset Turnover Rate: 4540000000.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 5560000000.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.651, Current Liability to Liability: 0.542, Current Liability to Equity: 0.342, Equity to Long-term Liability: 0.159, Cash Flow to Total Assets: 0.622, Cash Flow to Liability: 0.457, CFO to Assets: 0.586, Cash Flow to Equity: 0.306, Current Liability to Current Assets: 0.257, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.746, Total assets to GNP price: 0.127, No-credit Interval: 0.624, Gross Profit to Sales: 0.561, Net Income to Stockholder's Equity: 0.829, Liability to Equity: 0.299, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.014.'
Answer:
|
yes
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: CWT, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Rental Vehicle Excess Insurance, Duration: 1, Destination: MALAYSIA, Net Sales: 9.9, Commission: 5.94, Age: 37.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 23, the number of cards is 1, the credit balance is 4000, the number of transactions is 33, the number of international transactions is 0, the credit limit is 3.'
Answer:
|
good
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 44, the number of cards is 1, the credit balance is 0, the number of transactions is 34, the number of international transactions is 2, the credit limit is 17.'
Answer:
|
good
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -0.456, V2: 1.005, V3: 1.769, V4: 0.102, V5: -0.403, V6: -1.211, V7: 0.689, V8: -0.066, V9: -0.336, V10: -0.298, V11: -0.033, V12: -0.218, V13: -0.707, V14: 0.303, V15: 0.880, V16: 0.074, V17: -0.163, V18: -0.445, V19: -0.184, V20: 0.047, V21: -0.198, V22: -0.550, V23: 0.061, V24: 0.881, V25: -0.285, V26: 0.040, V27: 0.260, V28: 0.125, Amount: 6.990.'
Answer:
|
no
|
|
Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good'.
Text: 'The client is a female, the state number is 35, the number of cards is 1, the credit balance is 0, the number of transactions is 17, the number of international transactions is 0, the credit limit is 10.'
Answer:
|
good
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: CWT, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Rental Vehicle Excess Insurance, Duration: 29, Destination: AUSTRALIA, Net Sales: 39.6, Commission: 23.76, Age: 52.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.789, V2: -0.237, V3: -0.939, V4: 1.438, V5: -0.149, V6: -0.741, V7: 0.230, V8: -0.276, V9: 0.472, V10: 0.162, V11: -0.813, V12: 0.640, V13: 0.546, V14: 0.158, V15: 0.101, V16: -0.155, V17: -0.464, V18: -0.303, V19: -0.539, V20: -0.033, V21: 0.149, V22: 0.416, V23: 0.019, V24: -0.008, V25: 0.091, V26: -0.604, V27: 0.010, V28: -0.025, Amount: 109.110.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.503, ROA(A) before interest and % after tax: 0.569, ROA(B) before interest and depreciation after tax: 0.552, Operating Gross Margin: 0.602, Realized Sales Gross Margin: 0.602, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.797, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.304, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 8770000000.000, Research and development expense rate: 362000000.000, Cash flow rate: 0.465, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.165, Net Value Per Share (B): 0.207, Net Value Per Share (A): 0.207, Net Value Per Share (C): 0.207, Persistent EPS in the Last Four Seasons: 0.248, Cash Flow Per Share: 0.351, Revenue Per Share (Yuan ¥): 0.116, Operating Profit Per Share (Yuan ¥): 0.126, Per Share Net profit before tax (Yuan ¥): 0.203, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.689, Regular Net Profit Growth Rate: 0.689, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 8340000000.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.411, Current Ratio: 0.007, Quick Ratio: 0.006, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.019, Debt ratio %: 0.201, Net worth/Assets: 0.799, Long-term fund suitability ratio (A): 0.007, Borrowing dependency: 0.375, Contingent liabilities/Net worth: 0.005, Operating profit/Paid-in capital: 0.125, Net profit before tax/Paid-in capital: 0.201, Inventory and accounts receivable/Net value: 0.418, Total Asset Turnover: 0.213, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.009, Inventory Turnover Rate (times): 0.000, Fixed Assets Turnover Frequency: 0.002, Net Worth Turnover Rate (times): 0.079, Revenue per person: 0.066, Operating profit per person: 0.404, Allocation rate per person: 0.004, Working Capital to Total Assets: 0.783, Quick Assets/Total Assets: 0.625, Current Assets/Total Assets: 0.762, Cash/Total Assets: 0.176, Quick Assets/Current Liability: 0.006, Cash/Current Liability: 0.005, Current Liability to Assets: 0.199, Operating Funds to Liability: 0.352, Inventory/Working Capital: 0.278, Inventory/Current Liability: 0.004, Current Liabilities/Liability: 0.952, Working Capital/Equity: 0.736, Current Liabilities/Equity: 0.342, Long-term Liability to Current Assets: 0.001, Retained Earnings to Total Assets: 0.942, Total income/Total expense: 0.002, Total expense/Assets: 0.021, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 9950000000.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.049, Current Liability to Liability: 0.952, Current Liability to Equity: 0.342, Equity to Long-term Liability: 0.114, Cash Flow to Total Assets: 0.688, Cash Flow to Liability: 0.465, CFO to Assets: 0.639, Cash Flow to Equity: 0.327, Current Liability to Current Assets: 0.041, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.816, Total assets to GNP price: 0.007, No-credit Interval: 0.624, Gross Profit to Sales: 0.602, Net Income to Stockholder's Equity: 0.843, Liability to Equity: 0.289, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.017.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 4, Destination: INDONESIA, Net Sales: 10.0, Commission: 0.0, Age: 36.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -0.961, V2: 1.060, V3: 0.194, V4: -2.600, V5: 0.520, V6: -0.994, V7: 0.864, V8: 0.347, V9: -0.367, V10: -1.294, V11: 0.501, V12: 0.995, V13: 0.118, V14: 0.622, V15: -1.192, V16: 0.597, V17: -0.931, V18: -0.342, V19: -0.488, V20: -0.180, V21: -0.199, V22: -0.658, V23: -0.124, V24: -0.381, V25: -0.061, V26: 0.379, V27: 0.103, V28: 0.088, Amount: 1.000.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: CWT, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Rental Vehicle Excess Insurance, Duration: 9, Destination: MALAYSIA, Net Sales: 69.3, Commission: 41.58, Age: 52.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.472, ROA(A) before interest and % after tax: 0.537, ROA(B) before interest and depreciation after tax: 0.525, Operating Gross Margin: 0.595, Realized Sales Gross Margin: 0.595, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.797, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.304, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 5920000000.000, Research and development expense rate: 883000000.000, Cash flow rate: 0.465, Interest-bearing debt interest rate: 7000000.000, Tax rate (A): 0.037, Net Value Per Share (B): 0.169, Net Value Per Share (A): 0.169, Net Value Per Share (C): 0.169, Persistent EPS in the Last Four Seasons: 0.214, Cash Flow Per Share: 0.318, Revenue Per Share (Yuan ¥): 0.012, Operating Profit Per Share (Yuan ¥): 0.094, Per Share Net profit before tax (Yuan ¥): 0.171, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.689, Regular Net Profit Growth Rate: 0.689, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 6760000000.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.377, Current Ratio: 0.033, Quick Ratio: 0.025, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.001, Debt ratio %: 0.035, Net worth/Assets: 0.965, Long-term fund suitability ratio (A): 0.005, Borrowing dependency: 0.370, Contingent liabilities/Net worth: 0.005, Operating profit/Paid-in capital: 0.094, Net profit before tax/Paid-in capital: 0.170, Inventory and accounts receivable/Net value: 0.398, Total Asset Turnover: 0.099, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.007, Inventory Turnover Rate (times): 8790000000.000, Fixed Assets Turnover Frequency: 0.000, Net Worth Turnover Rate (times): 0.021, Revenue per person: 0.015, Operating profit per person: 0.390, Allocation rate per person: 0.015, Working Capital to Total Assets: 0.877, Quick Assets/Total Assets: 0.445, Current Assets/Total Assets: 0.563, Cash/Total Assets: 0.150, Quick Assets/Current Liability: 0.025, Cash/Current Liability: 0.024, Current Liability to Assets: 0.033, Operating Funds to Liability: 0.349, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.020, Current Liabilities/Liability: 0.816, Working Capital/Equity: 0.738, Current Liabilities/Equity: 0.327, Long-term Liability to Current Assets: 0.000, Retained Earnings to Total Assets: 0.933, Total income/Total expense: 0.002, Total expense/Assets: 0.009, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 0.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 0.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.409, Current Liability to Liability: 0.816, Current Liability to Equity: 0.327, Equity to Long-term Liability: 0.111, Cash Flow to Total Assets: 0.650, Cash Flow to Liability: 0.464, CFO to Assets: 0.575, Cash Flow to Equity: 0.315, Current Liability to Current Assets: 0.009, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.799, Total assets to GNP price: 0.002, No-credit Interval: 0.797, Gross Profit to Sales: 0.595, Net Income to Stockholder's Equity: 0.840, Liability to Equity: 0.276, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.565, Net Income Flag: 1.000, Equity to Liability: 0.108.'
Answer:
|
no
|
|
Identify the claim status of insurance companies using the following table attributes for travel insurance status. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes including 5 categorical attributes and 4 numerical attributes are as follows:
Agency: Name of agency (categorical).
Agency Type: Type of travel insurance agencies (categorical).
Distribution Channel: Distribution channel of travel insurance agencies (categorical).
Product Name: Name of the travel insurance products (categorical).
Duration: Duration of travel (categorical).
Destination: Destination of travel (numerical).
Net Sales: Amount of sales of travel insurance policies (categorical).
Commission: Commission received for travel insurance agency (numerical).
Age: Age of insured (numerical).
For instance: 'The insurance company has attributes: Agency: CBH, Agency Type: Travel Agency, Distribution Chanel: Offline, Product Name: Comprehensive Plan, Duration: 186, Destination: MALAYSIA, Net Sales: -29, Commision: 9.57, Age: 81.', should be classified as 'no'.
Text: 'The insurance company has attributes: Agency: EPX, Agency Type: Travel Agency, Distribution Channel: Online, Product Name: Cancellation Plan, Duration: 6, Destination: CHINA, Net Sales: 37.0, Commission: 0.0, Age: 36.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: -1.834, V2: 0.710, V3: 0.542, V4: 4.660, V5: -0.126, V6: 1.004, V7: -0.544, V8: 1.109, V9: -1.746, V10: 1.181, V11: -1.880, V12: -0.878, V13: -0.143, V14: 0.703, V15: 0.942, V16: 0.494, V17: 0.291, V18: 0.735, V19: 1.102, V20: 0.335, V21: 0.282, V22: 0.394, V23: -0.173, V24: 0.481, V25: -0.449, V26: 0.390, V27: 0.126, V28: -0.071, Amount: 105.930.'
Answer:
|
no
|
|
Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. For instance, 'The client has attributes: V1: 0.144, V2: 0.358, V3: 1.220, V4: 0.331, V5: -0.273, V6: 0.429, V7: -0.307, V8: -0.577, V9: 0.116, V10: -0.337, V11: 1.016, V12: 1.043, V13: -0.527, V14: 0.160, V15: -0.951, V16: -0.452, V17: 0.166, V18: -0.446, V19: 0.036, V20: -0.275, V21: 0.768, V22: -0.051, V23: -0.180, V24: 0.067, V25: 0.741, V26: 0.477, V27: 0.152, V28: 0.201, Amount: 6.990.' should be classified as 'no'.
Text: 'The client has attributes: V1: 1.921, V2: -0.462, V3: 0.148, V4: 0.343, V5: -0.661, V6: 0.358, V7: -1.112, V8: 0.181, V9: 2.324, V10: -0.252, V11: 1.904, V12: -1.240, V13: 2.222, V14: 1.208, V15: -0.632, V16: 0.769, V17: -0.136, V18: 0.676, V19: -0.300, V20: -0.144, V21: -0.054, V22: 0.188, V23: 0.354, V24: 0.682, V25: -0.639, V26: 0.390, V27: -0.043, V28: -0.045, Amount: 15.950.'
Answer:
|
no
|
|
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'.
Text: 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.494, ROA(A) before interest and % after tax: 0.549, ROA(B) before interest and depreciation after tax: 0.545, Operating Gross Margin: 0.613, Realized Sales Gross Margin: 0.613, Operating Profit Rate: 0.999, Pre-tax net Interest Rate: 0.797, After-tax net Interest Rate: 0.809, Non-industry income and expenditure/revenue: 0.303, Continuous interest rate (after tax): 0.782, Operating Expense Rate: 0.000, Research and development expense rate: 806000000.000, Cash flow rate: 0.463, Interest-bearing debt interest rate: 0.000, Tax rate (A): 0.226, Net Value Per Share (B): 0.179, Net Value Per Share (A): 0.179, Net Value Per Share (C): 0.179, Persistent EPS in the Last Four Seasons: 0.221, Cash Flow Per Share: 0.320, Revenue Per Share (Yuan ¥): 0.033, Operating Profit Per Share (Yuan ¥): 0.112, Per Share Net profit before tax (Yuan ¥): 0.176, Realized Sales Gross Profit Growth Rate: 0.022, Operating Profit Growth Rate: 0.848, After-tax Net Profit Growth Rate: 0.690, Regular Net Profit Growth Rate: 0.690, Continuous Net Profit Growth Rate: 0.218, Total Asset Growth Rate: 7250000000.000, Net Value Growth Rate: 0.000, Total Asset Return Growth Rate Ratio: 0.264, Cash Reinvestment %: 0.379, Current Ratio: 0.011, Quick Ratio: 0.006, Interest Expense Ratio: 0.631, Total debt/Total net worth: 0.007, Debt ratio %: 0.124, Net worth/Assets: 0.876, Long-term fund suitability ratio (A): 0.006, Borrowing dependency: 0.374, Contingent liabilities/Net worth: 0.005, Operating profit/Paid-in capital: 0.112, Net profit before tax/Paid-in capital: 0.175, Inventory and accounts receivable/Net value: 0.404, Total Asset Turnover: 0.154, Accounts Receivable Turnover: 0.001, Average Collection Days: 0.008, Inventory Turnover Rate (times): 6960000000.000, Fixed Assets Turnover Frequency: 0.000, Net Worth Turnover Rate (times): 0.036, Revenue per person: 0.012, Operating profit per person: 0.396, Allocation rate per person: 0.005, Working Capital to Total Assets: 0.816, Quick Assets/Total Assets: 0.337, Current Assets/Total Assets: 0.548, Cash/Total Assets: 0.060, Quick Assets/Current Liability: 0.006, Cash/Current Liability: 0.003, Current Liability to Assets: 0.097, Operating Funds to Liability: 0.345, Inventory/Working Capital: 0.277, Inventory/Current Liability: 0.011, Current Liabilities/Liability: 0.739, Working Capital/Equity: 0.737, Current Liabilities/Equity: 0.331, Long-term Liability to Current Assets: 0.005, Retained Earnings to Total Assets: 0.935, Total income/Total expense: 0.002, Total expense/Assets: 0.049, Current Asset Turnover Rate: 0.000, Quick Asset Turnover Rate: 8820000000.000, Working capitcal Turnover Rate: 0.594, Cash Turnover Rate: 4450000000.000, Cash Flow to Sales: 0.672, Fixed Assets to Assets: 0.225, Current Liability to Liability: 0.739, Current Liability to Equity: 0.331, Equity to Long-term Liability: 0.115, Cash Flow to Total Assets: 0.630, Cash Flow to Liability: 0.457, CFO to Assets: 0.582, Cash Flow to Equity: 0.313, Current Liability to Current Assets: 0.028, Liability-Assets Flag: 0.000, Net Income to Total Assets: 0.805, Total assets to GNP price: 0.005, No-credit Interval: 0.624, Gross Profit to Sales: 0.613, Net Income to Stockholder's Equity: 0.841, Liability to Equity: 0.280, Degree of Financial Leverage (DFL): 0.027, Interest Coverage Ratio (Interest expense to EBIT): 0.566, Net Income Flag: 1.000, Equity to Liability: 0.030.'
Answer:
|
no
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.