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Datasets:
Tekhnika
/
fraud-model-starter-pack-free

Tasks:
Tabular Classification
Time Series Forecasting
Languages:
English
Size:
1K<n<10K
Tags:
tabular-data
analytics
fintech
fraud-detection
License:
Dataset card Data Studio Files
xet
 Community
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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 8 new columns ({'customer_segment', 'tenure_months', 'home_region', 'travel_frequency', 'risk_band', 'customer_name', 'credit_score_band', 'historical_chargeback_flag'}) and 9 missing columns ({'transaction_amount', 'merchant_id', 'card_id', 'transaction_id', 'entry_mode', 'transaction_date', 'transaction_currency', 'merchant_category', 'merchant_country'}).

This happened while the csv dataset builder was generating data using

hf://datasets/Tekhnika/fraud-model-starter-pack-free/sample_output/cardholders.csv (at revision a83b2b729b6eec14a89a6c50511df896469eadfb), [/tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/card_transactions.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/card_transactions.csv), /tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/cardholders.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/cardholders.csv), /tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/cards.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/cards.csv), /tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/daily_fraud_metrics.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/daily_fraud_metrics.csv), /tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/fraud_cases.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/fraud_cases.csv), /tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/merchant_risk_profiles.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/merchant_risk_profiles.csv)]

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1890, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 760, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              cardholder_id: int64
              credit_score_band: string
              customer_name: string
              home_region: string
              customer_segment: string
              tenure_months: int64
              travel_frequency: string
              historical_chargeback_flag: int64
              risk_band: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1420
              to
              {'transaction_id': Value('int64'), 'cardholder_id': Value('int64'), 'card_id': Value('int64'), 'merchant_id': Value('int64'), 'transaction_date': Value('string'), 'merchant_category': Value('string'), 'transaction_amount': Value('float64'), 'merchant_country': Value('string'), 'entry_mode': Value('string'), 'transaction_currency': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1892, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 8 new columns ({'customer_segment', 'tenure_months', 'home_region', 'travel_frequency', 'risk_band', 'customer_name', 'credit_score_band', 'historical_chargeback_flag'}) and 9 missing columns ({'transaction_amount', 'merchant_id', 'card_id', 'transaction_id', 'entry_mode', 'transaction_date', 'transaction_currency', 'merchant_category', 'merchant_country'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Tekhnika/fraud-model-starter-pack-free/sample_output/cardholders.csv (at revision a83b2b729b6eec14a89a6c50511df896469eadfb), [/tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/card_transactions.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/card_transactions.csv), /tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/cardholders.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/cardholders.csv), /tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/cards.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/cards.csv), /tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/daily_fraud_metrics.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/daily_fraud_metrics.csv), /tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/fraud_cases.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/fraud_cases.csv), /tmp/hf-datasets-cache/medium/datasets/51665461294514-config-parquet-and-info-Tekhnika-fraud-model-star-150e5afc/hub/datasets--Tekhnika--fraud-model-starter-pack-free/snapshots/a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/merchant_risk_profiles.csv (origin=hf://datasets/Tekhnika/fraud-model-starter-pack-free@a83b2b729b6eec14a89a6c50511df896469eadfb/sample_output/merchant_risk_profiles.csv)]
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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transaction_id
int64
cardholder_id
int64
card_id
int64
merchant_id
int64
transaction_date
string
merchant_category
string
transaction_amount
float64
merchant_country
string
entry_mode
string
transaction_currency
string
1
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jewelry
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uk
contactless
eur
2
10,277
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2025-04-19
gaming
212.56
fr
wallet
gbp
3
16,790
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686
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fuel
5,420.82
fr
manual_keyed
usd
4
7,848
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2025-07-25
fuel
6,169.53
uk
wallet
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2025-01-23
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6
16,546
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br
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2025-04-18
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contactless
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2025-01-08
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5,555.38
mx
manual_keyed
cad
9
17,179
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2025-11-29
electronics
5,130.5
fr
contactless
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10
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48
12,741
2025-06-04
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5,835.69
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ecommerce
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11
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4,763
2025-10-24
electronics
2,884.89
ae
ecommerce
gbp
12
11,776
394
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2025-07-23
gaming
3,384.85
br
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usd
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grocery
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br
manual_keyed
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cash_advance
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mx
chip
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gaming
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us
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2025-03-15
gaming
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mx
contactless
cad
17
8,155
13,983
2,129
2025-12-27
travel
660.94
mx
manual_keyed
gbp
18
13,521
2,576
12,402
2025-07-18
cash_advance
8,421.58
br
ecommerce
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19
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3,621
6
2025-06-09
cash_advance
4,746.31
mx
chip
gbp
20
960
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2025-03-18
gaming
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sg
chip
cad
21
673
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jewelry
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sg
manual_keyed
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22
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grocery
7,593.32
us
ecommerce
gbp
23
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2,936
1,917
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cash_advance
8,295.57
us
contactless
eur
24
9,047
23,271
7,212
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electronics
4,072.08
uk
chip
usd
25
8,769
8,149
14,466
2025-01-22
digital_services
1,990.05
br
wallet
usd
26
13,456
18,175
7,623
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electronics
5,359.13
de
wallet
gbp
27
11,077
3,295
5,117
2025-02-15
travel
4,054.85
uk
ecommerce
usd
28
5,065
13,504
2,683
2025-10-23
electronics
5,273.69
ae
chip
cad
29
17,517
8,676
7,018
2025-06-02
electronics
76.49
us
manual_keyed
cad
30
2,921
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2025-06-20
jewelry
3,102.42
mx
manual_keyed
usd
31
10,581
17,628
12,034
2025-04-01
fuel
4,771.47
de
chip
usd
32
17,432
10,518
14,043
2025-06-18
grocery
3,623.69
fr
ecommerce
eur
33
15,034
612
8,146
2025-08-13
grocery
2,027.45
br
ecommerce
eur
34
11,848
6,349
8,963
2025-08-14
travel
6,160.72
br
chip
usd
35
1,496
23,668
4,428
2025-08-20
grocery
86.23
sg
chip
gbp
36
2,289
15,602
14,580
2025-03-26
grocery
4,181.62
us
chip
usd
37
4,034
5,330
13,905
2025-09-08
cash_advance
1,614.44
mx
chip
eur
38
1,278
2,961
1,659
2025-01-22
fuel
762.13
de
contactless
eur
39
4,994
4,711
6,130
2025-02-12
cash_advance
1,481.3
uk
chip
cad
40
13,798
4,226
6,348
2025-01-21
grocery
3,268.67
mx
contactless
gbp
41
9,341
3,591
7,241
2025-06-12
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1,712.9
br
wallet
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42
3,618
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ae
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eur
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fuel
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mx
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46
13,237
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313
2025-10-27
travel
4,206.5
de
chip
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2025-09-03
fuel
5,615.04
de
manual_keyed
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48
11,756
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fuel
2,668.69
us
contactless
eur
49
9,095
2,803
6,713
2025-12-21
cash_advance
6,657
de
ecommerce
eur
50
10,971
6,562
2,939
2025-07-20
digital_services
1,579.68
de
manual_keyed
eur
51
3,254
7,661
8,850
2025-05-22
gaming
8,701.07
ae
wallet
usd
52
14,427
20,331
12,980
2025-11-30
digital_services
2,313.55
ae
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cad
53
2,935
23,650
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2025-01-31
jewelry
2,288.81
ae
manual_keyed
usd
54
10,684
18,072
3,131
2025-03-12
travel
268.65
mx
wallet
cad
55
5,396
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7,527
2025-10-11
fuel
6,963.27
sg
manual_keyed
cad
56
13,304
18,366
8,332
2025-11-14
jewelry
8,349.54
ae
wallet
gbp
57
5,293
9,105
2,424
2025-07-25
fuel
1,451.38
fr
contactless
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58
8,856
18,822
2,026
2025-09-06
fuel
7,547.01
de
contactless
eur
59
13,821
5,168
2,767
2025-08-22
cash_advance
5,632.55
fr
contactless
eur
60
15,220
3,854
11,294
2025-03-20
gaming
8,882.74
sg
ecommerce
gbp
61
10,924
22,478
3,361
2025-05-11
gaming
4,953.5
ae
chip
cad
62
13,642
23,802
5,142
2025-03-23
electronics
5,023.95
fr
manual_keyed
gbp
63
8,076
8,471
1,174
2025-04-18
fuel
9,247.09
uk
chip
gbp
64
7,968
6,688
8,456
2025-03-02
travel
2,100.94
us
ecommerce
cad
65
12,030
3,455
8,813
2025-07-06
digital_services
1,551.34
ae
contactless
eur
66
9,626
22,902
4,280
2025-02-07
electronics
3,167.51
uk
ecommerce
usd
67
8,644
22,896
11,620
2025-02-09
electronics
1,426.6
de
chip
eur
68
5,575
14,544
3,550
2025-09-04
cash_advance
3,745.71
mx
contactless
cad
69
15,154
24,004
8,966
2025-10-17
jewelry
7,028.19
fr
contactless
usd
70
12,486
19,070
3,997
2025-04-03
cash_advance
8,299.03
de
chip
cad
71
11,114
19,259
2,777
2025-07-11
gaming
1,972.7
br
wallet
cad
72
16,995
23,575
67
2025-01-07
gaming
3,371.14
mx
wallet
gbp
73
9,605
5,980
3,119
2025-10-26
digital_services
3,394.66
mx
contactless
eur
74
5,716
23,450
4,811
2025-10-18
electronics
2,648.86
fr
chip
cad
75
12,485
4,389
8,526
2025-03-29
fuel
7,256.35
br
wallet
usd
76
1,395
21,378
8,344
2025-03-16
fuel
4,485.01
de
manual_keyed
gbp
77
6,110
22,442
8,299
2025-12-17
digital_services
733.78
sg
wallet
eur
78
10,060
15,004
12,523
2025-04-23
digital_services
367.73
uk
wallet
gbp
79
17,195
3,005
9,169
2025-08-04
digital_services
8,377.97
sg
contactless
gbp
80
9,053
6,907
5,316
2025-07-17
electronics
8,404.31
mx
chip
usd
81
763
22,063
846
2025-08-25
fuel
484.52
de
contactless
eur
82
6,664
6,868
14,844
2025-05-11
digital_services
1,901.15
sg
chip
usd
83
5,907
8,797
11,396
2025-03-03
travel
6,626.13
ae
chip
gbp
84
14,490
6,331
14,982
2025-05-16
cash_advance
5,123.17
uk
wallet
gbp
85
15,185
18,593
11,919
2025-12-24
cash_advance
111.41
uk
chip
cad
86
12,410
7,131
2,525
2025-07-20
electronics
4,917.82
us
ecommerce
cad
87
1,117
13,778
9,161
2025-05-25
fuel
4,838.6
fr
wallet
gbp
88
2,154
13,339
2,061
2025-05-16
electronics
4,639.04
ae
chip
eur
89
4,358
9,209
8,658
2025-09-01
cash_advance
1,567.3
us
ecommerce
eur
90
9,546
9,038
4,928
2025-02-03
jewelry
3,924.19
uk
contactless
eur
91
14,055
17,823
11,740
2025-08-02
gaming
5,410.78
uk
wallet
cad
92
780
17,169
1,375
2025-03-21
jewelry
8,370.2
fr
ecommerce
gbp
93
16,388
16,137
4,770
2025-08-02
cash_advance
6,549.93
sg
manual_keyed
eur
94
10,033
13,075
8,246
2025-06-06
grocery
695.45
br
contactless
eur
95
1,193
2,023
4,736
2025-08-29
electronics
9,035.14
mx
chip
cad
96
866
5,663
14,837
2025-11-22
fuel
7,737.26
us
wallet
eur
97
7,984
2,344
6,847
2025-10-28
electronics
3,803.05
ae
chip
cad
98
11,880
3,270
4,880
2025-09-01
fuel
7,614.27
mx
contactless
gbp
99
466
13,813
13,806
2025-11-26
electronics
6,555.61
us
contactless
cad
100
4,887
18,845
4,883
2025-03-11
electronics
6,965.91
ae
chip
eur
End of preview.

Fraud Model Starter Pack

Free sample for fraud monitoring dashboards, suspicious activity analysis, and finance-oriented analytics workflows.

What is included

  • card_transactions.csv: 4398 rows, 10 columns
  • cardholders.csv: 439 rows, 9 columns
  • cards.csv: 593 rows, 9 columns
  • daily_fraud_metrics.csv: 733 rows, 10 columns
  • fraud_cases.csv: 967 rows, 10 columns
  • merchant_risk_profiles.csv: 366 rows, 9 columns

Why this dataset is useful

  • Useful for a first fraud dashboard or suspicious activity notebook.
  • Works well for SQL, notebooks, and BI prototyping.
  • Provides a reduced but representative sample of the core workflow in the full starter pack.

Starter use cases

  • Fraud baseline using linked workflow and event data.
  • Fraud monitoring dashboard for suspicious activity patterns.

Schema overview

card_transactions.csv

  • Rows: 4398
  • Columns: transaction_id, cardholder_id, card_id, merchant_id, transaction_date, merchant_category, transaction_amount, merchant_country, entry_mode, transaction_currency

cardholders.csv

  • Rows: 439
  • Columns: cardholder_id, credit_score_band, customer_name, home_region, customer_segment, tenure_months, travel_frequency, historical_chargeback_flag, risk_band

cards.csv

  • Rows: 593
  • Columns: card_id, cardholder_id, issue_date, status, card_product, network, credit_limit, card_present_usage_ratio, digital_wallet_flag

daily_fraud_metrics.csv

  • Rows: 733
  • Columns: metric_id, merchant_id, metric_date, transactions_total, approved_amount, decline_rate, fraud_alerts_total, confirmed_fraud_total, chargeback_rate, loss_amount

fraud_cases.csv

  • Rows: 967
  • Columns: fraud_case_id, transaction_id, cardholder_id, card_id, alert_date, fraud_type, investigation_status, chargeback_amount, loss_amount, model_score

merchant_risk_profiles.csv

  • Rows: 366
  • Columns: merchant_id, merchant_category, merchant_country, merchant_name, acquirer_region, merchant_size, card_not_present_share, chargeback_ratio, merchant_risk_band

Free vs full version

  • Free Kaggle sample: reduced rows, reduced columns, starter notebook, and enough linked finance fraud tables to validate the core workflow.
  • Full version: full row volume, richer feature coverage, and extra starter assets for dashboards, SQL, and fraud-analysis work.

Upgrade to full version

Notes

  • Contains generated data only and no real personal data.
  • Designed as a lightweight free sample for evaluation and discovery.
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