Hugging Face's logo

Datasets:
lainmn
/
AgentDS-RetailBanking

Tasks:
Tabular Classification
Feature Extraction
Text Classification
Languages:
English
Tags:
agentic-ai
synthetic-data
data-science
benchmark
human-AI-collaboration
License:
Dataset card Data Studio Files
xet
Community
lainmn commited on
Commit
5f12aca
·
verified ·
1 Parent(s): 651cf4f

Upload RetailBanking domain

Browse files
Files changed (10) hide show
  1. .gitattributes +3 -0
  2. RetailBanking/accounts_all.csv +0 -0
  3. RetailBanking/customer_panel_test.csv +0 -0
  4. RetailBanking/customer_panel_train.csv +0 -0
  5. RetailBanking/customers_all.csv +1001 -0
  6. RetailBanking/description.md +140 -0
  7. RetailBanking/device_sessions_all.json +3 -0
  8. RetailBanking/devices_all.csv +2216 -0
  9. RetailBanking/transactions_test.csv +3 -0
  10. RetailBanking/transactions_train.csv +3 -0
.gitattributes CHANGED
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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+ RetailBanking/device_sessions_all.json filter=lfs diff=lfs merge=lfs -text
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+ RetailBanking/transactions_test.csv filter=lfs diff=lfs merge=lfs -text
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+ RetailBanking/transactions_train.csv filter=lfs diff=lfs merge=lfs -text
RetailBanking/accounts_all.csv ADDED
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RetailBanking/customer_panel_test.csv ADDED
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RetailBanking/customer_panel_train.csv ADDED
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RetailBanking/customers_all.csv ADDED
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RetailBanking/description.md ADDED
@@ -0,0 +1,140 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Retail Banking Data Challenge Overview
2
+
3
+ Welcome to the retail banking data challenge! This dataset contains information for two predictive modeling tasks involving customer financial behavior, transaction monitoring, and credit risk assessment.
4
+
5
+ ## Directory & File Structure
6
+
7
+ The dataset is organized into CSV files and JSON logs:
8
+
9
+ ```
10
+ ├── customers_all.csv
11
+ ├── accounts_all.csv
12
+ ├── devices_all.csv
13
+ ├── device_sessions_all.json
14
+ ├── transactions_train.csv
15
+ ├── transactions_test.csv
16
+ ├── customer_panel_train.csv
17
+ ├── customer_panel_test.csv
18
+ ```
19
+
20
+ ## Customer Demographics
21
+
22
+ **File:** `customers_all.csv`
23
+
24
+ **Columns:**
25
+
26
+ - **CustomerID**: Unique identifier for each customer (e.g., C000001, C000002...)
27
+ - **Age**: Age of the customer in years
28
+ - **Tenure**: Number of years as a bank customer
29
+ - **CreditScore**: Credit score of the customer (300-850 scale)
30
+ - **HomeCity**: Customer's primary city of residence
31
+ - **AnnualSalary**: Customer's annual salary in USD
32
+
33
+ ## Account Information
34
+
35
+ **File:** `accounts_all.csv`
36
+
37
+ **Columns:**
38
+
39
+ - **CustomerID**: Identifier linking the account to a customer
40
+ - **AccountID**: Unique identifier for each account
41
+ - **Type**: Account type (checking, savings, credit_card)
42
+ - **Balance**: Current account balance in USD
43
+ - **Limit**: Credit limit for credit card accounts (empty for checking/savings)
44
+ - **OpenDate**: Date when the account was opened (YYYY-MM-DD format)
45
+
46
+ ## Device Associations
47
+
48
+ **File:** `devices_all.csv`
49
+
50
+ **Columns:**
51
+
52
+ - **CustomerID**: Identifier linking the device to a customer
53
+ - **DeviceID**: Unique identifier for each device used by the customer
54
+
55
+ ## Device Session Logs
56
+
57
+ **File:** `device_sessions_all.json`
58
+
59
+ **Structure:**
60
+ ```json
61
+ [
62
+ {
63
+ "CustomerID": "C000001",
64
+ "SessionID": "551a1ac3-4f20-4ce9-b6d6-c7cb07b460d5",
65
+ "Timestamp": "2025-01-01T01:11:12",
66
+ "City": "City096",
67
+ "IP": "192.168.1.100",
68
+ "DeviceID": "B5E74E",
69
+ "Actions": [
70
+ "login",
71
+ "account_view",
72
+ "payment",
73
+ "logout"
74
+ ]
75
+ }
76
+ ]
77
+ ```
78
+
79
+ Contains detailed session information including device usage patterns, location data, and user actions performed during each banking session.
80
+
81
+ ## Transaction Data
82
+
83
+ **Files:** `transactions_train.csv`, `transactions_test.csv`
84
+
85
+ **Columns:**
86
+
87
+ - **Timestamp_dt**: Date portion of the transaction timestamp
88
+ - **TxnID**: Unique identifier for each transaction
89
+ - **Timestamp**: Full timestamp of the transaction (YYYY-MM-DD HH:MM:SS)
90
+ - **SessionID**: Identifier linking to device session (if online transaction)
91
+ - **CustomerID**: Identifier linking the transaction to a customer
92
+ - **SrcAccount**: Source account identifier
93
+ - **DstAccount**: Destination account identifier (for transfers)
94
+ - **Channel**: Transaction channel (online, ATM, branch)
95
+ - **MCC_Group**: Merchant category code group (Groceries, Restaurants, Online Retail, etc.)
96
+ - **Amount**: Transaction amount in USD
97
+ - **FraudLabel** (train only): Binary indicator (0 or 1) of whether the transaction is fraudulent (target variable)
98
+
99
+ > Note: `FraudLabel` is not provided in the test set.
100
+
101
+ ## Customer Panel Data
102
+
103
+ **Files:** `customer_panel_train.csv`, `customer_panel_test.csv`
104
+
105
+ **Columns:**
106
+
107
+ - **CustomerID**: Identifier linking to customer information
108
+ - **Week**: Week number (1-26 representing weeks in the 6-month period)
109
+ - **Utilisation**: Credit utilization ratio (credit card balance / credit limit)
110
+ - **PaymentRatio**: Ratio of payments made to previous week's credit card balance
111
+ - **HardInquiries**: Number of hard credit inquiries during the period
112
+ - **DefaultLabel** (train only): Binary indicator (0 or 1) of whether the customer will default within the period (target variable)
113
+
114
+ > Note: `DefaultLabel` is not provided in the test set.
115
+
116
+ ## Challenge Tasks & Submission Format
117
+
118
+ ### Challenge 1: Fraud Detection
119
+
120
+ Predict the `FraudLabel` for each transaction in `transactions_test.csv`.
121
+
122
+ **Submission CSV:** `TxnID,FraudLabel`
123
+
124
+ **Evaluation Metric:** Macro-F1.
125
+
126
+ ### Challenge 2: Credit Default Prediction
127
+
128
+ Predict the `DefaultLabel` for each customer-week combination in `customer_panel_test.csv`.
129
+
130
+ **Submission CSV:** `CustomerID,Week,DefaultLabel`
131
+
132
+ **Evaluation Metric:** Macro-F1.
133
+
134
+ ## Notes & Tips
135
+
136
+ - Only the described columns are provided. Participants must infer any latent variables from provided transaction data, device sessions, and behavioral patterns.
137
+ - Data integration across multiple files using `CustomerID` is essential for effective modeling.
138
+ - Ensure submissions strictly adhere to the specified CSV formats.
139
+
140
+ Good luck!
RetailBanking/device_sessions_all.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e628abe3b5491ac8ff089d32b2613007b1d9573f1e197ef3be03adb9e1dcba6b
3
+ size 39256586
RetailBanking/devices_all.csv ADDED
@@ -0,0 +1,2216 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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