Dataset Information

This dataset contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005.

Content

There are 25 variables:

• ID: ID of each client
• LIMIT_BAL: Amount of given credit in NT dollars (includes individual and family/supplementary credit
• SEX: Gender (1=male, 2=female)
• EDUCATION: (1=graduate school, 2=university, 3=high school, 4=others, 5=unknown, 6=unknown)
• MARRIAGE: Marital status (1=married, 2=single, 3=others)
• AGE: Age in years
• PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months, ... 8=payment delay for eight months, 9=payment delay for nine months and above)
• PAY_2: Repayment status in August, 2005 (scale same as above)
• PAY_3: Repayment status in July, 2005 (scale same as above)
• PAY_4: Repayment status in June, 2005 (scale same as above)
• PAY_5: Repayment status in May, 2005 (scale same as above)
• PAY_6: Repayment status in April, 2005 (scale same as above)
• BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar)
• BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar)
• BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar)
• BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar)
• BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar)
• BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar)
• PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar)
• PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar)
• PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar)
• PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar)
• PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar)
• PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)
• default.payment.next.month: Default payment (1=yes, 0=no)

Inspiration

Some ideas for exploration:

1. How does the probability of default payment vary by categories of different demographic variables?
2. Which variables are the strongest predictors of default payment?

Acknowledgements

Any publications based on this dataset should acknowledge the following:

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

The original dataset can be found here at the UCI Machine Learning Repository.

Credit risk assessment remains a critical function within financial services, influencing lending decisions, portfolio risk management, and regulatory compliance. It integrates multiple categories of financial, transactional, and behavioral data to enable advanced machine learning applications in the domain of financial risk modeling.

Data Composition and Structure

The dataset comprises a total of 1,212 distinct features, systematically grouped into four principal categories, alongside a binary target variable. Each feature category represents a specific dimension of credit risk assessment, reflecting both internal transactional data and externally sourced credit bureau information.

Target Variable

The dependent variable, denoted as bad_flag, represents a binary risk classification outcome associated with each customer account. The variable takes the following values:

• 0: Denotes a low-risk, creditworthy customer
• 1: Denotes a high-risk, default-prone customer

This variable serves as the target for binary classification models aimed at predicting credit risk propensity.

Feature Groups

Each feature within a category follows a systematic sequential naming convention (e.g., transaction_attribute_1, bureau_1), facilitating programmatic identification and group-level analysis.

Data Characteristics

The dataset exhibits several characteristics that mirror operational credit risk data environments:

• High Dimensionality: The feature space exceeds 1,200 variables
• Mixed Data Types: Numerical values (continuous and discrete), binary indicators
• High Sparsity: A substantial proportion of features contain zero values or missing entries
• Value Range Disparity: Feature values exhibit significant variance, with magnitudes ranging from small ratios (0.001) to large transaction amounts (288,500)

Methodological Rationale

The dataset was constructed by simulating data generation processes typical within financial services institutions. Transactional behaviors, bureau records, and inquiry histories were aggregated and engineered into derivative features.

Daily, weekly and monthly data showing seasonally adjusted and non-seasonally adjusted UK spending using debit and credit cards. These are official statistics in development. Source: CHAPS, Bank of England.