About This Assessment

This credit risk assessment is generated using a supervised machine-learning model trained on historical credit card repayment data. The inputs include customer demographics (credit limit, age, education, marital status), recent repayment behavior, monthly bill statement amounts, and previous payment amounts across multiple billing cycles.

Risk Score Interpretation

The model estimates the probability of default in the next payment period. A lower probability indicates lower risk. The decision outcome (Approve, Review, or Reject) is derived from calibrated risk thresholds and should be interpreted as decision support rather than a replacement for professional judgment.

• 0-30:

Low Risk - Approve with standard terms

• 31-60:

Medium Risk - Manual review required

• 61-100:

High Risk - Rejection recommended

Dataset and model

This system uses the Default of Credit Card Clients dataset from the UCI Machine Learning Repository (dataset link), which includes demographic attributes, credit limits, repayment behavior, bill amounts, and payment history for 30,000 credit card holders. A supervised machine-learning classification model is trained to predict the probability of default in the next billing cycle, with probability calibration applied to ensure reliable risk interpretation.