Credit Scoring Card Model Based on Logistic Regression: Full Development Process and Implementation

Abstract Credit scoring models discretize variables using WOE encoding and apply logistic regression as a binary classification generalized linear model. The resulting credit scores guide credit approval decisions, credit limits, and interest rates, helping banks manage asset risk, improve income, and predict default rates.

Card Types

Application Score Card (A‑card) : Improves applicant performance prediction, speeds up automated approvals, and enhances decision consistency.

Behavior Score Card (B‑card) : Supports existing customer management, reduces churn of good customers, and provides early warnings for potential delinquents.

Collection Score Card (C‑card) : Optimizes collection strategies, raises recovery rates, and lowers unnecessary collection costs.

Full Development Process

The quantitative analysis team follows an eleven‑step workflow:

Step 1: Variable Pre‑selection – Apply equal‑frequency or optimal binning, compute Information Value (IV), and discard weak predictors.

Step 2: Variable Elimination – Use clustering or correlation analysis to remove multicollinearity.

Step 3: Data Discretization – Convert continuous variables into bins via supervised methods (BEST Ks, chi‑square, decision trees) and perform WOE transformation.

Step 4: Initial Modeling – Replace original variables with their WOE values, fit a logistic regression, and drop variables with negative coefficients.

Step 5: Logistic Regression Implementation – Each feature is multiplied by a coefficient, summed, and passed through a Sigmoid function to produce a probability; a threshold converts this to a class label.

Step 6: Optimization Algorithm – Identify optimal coefficients using methods such as Gradient Descent, Newton, Quasi‑Newton, or Conjugate Gradient.

Step 7: Gradient Descent – Iteratively update parameters: θ = θ - α * ∇J(θ) , where α is the learning rate.

Step 8: Manual Intervention – Adjust binning rules based on business meaning, limits, population share, and default rates.

Step 9: WOE Update – Re‑calculate WOE values after manual adjustments.

Step 10: Model Update – Re‑estimate logistic coefficients using the updated WOE.

Step 11: Score Conversion – Translate logistic outputs and WOE bins into credit scores.

Step 12: Model Performance Evaluation – Use AUC, KS, and PSI to assess discrimination power and population stability.

Step 13: Model Monitoring – Track PSI to ensure score distribution stability over time.

Step 14: Scoring Example – For a base score of 50, a customer with specific attributes (e.g., college degree, male, home ownership, >10 years employment) receives a final score of 109 (50 + 14 + 9 + 24 + 12).

Conclusion The article outlines the end‑to‑end implementation of a logistic‑regression‑based credit scoring card, covering algorithmic foundations, IV/WOE calculations, evaluation metrics, and practical considerations that help reduce mis‑judgment, improve approval objectivity, and enhance customer satisfaction.