End-to-End Credit Risk Scoring System: From Modeling to Interpretable Decision Support

This project is an end-to-end credit risk scoring system released by GitHub user myrazd on June 15, 2026. It covers the entire workflow of credit risk scoring, loan default prediction, model interpretability analysis, and loan approval decision support. The project balances prediction accuracy and interpretability requirements, adopts multiple machine learning models (e.g., XGBoost, logistic regression), integrates interpretable technologies like SHAP, supports real-time services and monitoring operations, and provides compliant and efficient risk control solutions for financial institutions.

Credit risk scoring is a core capability of the financial industry. Traditional scorecard models have strong interpretability but struggle to capture complex nonlinear relationships. The development of machine learning technology has driven financial institutions to explore advanced modeling methods, while facing strict regulatory requirements for model interpretability. This project provides an end-to-end solution that balances prediction accuracy and interpretability needs.

The project adopts a typical machine learning engineering architecture:

• Data Layer: Raw credit application data cleaning and preprocessing, feature engineering (deriving hundreds of predictive variables), data validation (missing value/outlier handling, distribution drift monitoring);
• Model Layer: Implements logistic regression (baseline), gradient boosting trees (XGBoost/LightGBM), and ensemble models for selection;
• Service Layer: REST API encapsulation for real-time scoring, batch task processing, model version management, and A/B testing support.

Core functions include:

1. Loan Default Prediction: Binary classification framework outputs default probability, defines target variable (90-day overdue), reasonably divides time windows, and uses SMOTE/cost-sensitive learning to handle sample imbalance;
2. Credit Risk Scoring: Maps default probability to a score range of 300-850, corresponding to risk levels, and monitors score distribution and drift;
3. Model Interpretability: Integrates SHAP value analysis (global/local interpretation, interaction effects), feature importance visualization (waterfall charts, force plots, dependency plots), and counterfactual explanations;
4. Decision Support: Automatic approval/rejection, manual review, supplementary material suggestions, and dynamic adjustment of decision thresholds.

Technical points:

• Feature Engineering: Statistical features (debt-to-income ratio, credit history length), time-series features (6/12/24-month repayment patterns), aggregated features (multi-account summary), cross features (combinations like age and occupation);
• Model Training: Time-series cross-validation (to avoid data leakage), Bayesian optimization for hyperparameters, model calibration (to ensure probability authenticity);
• Monitoring and Operations: Performance monitoring (KS, AUC, PSI), feature drift detection, prediction distribution stability monitoring.

Application scenarios:

• Bank Credit Departments: Personal consumer loan approval, credit card application evaluation, credit limit adjustment for existing customers;
• Internet Finance Platforms: Small cash loan risk control, installment shopping credit evaluation, merchant financing access;
• Regulatory Compliance: Automatic model document generation, fairness audit support, interpretable report output.

Industry trends:

1. From Scorecards to Machine Learning: Traditional logistic regression scorecards are gradually replaced by tree models like XGBoost, and interpretable technologies like SHAP are becoming popular;
2. From Black Box to White Box: Regulatory pressure makes interpretable AI an engineering necessity, and this project demonstrates practical implementation;
3. From Offline to Real-Time: Stream computing technology drives the evolution of risk scoring towards real-time decision-making.

This project demonstrates a mature application model of machine learning in the financial risk control field. It not only focuses on model accuracy but also emphasizes interpretability, monitoring operations, and engineering practices, providing a valuable reference architecture for similar projects.