Retail Credit Default Probability Prediction: Practice of End-to-End Machine Learning Modeling Workflow

The open-source project Credit-Risk-PD-Model (author: lesupi-neo, released on GitHub on June 16, 2026) introduced in this article builds a complete machine learning pipeline for retail credit default probability (PD) prediction, covering data preprocessing, feature engineering, model training, and evaluation, providing reusable technical references for practitioners in the financial risk management field.

Credit risk is one of the core risks for financial institutions. Accurate PD prediction in retail credit is the foundation for businesses such as risk pricing and credit approval. After 2008, Basel III promoted the application of ML credit scoring models; traditional models (e.g., logistic regression) have good interpretability but are limited in handling nonlinear and high-dimensional features. ML can mine complex patterns to improve accuracy, but financial scenarios have strict requirements on model stability, interpretability, and fairness, making modeling challenging.

PD refers to the possibility that a borrower will fail to repay debts on time in the future, which is crucial for multiple business links:

1. Risk pricing: Calculate expected losses to determine interest rates;
2. Credit approval: Set thresholds for automated decision-making;
3. Capital measurement: Calculate regulatory capital according to Basel Accords;
4. Portfolio management: Evaluate portfolio risks to support limit setting, etc.

The project builds a modular pipeline:

• Data preprocessing: Missing value handling (mean/median imputation, etc.), outlier detection, category encoding, time-series-friendly data splitting;
• Feature engineering: Statistical features (mean/variance), ratio features (asset-liability ratio), time features, interaction features;
• Model training: Common models like logistic regression (baseline), XGBoost/LightGBM (gradient boosting trees), random forest, neural networks;
• Model evaluation: Discriminative power (AUC-ROC/KS), calibration, stability (PSI), interpretability (SHAP/feature importance).

1. Imbalanced sample handling: Oversampling (SMOTE), undersampling, cost-sensitive learning, threshold adjustment;
2. Time-series cross-validation: Use time windows to avoid data leakage;
3. Feature selection: Filter methods (statistical tests), wrapper methods (RFE), embedded methods (L1 regularization).

Provides financial institutions and practitioners with:

1. Rapid prototype development: Quickly build models based on the code framework;
2. Best practice references: Industry best practices for data processing, feature engineering, etc.;
3. Algorithm comparison benchmarks: Reproduce experiments to establish performance benchmarks;
4. Teaching and training: Serve as a case study for the combination of financial risk management and ML.

Deployment considerations:

1. Data privacy compliance: Comply with regulations like GDPR to protect sensitive information;
2. Model fairness: Monitor for discriminatory predictions;
3. Interpretability: Use SHAP/LIME to meet regulatory requirements;
4. Model drift monitoring: Establish mechanisms to detect data/concept drift and trigger retraining.

This project provides a complete ML implementation reference for PD prediction, demonstrating typical processes and technical points in financial risk management, and serves as a research resource for fintech and risk management professionals. Future directions include the application of alternative data, exploration of deep learning, federated learning, real-time decision-making, etc.