Overview
Developed a comprehensive credit risk assessment system that evaluates loan applications and predicts default probability using advanced machine learning techniques.
Key Features
- Multi-model ensemble approach
- Feature importance analysis
- Risk score calibration
- Regulatory compliance (Basel III)
- What-if scenario analysis
Technical Stack
- ML Models: XGBoost, CatBoost, Neural Networks
- Feature Engineering: Feature-engine, Category Encoders
- Model Monitoring: MLflow, Evidently AI
- Deployment: Docker, FastAPI, Redis
Results
- 87% AUC-ROC score on test dataset
- 40% reduction in bad loan approvals
- 15% increase in approval rate for good customers
- Compliant with regulatory requirements
Implementation
The model incorporates:
- Traditional credit bureau data
- Alternative data sources (payment history, employment)
- Behavioral features from application process
- Economic indicators and market conditions
Features advanced techniques:
- SMOTE for handling class imbalance
- Monotonic constraints for interpretability
- Calibrated probability scores
- Fairness metrics to prevent bias
Integrated with loan origination system for real-time decisioning with explainable AI outputs.