README.md

Let me provide a comprehensive introduction to this credit risk modeling project that encompasses the entire modeling lifecycle, from data preparation to final expected loss calculation.

Credit Risk Modeling: An End-to-End Implementation of Expected Loss Framework

This project implements a complete credit risk quantification system using modern statistical techniques and machine learning approaches. The framework follows Basel regulatory requirements and industry best practices to calculate Expected Credit Loss (ECL) through its three key components: Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD).

Let me visualize the project structure:

The project consists of six interconnected components, each building upon the previous:

1. Training Data Preparation (Notebook 1) This phase implements sophisticated feature engineering techniques for both continuous and categorical variables. Key methods include:

• Weight of Evidence (WoE) transformation
• Information Value (IV) calculation
• Fine and coarse classing for continuous variables
• Reference category selection for categorical variables

2. Test Data Preparation (Notebook 2) This component ensures consistent application of feature engineering approaches to new data, critical for model validation and production deployment. It includes:

• Consistent WoE transformations
• Handling of missing categories
• Data quality checks
• Feature consistency validation

3. PD Model Development (Notebook 3) This section develops the probability of default model and creates a credit scorecard. Key elements include:

• Logistic regression with p-values
• Scorecard scaling and calibration
• Risk segmentation
• Model performance assessment through ROC-AUC, Gini, and KS statistics

4. Model Monitoring Framework (Notebook 4) This component establishes a robust monitoring system focusing on:

• Population Stability Index (PSI) calculation
• Characteristic stability analysis
• Performance tracking
• Model degradation detection

5. LGD and EAD Models (Notebook 5) This section implements advanced techniques for loss and exposure estimation:

• Two-stage LGD modeling (recovery probability and amount)
• CCF-based EAD calculation
• Beta regression for bounded outcomes
• Component validation frameworks

6. Expected Loss Calculation (Notebook 6) The final component brings everything together to calculate expected losses:

• Component integration
• Portfolio-level calculations
• Risk contribution analysis
• Economic capital implications

The project uses Lending Club data, specifically:

• Training data: 2007-2014 loan data
• Validation data: 2015 loan data
• Preprocessed dataset for advanced modeling

Key Technical Innovations:

1. Statistical Methods:

• WoE transformation for optimal feature encoding
• Information Value for feature selection
• Fine-coarse classing for continuous variable treatment

2. Model Development:

• Logistic regression with statistical inference
• Linear regression with robust standard errors
• Beta regression for bounded outcomes

3. Model Validation:

• ROC curve analysis
• Gini coefficient calculation
• Kolmogorov-Smirnov tests
• Population stability assessment

4. Risk Metrics:

• PD calibration
• LGD component separation
• EAD estimation through CCF
• Expected loss integration

This comprehensive framework provides a production-ready credit risk quantification system that can be adapted for various lending contexts while maintaining regulatory compliance and statistical rigor.

Would you like me to elaborate on any specific component of this framework?