This project focuses on predicting customer default risk using statistical feature engineering and interpretable modeling techniques. The goal is to build a robust and explainable model that can effectively distinguish between defaulters and non-defaulters.
Financial institutions face significant losses due to loan defaults. This project aims to:
- Identify high-risk customers
- Improve decision-making for credit approval
- Enhance risk segmentation using data-driven insights
The dataset contains customer-level financial and demographic information, including:
- Credit history
- Income details
- Loan attributes
- Behavioral indicators
- Handled missing values and inconsistencies
- Encoded categorical variables
- Scaled numerical features where required
- Applied Weight of Evidence (WoE) transformation
- Used Information Value (IV) for feature selection
- Removed multicollinearity using VIF
- Built a classification model for default prediction
- Ensured stability and generalization across data splits
- Compared model performance across segments
- Analyzed overfitting and generalization gap
- Strong separation achieved between high-risk and low-risk customer segments
- Reduced redundant features significantly, improving model stability
- Identified critical drivers of default risk using statistical measures
- Improved consistency of predictions across different customer groups
- Importance of interpretable models in financial applications
- Role of feature engineering in improving model performance
- Practical challenges in handling real-world tabular data
credit-risk-model/
│
├── notebook/
│ └── credit_risk_model.ipynb
│
├── artifacts/
│ └── model_data.joblib
│
├── README.md
└── requirements.txt
- Implement monotonic binning for better WoE stability
- Build a complete credit scorecard
- Deploy model using a simple API interface
If you have any questions or suggestions, feel free to connect.