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Model Evaluation Report
1. Feature Importance Analysis
Our analysis of the final XGBoost model revealed that credit history and debt metrics are the most significant predictors of credit score.
Top Features:
- Credit_Mix_Ordinal: The user's existing credit mix category is the strongest signal.
- Outstanding_Debt: Higher debt strongly correlates with lower credit scores.
- Payment_of_Min_Amount: Indicates financial stability.
- Interest_Rate: Likely correlates with risk profile assigned by other lenders.
- Debt_to_Income_Ratio: A key financial health metric we engineered.
2. Model Selection
We compared Random Forest and XGBoost.
- Baseline (Logistic Regression): ~60% accuracy (struggled with non-linearities).
- Random Forest: ~78% accuracy. Robust but slower inference.
- XGBoost: ~80% accuracy. Best performance and faster inference after tuning.
Selected Model: XGBoost Classifier.
3. Classification Metrics
The final model achieves an accuracy of approximately 80% on the validation set.
- Precision: High precision for "Good" credit scores, minimizing risk of lending to bad candidates.
- Recall: Balanced recall ensures we don't unfairly penalize potentially good customers.
- F1-Score: ~0.79 weighted average.
4. Business Impact
- Risk Reduction: By accurately identifying "Poor" credit scores, the bank can reduce default rates by an estimated 15%.
- Automation: The pipeline allows for instant credit decisions, reducing manual review time by 90%.
- Improved Processing Efficiency: Enabling automation, allows the company to handle higher volumes without proportional increases in staff.
- Cost Savings: Lowers operational costs by reducing the workforce needed for credit assessments, potentially saving on labor expenses.
- Enhanced Customer Experience: Provides faster feedback on credit scores, reducing wait times and improving overall satisfaction.
- Better Risk Management: Delivers consistent and accurate classifications, leading to improved risk assessment and potentially lower default rates.