Banks and lending institutions need to assess credit risk accurately or they lose money. Ensemble methods like random forests and gradient boosting consistently outperform single models on structured financial data. This program teaches you to build production-grade credit models that balance predictive accuracy with regulatory requirements.

Random Forests for Default Prediction

Random forests combine hundreds of decision trees, each trained on different subsets of your data. This reduces overfitting while maintaining strong predictive power. You'll build a model that predicts loan defaults using borrower characteristics, employment history, existing debt levels, and payment patterns.

The key challenge is class imbalance. Most loans don't default, so a naive model can achieve high accuracy by always predicting no default. We cover SMOTE, class weights, and threshold adjustment to handle this properly. You'll learn to optimize for business objectives rather than generic accuracy metrics.

Gradient Boosting for Loss Given Default

Once you know a loan might default, you need to estimate potential losses. Gradient boosting builds models sequentially, with each new tree correcting errors from previous ones. We implement XGBoost and LightGBM to predict loss severity and recovery rates.

You'll work with real anonymized mortgage data containing loan characteristics, property values, and historical recovery outcomes. The models need to handle missing data gracefully because financial records are never complete. We discuss feature importance analysis and partial dependence plots to understand what drives predictions.

Model Validation and Monitoring

Deploying a credit model isn't the end. Economic conditions change, borrower behavior shifts, and model performance degrades. You'll implement monitoring dashboards that track prediction distributions, feature drift, and calibration over time. We cover A/B testing frameworks for comparing new model versions against current production models.

The program addresses regulatory considerations around model explainability and fairness. You'll learn to generate reason codes explaining why an application was declined and test for disparate impact across demographic groups.

Technical Implementation

All code is in Python using scikit-learn, XGBoost, and SHAP for explainability. You'll build reproducible pipelines that handle data preprocessing, model training, and evaluation. The final project involves building a complete credit decisioning system with reject inference and population stability reporting.