XGBoost Classifier
XGBoost is our primary fraud detection model due to its superior performance across all metrics. It excels at capturing complex non-linear patterns in transaction data and handles imbalanced fraud datasets effectively. The ensemble approach combines multiple decision trees with gradient boosting, making it highly accurate while remaining interpretable through SHAP analysis. Best suited for real-time fraud scoring in production environments.
Random Forest Classifier
Random Forest serves as our secondary benchmark model. While it performs well with strong accuracy and robust handling of noisy data, it falls slightly behind XGBoost in precision and recall. The model uses an ensemble of decision trees with bagging, providing good generalization. Useful for validation and comparison purposes, though XGBoost remains preferred for production fraud detection.
Isolation Forest
Isolation Forest specializes in detecting unknown fraud patterns through anomaly detection. Unlike supervised models, it identifies transactions that deviate significantly from normal behavior without requiring labeled fraud examples. Excellent for catching novel fraud schemes and zero-day attacks. Lower precision means more false positives, but high recall ensures few fraudulent transactions slip through. Best used as a complementary layer alongside XGBoost.
XGBoost Classifier
XGBoost is our primary fraud detection model due to its superior performance across all metrics. It excels at capturing complex non-linear patterns in transaction data and handles imbalanced fraud datasets effectively. The ensemble approach combines multiple decision trees with gradient boosting, making it highly accurate while remaining interpretable through SHAP analysis. Best suited for real-time fraud scoring in production environments.
Feature Importance
Random Forest Classifier
Random Forest serves as our secondary benchmark model. While it performs well with strong accuracy and robust handling of noisy data, it falls slightly behind XGBoost in precision and recall. The model uses an ensemble of decision trees with bagging, providing good generalization. Useful for validation and comparison purposes, though XGBoost remains preferred for production fraud detection.
Feature Importance
Isolation Forest
Isolation Forest specializes in detecting unknown fraud patterns through anomaly detection. Unlike supervised models, it identifies transactions that deviate significantly from normal behavior without requiring labeled fraud examples. Excellent for catching novel fraud schemes and zero-day attacks. Lower precision means more false positives, but high recall ensures few fraudulent transactions slip through. Best used as a complementary layer alongside XGBoost.
Feature Importance
