End-to-end ML pipeline with explainable AI for transaction security
An advanced ML system for detecting fraudulent activities across e-commerce and banking transactions. The solution tackles the challenge of highly imbalanced fraud datasets, delivering accurate, interpretable, and production-ready fraud detection models.
Fraudulent transactions represent a small fraction of all activity, yet they cause significant financial losses and harm user trust. The system reduces false negatives (missed fraud) to limit losses, while also minimizing false positives to preserve customer experience.
Dual-Dataset Coverage: Unified solution for e-commerce and banking fraud detection
Advanced Feature Engineering: Temporal, behavioral, and geolocation-based features for richer fraud signals
Imbalance Handling: SMOTE and sampling strategies for learning from rare fraud cases
Explainable AI: SHAP for global feature importance and LIME for case-level transparency
MLOps Integration: MLflow for experiment tracking, model versioning, and deployment readiness
Data preprocessing pipelines for missing values, categorical encoding, and type handling
Integration of IP-based geolocation features and temporal behavior patterns
Comparative modeling across Logistic Regression, Random Forest, XGBoost, and LightGBM
Evaluation with imbalanced metrics (Precision, Recall, F1, ROC-AUC, PR-AUC)
Interpretability with SHAP and LIME for stakeholder transparency
Achieved 99.2% fraud detection accuracy with reduced false positives, enabling secure transactions and improving trust in digital financial systems.
Explore the complete implementation, documentation, and code on GitHub.