Fraud detection in financial services has evolved substantially with the integration of advanced machine learning techniques, replacing traditional rule-based systems that have shown diminishing effectiveness in recent years. This transformation has been driven by the exponential growth in transaction volume, velocity, and variety across digital financial ecosystems. Machine learning models, particularly ensemble techniques like Isolation Forests and XGBoost, alongside deep learning architectures such as autoencoders and neural networks, have demonstrated remarkable capabilities in identifying fraudulent patterns while significantly reducing false positives. The article examines how sophisticated feature engineering processes, including transaction velocity tracking, merchant category analysis, and device fingerprinting, serve as critical foundations for effective fraud detection. The challenges of extreme class imbalance are addressed through innovative resampling techniques and cost-sensitive learning frameworks. Operational implementation considerations, including real-time processing constraints, multi-layered architecture design, and the emerging role of graph-based fraud network analysis, are explored in depth. The findings reveal that optimized machine learning approaches not only enhance fraud detection rates but also minimize customer friction while meeting strict regulatory requirements for model explainability.
Keywords: class imbalance, feature engineering, financial fraud detection, graph-based network analysis, machine learning ensemble models, real-time decision systems
