Machine Learning Algorithms for Financial Fraud Detection

Financial institutions face a persistent arms race against increasingly sophisticated fraud, from card-not-present (CNP) transactions and account takeovers to synthetic identities and mule networks. This article surveys and systematizes machine-learning (ML) approaches for fraud detection across supervised, semi-supervised, and graph-based paradigms, with a focus on severe class imbalance, concept drift, and real-time constraints. We review data pipelines, feature engineering for transactional and device telemetry, and algorithmic choices (tree ensembles, margin-based models, deep learning, and graph neural networks). We discuss cost-sensitive learning, imbalanced evaluation (PR-AUC, MCC), explainability (SHAP/LIME), and privacy-preserving deployment (federated learning, differential privacy). A comparative illustration highlights the practical trade-offs among commonly used algorithms. We conclude with a roadmap that integrates MLOps, human-in-the-loop review, and regulatory compliance to reduce false positives while capturing evolving fraud patterns.