Machine Learning and Data Science for Financial Fraud Detection: A Critical Review of Predictive Analytics, Anomaly Detection, and Emerging Approaches

Financial fraud presents a persistent challenge to banks, payment providers, and other financial institutions as digital transactions increase in volume, speed, and complexity. Conventional rule-based controls remain useful for enforcing known policies, but they can be difficult to maintain when fraudulent behavior changes or when suspicious activity is distributed across many accounts, devices, and transactions. Machine learning (ML) and data science offer complementary methods for identifying patterns, estimating transaction risk, and prioritizing cases for investigation. This review critically examines supervised, unsupervised, semi-supervised, ensemble, and deep learning approaches used in financial fraud detection. It discusses the role of data preparation, feature engineering, anomaly detection, temporal and relational analysis, class-imbalance management, and evaluation metrics. The review finds that no single algorithm is universally superior: model performance depends on the fraud scenario, data quality, prevalence of fraud, label availability, decision latency, and the operational cost of errors. Tree-based ensembles are strong candidates for structured transaction data, while sequence models and graph learning can add value when temporal behavior and relationships among entities are important. However, severe class imbalance, concept drift, limited access to representative data, privacy constraints, false-positive burdens, and limited explainability continue to restrict practical deployment. The paper proposes a deployment-oriented framework that combines data governance, sound validation, cost-sensitive model selection, human investigation, and continuous monitoring. Future research should prioritize realistic temporal evaluation, explainable and privacy-preserving learning, graph-based behavioral analysis, and adaptive systems that can respond to changing fraud strategies.