Artificial Intelligence for Fraud Detection and Anti-Money Laundering in Financial Institutions: A Review
Abstract
The increasing volume and complexity of digital financial transactions have created significant challenges for banks and financial institutions in detecting fraud and preventing money laundering. Traditional rule-based systems often struggle to identify sophisticated, evolving, and previously unseen patterns of financial crime. Artificial Intelligence (AI) and Machine Learning (ML) offer advanced capabilities for analyzing large-scale transactional data and identifying anomalous behavior in near real time. This review examines the application of supervised, unsupervised, semi-supervised, and deep learning techniques for financial fraud detection and Anti-Money Laundering (AML). The study reviews applications including credit card fraud, payment fraud, account takeover, identity-related fraud, suspicious transaction detection, and transaction network analysis. Particular attention is given to anomaly detection, graph-based machine learning, natural language processing, and deep neural networks. The review evaluates the advantages and limitations of AI-driven systems compared with conventional rule-based approaches. Key challenges involving false positives, highly imbalanced datasets, explainability, data privacy, adversarial behavior, and regulatory requirements are discussed. The role of graph neural networks, federated learning, and explainable AI in developing next-generation financial crime detection systems is also examined. The study identifies future research opportunities for creating adaptive, transparent, privacy-preserving, and real-time AI systems for financial crime prevention.
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