AI-Based Anomaly Detection In Financial Fraud Prevention Systems

Abstract

Financial fraud continues to evolve with increasing digital transactions, global banking connectivity, and sophisticated cyber-attacks. Traditional rule-based systems are no longer adequate for detecting complex and emerging fraud patterns. Artificial Intelligence (AI)–driven anomaly detection models offer advanced capabilities for analyzing transactional behavior, identifying suspicious activities, and enhancing real-time decision-making. This article presents a comprehensive examination of AI-based anomaly detection techniques—including machine learning, deep learning, graph-based models, time-series analysis, and hybrid fraud detection frameworks. Two graphs illustrate the performance comparison of AI algorithms and the rising adoption of AI fraud detection across the financial sector. The article concludes with future challenges related to data imbalance, explainability, privacy, and adversarial attacks, while identifying future opportunities in federated learning, quantum-safe analytics, and real-time adaptive systems.