AI-Driven Fraud Detection in Digital Finance: Trends, Techniques, and Research Challenges
Abstract
The rapid growth of digital financial platforms and online transactions has significantly increased the complexity and volume of financial fraud, necessitating advanced intelligent detection mechanisms. This review paper presents a comprehensive analysis of Artificial Intelligence techniques used for fraud detection in banking, fintech, insurance, and digital payment systems. The paper examines supervised, unsupervised, and hybrid machine learning approaches including neural networks, anomaly detection, ensemble learning, graph analytics, and deep learning frameworks for identifying fraudulent activities. A systematic review of recent studies highlights the effectiveness of AI in detecting transaction fraud, identity theft, money laundering, and cyber-financial attacks with improved accuracy and reduced false positives. The paper also addresses practical challenges such as imbalanced datasets, adversarial attacks, real-time scalability, and data privacy regulations. Ethical implications and governance concerns associated with automated fraud detection are further explored. The review identifies emerging opportunities involving blockchain-integrated AI systems and federated fraud intelligence for future financial security ecosystems.
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