AI-POWERED FRAUD DETECTION IN DIGITAL FINANCIAL PLATFORMS: A STATE-OF-THE-ART SURVEY
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Abstract
This rapid pace of digitization of financial services has greatly amplified the scale, complexity, and variety of financial transactions. Consequently, it has led to an increase in sophisticated fraudulent activities that challenge conventional detection methods. Manual and traditional solutions that are rule-based are unlikely to be effective in dealing with up-and-coming trends of fraud since they possess low levels of flexibility, high operation costs, and constrained capabilities of responding to fraud. As a result, AI has turned into a paradigm shift that can enhance the detection of fraud through automated learning and real-time analysis, and high-precision anomaly detection. The survey gives a detailed analysis of AI-based ML, deep learning (DL), unsupervised learning, and natural language processing (NLP) models applied to detect fraudulent behaviour in financial ecosystems. The paper has provided a detailed overview of what external and internal fraud is, examined traditional approaches and identified the downsides that can only be addressed with the help of AI. Besides that, it offers the most recent publications to analyze the progress of algorithms, performance metrics, the introduction of new tendencies, and the increased role of explainable and privacy-conscious AI. This work offers a comparative analysis of the latest models and points out how AI could make current fraud detection techniques more accurate, scalable, and flexible. The findings suggest that additional innovation needs to be maintained to address more intricate, data-driven financial fraud problems.
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