Paper Key : IRJ************126
Author: Nasir Hussain Wali Mohammed Sayed
Date Published: 24 Mar 2025
Abstract
This article examines the transformative impact of artificial intelligence on financial fraud detectionsystems, with particular emphasis on machine learning approaches that have supplanted traditionalrule-based methodologies. The article presents a comprehensive taxonomy of supervised, unsupervised,and reinforcement learning models currently deployed in financial institutions, analyzing their respectivestrengths and limitations in detecting fraudulent activities. Through case studies from regulatory bodiesand major financial institutions, the article demonstrates how these technologies enhance transactionmonitoring, insider trading detection, and identity verification processes. The article further addressesimplementation challenges including data quality issues, false positive rates, and ethical considerationssurrounding algorithmic decision-making in financial security contexts. By synthesizing technicalanalysis with practical applications, this research provides valuable insights for financial institutionsseeking to strengthen their fraud detection capabilities while navigating the complex regulatory landscapeof AI implementation.
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