COMBATING BANKING FRAUD WITH IT: INTEGRATING MACHINE LEARNING AND DATA ANALYTICS

Abstract

Banking fraud poses a significant threat to financial institutions, customers, and the stability of the financial system. Traditional fraud detection methods, which rely heavily on rule-based systems, have proven inadequate against increasingly sophisticated fraud techniques. This paper explores the integration of Information Technology (IT), specifically Machine Learning (ML) and Data Analytics, in combating banking fraud. Through a comprehensive review of existing literature and case studies, advancements in fraud detection methodologies are highlighted, emphasizing the effectiveness of various machine learning models and the role of big data analytics in enhancing detection accuracy and real-time processing. Additionally, the challenges and limitations of implementing these technologies are discussed, along with future trends and developments that could shape the future of banking fraud prevention. The study aims to provide a holistic understanding of how IT-driven approaches can revolutionize fraud detection and offer practical insights for financial institutions seeking to bolster their defenses against fraud.