EVALUATING THE ACCURACY AND EFFECTIVENESS OF FRAUD DETECTION SYSTEMS

Abstract

Financial fraud, waste, and abuse cost the global economy an estimated $5.4 trillion annually, with digital payment systems becoming increasingly vulnerable. This study systematically evaluates modern fraud detection and prevention systems across major financial institutions, focusing on accuracy, scalability, and adaptability. Using a mixed-methods approach, the research assessed detection algorithms on four key dimensions: accuracy, efficiency, adaptability to new threats, and feasibility. Hybrid models combining supervised learning and unsupervised anomaly detection outperformed traditional rule-based systems, achieving 92.7% accuracy versus 78.3%. Graph-based deep learning models proved especially effective against organized fraud, reducing false positives by 34% and increasing true positives by 27%. As real-time, high-volume transactions rise, detection systems must scale accordingly. A classification framework is introduced, mapping systems by algorithm type, fraud category, and performance metrics. Key challenges identified include adversarial threats, real-time computational limits, and evolving fraud tactics. The study proposes a next-gen detection architecture featuring real-time adaptability, explainable AI, and cross-institutional data sharing—potentially reducing fraud losses by up to 41% when deployed at scale.