COMPREHENSIVE ANALYSIS OF FRAUD DETECTION PREVENTION SYSTEMS FOR ACCURACY AND EFFICACY

Abstract

Financial fraud, waste, and abuse cost global economies an estimated $5.4 trillion annually, with digital payment platforms experiencing unprecedented vulnerability. This study presents a systematic evaluation of contemporary fraud detection and prevention systems across major financial institutions, analyzing their accuracy, efficacy, and scalability in high-volume transaction environments. The mixed-methods approach combined quantitative performance metrics from financial institutions with qualitative assessments from cybersecurity specialists to evaluate detection algorithms across four dimensions: detection accuracy (false positive/negative rates), computational efficiency, adaptability to emerging threats, and implementation feasibility. Results demonstrate that hybrid approaches combining supervised machine learning with unsupervised anomaly detection achieved superior performance (92.7% detection accuracy) compared to traditional rule-based systems (78.3%). Notably, models integrating graph-based network analysis with deep learning techniques showed particular promise in identifying sophisticated organized fraud schemes, reducing false positives by 34% while increasing true positive rates by 27% compared to standalone approaches. The rise of cloud computing and mobile transactions has fundamentally altered the fraud landscape, requiring detection systems that can process and analyze real- time streaming data at unprecedented scale. The comprehensive classification framework categorizes existing detection systems based on algorithmic approach, fraud typology, and quantitative performance metrics across diverse  financial contexts. The study identify critical challenges in current implementations, including the increasing sophistication of adversarial attacks, computational constraints in real-time environments, and the dynamic nature of fraudulent behaviors. Based on our findings, we propose a next-generation architectural framework for financial fraud detection that emphasizes real-time adaptability, explainable AI components, and cross-institutional collaboration, potentially reducing overall fraud losses by an estimated 41% when implemented at scale.