Fraud detection in financial transactions: state of the art

Abstract

The surge in digital financial transactions, fueled by the proliferation of online banking, ecommerce, and emerging technologies, has brought significant oppor- tunities and equally critical vulnerabilities. Fraudulent activities have evolved in parallel, leveraging the complexity and global reach of digital systems to exploit weaknesses. This paper investigates the multifaceted nature of fraud in financial transactions, focusing on key types such as credit card fraud, money laundering, insurance fraud, and emerging threats in cryptocurrency systems. In this paper, we establish a state-of-the art overview of fraud detection method- ologies, analyzing their strengths and limitations. Traditional rule-based ap- proaches are contrasted with modern machine learning (ML) models, hybrid frame- works, and the application of advanced technologies. The study highlights the critical role of systems capable of identifying complex fraud patterns while ad- dressing persistent challenges. By synthesizing findings from existing research and evaluating innovative methods, this paper provides actionable insights into enhancing the effectiveness and resilience of fraud detection systems.