Comprehensive Analysis of Proactive Fraud Prevention and Contribution of Machine Language

Abstract: The rising trend of fraud is considered to be one of the most significant issues in the contemporary digital environment, especially in finance, e-commerce, insurance and telecom sectors. Conventional fraud detection approaches are primarily based on rule-based frameworks, whose effectiveness deteriorates as fraud evolves. In contrast to traditional approaches, machine learning (ML) and predictive analytics offer flexible and data-centric ways to prevent fraud proactively. This study focuses on how ML techniques such as supervised, unsupervised and deep learning can be utilized to detect.