ABSTRACT
Fraud detection is a critical task across industries, demanding comprehensive and adaptive strategies to thwart evolving fraudulent schemes while maintaining financial system integrity. This essay explores the multifaceted nature of fraudulent transactions, identifying obstacles in detection and the technological strategies employed to mitigate risks. Through an analysis of various machine-learning approaches using transactional data, we evaluate effectiveness, address challenges such as imbalanced data, and explore feature importance to understand underlying factors contributing to fraud detection. Our findings offer valuable insights for fortifying fraud detection systems and improving financial security measures amidst adynamic landscape.
