Analyzing the Effectiveness of Traditional and Modern Methods in Controlling and Detecting Bank Fraud Using Regression Analysis

Abstract

Fraud has now become one of the most visible problems in the world banking sector, from which financial loss, as well as reputational damage, may be likely through very technically sophisticated fraud tactics. This paper is all about discussing the relative effectiveness of traditional approaches versus modern approaches applied in fraud detection methods among financial institutions. The traditional approach is an all-manual audit and red-flagging system, which is quite slow, responsive, and laborious. Advances in the field of technologies, including AI, ML, BD analytics, and RTM, are now surfacing to accelerate and identify this with greater accuracy in this new mechanism of audit detection and applied regression analysis to determine which method is more effective and better ones regarding speed, accuracy, and effectiveness. They explained 97% of the fraud variance in detection, but new methods have problems arising from disadvantages in high implementation costs and false positives. The classic methods are relevant for some contexts since they encompass human judgment and intervention. The findings indicate a need for a hybrid approach that incorporates the traditional with the modern. It would therefore suit detection and prevention in fraud cases within the banking industry to fully utilize advanced technologies while still providing personal oversight that might be required for other kinds of fraud.