Enhancing Online Transaction Security: A Study on Fraud Detection and Prevention with HMM and Behavior Analysis

This research paper's goal is to recognize and stop fraudulent financial transactions. Credit card payments are now widely accepted for both online and offline purchases due to the quick development of internet technology and the rise in popularity of online payment methods like RTGS and NEFT. Despite this, there has been an increase in online transaction deception. Criminals have inflicted enormous losses on banks by capitalizing on the widespread use of network transactions to perpetrate crimes. At the moment, fraudulent transactions remain undetected until the fraud detection system verifies their completion. By applying the Hidden Markov Model (HMM) to this issue, fraudulent activities can be promptly averted through the implementation of a transaction verification code that is emailed to the user. An area 1-time payment gateway mechanism has been incorporated into the system in order to optimize its performance. Furthermore, behavior analysis will be implemented to gain insights into the spending habits of the user. This HMM and behavior analysis combination will facilitate sophisticated fraud analysis with a low false alarm ratio.