Online Fraud Detection Using Hidden Markov Model and Behavior Analysis

Abstract: In the realm of online shopping, credit cards have emerged as the preferred method of payment for many consumers. However, with their widespread use comes a troubling rise in fraudulent activities. The nature of online transactions makes them particularly vulnerable, as they require only the card details, which can be easily stored in digital formats, rather than the physical card itself. Unfortunately, current systems typically identify fraudulent transactions only after they have been completed, leaving consumers at risk. This paper introduces a promising solution utilizing Hidden Markov Models (HMM), a sophisticated stochastic approach designed to analyze systems that exhibit variability. By establishing specific threshold values, this innovative system can effectively discern between legitimate and fraudulent transactions, boasting an impressive accuracy rate of nearly 80% and rapid processing capabilities, as supported by recent studies. Moreover, Behavioral Analysis (BA) serves an important aspect in deciphering the spending patterns of cardholders, enhancing the system's ability to detect fraud. The integration of Hidden Markov Models allows for a cost-effective strategy to combat fraud, leveraging the forward-backward algorithm to achieve remarkable results. Together, these advancements pave the way for a safer online shopping experience, empowering consumers and fostering trust in digital transactions.