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.

Introduction

As the digital economy grows, fraud in online transactions, especially in digital remittances, poses significant threats to banking and e-commerce. Traditional fraud detection methods relying solely on data mining are insufficient because transaction patterns vary by season, economic conditions, and customer behavior. To improve fraud detection, behavior analysis combined with machine learning techniques is increasingly used.

This study focuses on using Hidden Markov Models (HMM) to analyze cardholder buying behaviors across merchant categories to detect fraudulent transactions more accurately. The HMM leverages algorithms like forward-backward (Baum-Welch), forward, and Viterbi to train on transaction data, model hidden states, and predict transaction legitimacy.

Other machine learning methods such as Support Vector Machines (SVM), K-nearest neighbor, and Bayesian classifiers have also been applied in fraud detection with varying degrees of success. SVMs are accurate but require long training times, while K-nearest neighbor showed strong performance in comparison to other classifiers.

The HMM approach models transaction sequences with states representing spending levels and transitions capturing spending behavior over time. During fraud detection, the system compares current transactions to the user's established spending pattern stored in an SQL database. If a transaction’s probability falls below a threshold, additional security verification (e.g., security questions) is triggered.

Overall, this approach aims to enhance fraud detection by combining probabilistic models of user behavior with real-time transaction monitoring, improving accuracy over traditional rule-based systems.

Conclusion

The suggested structure, explored the effectiveness of HMM Markov for identifying fraudulent activities in online transactions. Every stage involved in the business procedure is regarded as stochastic processes of Hidden Markov Models, whereas the price intervals of transactions are viewed as observation symbols, and the purchased items are recognized as states within the Hidden Markov Model (HMM). The suggested system is also capable of scaling to manage large volumes of transactions. This system provides rapid results compared to the current system. Within the structure, every new transaction is evaluated in terms of authenticity or fraud according to the consumer\'s expenditure patterns. This structure will additionally determine whether transactions are fake or legitimate according to the established threshold values. The Fraud Detection system achieves an accuracy of almost 80% and demonstrates a high processing speed, as indicated by the comparative studies conducted. It is highly appropriate for detecting Online Transaction Fraud since it keeps a record of users, eliminating the need to verify the original user.

References

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Copyright

Copyright © 2025 Faith R Kakwere, Zheng Taotao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.