Behavioral and Hybrid Anomaly Detection Techniques for Enhanced Fraud Prevention in E-Commerce Transactions

This paper is designed to develop an automated fraud detection system for e-commerce by leveraging user behavior analysis and hybrid anomaly detection models. As e-commerce grows rapidly, fraud, which poses a huge financial risk and damages brand reputation, is also increasing. Modern fraud detection methods are often code-based or business-centric, limiting their ability to transform fraud strategies. This paper proposes a Behavior and Hybrid Anomaly Detection Model (BHADM) which integrates various data, including usage patterns, transaction details, and historical data, to increase accuracy. The BHAD model that combines vulnerability detection, formality testing, and risk scoring is then used to assess the probability of fraud for each transaction. Based on the risk score calculation, the system triggers alerts and suspicious transactions can be flagged or blocked. In addition, a study is ongoing to monitor performance and update the model to adapt to new fraud models. This method not only improves detection capability, but also maintains the performance of the operation, making it suitable for urgent applications in high-frequency e-commerce environments. The application process provides a unique, flexible and effective way to reduce e-commerce fraud transactions.