Impact of Real Time Fraud Prevention on Online Resale Platform using Machine Learning and Device Fingerprint Techniques

摘要

The continuous rise in online resale transactions is associated with increased fraudulent activities in modern world economies. Customers are financially and emotionally impacted due to these fraudulent activities. To detect and prevent online fraudulent activities, there is a need for an efficient and impactful fraud detection system. Real-time fraud prevention on an online resale platform is a crucial aspect of ensuring a secure and trustworthy platform for both buyers and sellers. This leads to an increase in user trust, customer satisfaction, and reliable brand equity. In this paper, a real-time fraud detection technique is proposed with a live data stream, device fingerprint (DFP) algorithm, and machine learning techniques to improve the latency of fraudster detection and minimize the impact of fraudster activities. Real-time fraud detection mainly works on repeat fraudsters with multiple user accounts. Fraudster behavior and pattern analysis is conducted to build multiple statistics rules, seed fraudster detectors, machine learning models, and DFP mappers. The improved proposed model is quantitatively verified by the performance measuring parameter. From the result analysis, it is concluded that DFP-based real-time fraud detection increases the efficiency of detecting fraudsters and decreases the latency as compared to other conventional methods. It significantly prevents the customers from fraudulent activities and enhances the overall customer experience to a certain level.