An Integrated Approach to Online Fraud Prevention with Multiple Participants

Abstract

The detection of fraudulent activity provides a considerable issue in the arena of e-commerce, which is characterized by the presence of several players in transactions, including buyers, sellers, and intermediaries. In order to address this problem, the solution that we have proposed is centered on a Mult viewpoint approach, which is intended to improve the accuracy and efficiency of fraud detection operations. The first phase is the detection of user activities, which involves utilizing a variety of approaches such as behavioral analysis and the evaluation of transaction histories in order to acquire insights into the typical patterns of user behavior. We are able to build a baseline against which anomalous behaviors can be discovered by gaining an understanding of the typical user interactions that occur within the ecosystem of e-commerce services. Following that, we conduct an investigation into the examination of abnormalities for the purpose of feature extraction. With the use of sophisticated algorithms for anomaly detection, we examine transaction data in order to identify odd patterns that may be suggestive of fraudulent actions. Through the use of this procedure, we are able to extract significant characteristics that can be used as crucial indications for the detection of fraud. In conclusion, in order to construct our technique for detecting fraudulent activity, we make use of an ensemble classification model