A Comprehensive Strategy for Online Fraud Mitigation Involving Multiple Stakeholders

Abstract

In the field of e-commerce, which is defined by the involvement of several parties in transactions, including buyers, sellers, and intermediaries, the identification of fraudulent behavior presents a significant challenge. Our suggested method, which aims to increase the precision and effectiveness of fraud detection activities, is based on a multiviewpoint approach. In order to get insights into the normal patterns of user activity, a number of techniques, including behavioral analysis and the assessment of transaction histories, are employed in the first step, which is the detection of user activities. By learning about the regular user interactions that take place inside the ecosystem of ecommerce services, we are able to establish a baseline against which aberrant behaviors may be found. After that, we look into the analysis of anomalies in order to extract features. We analyze transaction data using advanced anomaly detection algorithms to find strange patterns that could be indicative of fraudulent activity. This process allows us to extract important traits that might serve as critical markers for fraud detection. Finally, we employ an ensemble classification model to build our method for identifying fraudulent activities.