A Multi-Perspective Ensemble Learning Framework for Fraud Detection in Multi-Participant E-Commerce Transactions

Abstract

In the dynamic landscape of online commerce, where transactions involve various stakeholders like buyers, sellers, and intermediaries, detecting fraudulent activities emerges as a formidable task. Our innovative approach aims to revolutionize fraud detection by adopting a comprehensive Multifaceted Fraud Detection Framework. This framework amalgamates diverse methodologies to enhance the accuracy and efficiency of fraud detection mechanisms. Initially, our method centers on profiling user behaviors. We leverage an array of sophisticated techniques including behavioral analytics and transaction history examination to gain profound insights into customary user behavior patterns. This foundational understanding enables us to establish a benchmark for normal user interactions within the e-commerce ecosystem, facilitating the identification of anomalous behaviors. Next, we pivot towards the analysis of anomalies for feature extraction. Employing advanced anomaly detection algorithms, we meticulously scrutinize transactional data to unveil irregular patterns suggestive of potential fraudulent activities. This meticulous process enables us to distill crucial features that act as pivotal indicators for fraud detection. Finally, our approach employs an ensemble classification model to operationalize the fraud detection mechanism, thus eschewing reliance on any singular algorithm. Instead, we harness the collective power of ensemble algorithms such as Random Forest, Gradient Boosting, or AdaBoost. By feeding the extracted features into the ensemble model, we train it to discern between legitimate and fraudulent behaviors in multiparticipant e-commerce transactions. Through this innovative framework, we aim to establish a robust and adaptive fraud detection system that can effectively combat fraudulent activities in the ever-evolving landscape of online commerce.