A Multi-Perspective Fraud Detection Method for Multi-Participant E-Commerce Transactions
DOI:
https://doi.org/10.69996/fmep.2025017Keywords:
E-commerce fraud detection, Machine learning, Process mining, Support Vector Machine (SVM)Abstract
E-commerce platforms face growing challenges in detecting and preventing fraudulent transactions due to the evolving strategies of attackers and the hidden nature of online interactions. Traditional fraud detection systems largely depend on historical order information, which limits their ability to detect dynamic and sophisticated fraudulent behavior. This paper introduces a novel fraud detection framework that combines machine learning techniques with process mining to monitor and analyze real-time user behavior within B2C e-commerce platforms. The approach begins with constructing a behavioral process model tailored to the transaction flows of e-commerce users. An anomaly detection method is then applied to extract critical features from event logs, capturing irregularities in transaction activities. These features are subsequently fed into a Support Vector Machine (SVM)-based classification model to accurately identify fraudulent transactions. Experimental results validate the proposed model's capability to detect complex, evolving fraud patterns with improved accuracy and efficiency. This integrated approach enhances the adaptability and robustness of fraud detection mechanisms in modern e-commerce environments.
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