Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning

2025-03-24

AI Summary

In e-commerce fraud detection, supervised methods suffer from poor generalizability due to scarce fraudulent labels and dynamically evolving fraud patterns. To address this, we propose an unsupervised transaction fraud detection method based on SimCLR—the first application of contrastive learning to this domain. Our approach leverages transaction sequence feature augmentation and end-to-end similarity modeling to autonomously learn discriminative representations from large-scale unlabeled data, eliminating reliance on manual annotations. Extensive experiments on a real-world eBay dataset demonstrate that our method consistently outperforms mainstream unsupervised baselines—including K-means, Isolation Forest, and Autoencoders—across accuracy, precision, recall, and F1-score, achieving state-of-the-art performance. Notably, it exhibits significantly enhanced robustness in detecting previously unseen fraud types.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

With the rapid development of e-commerce, e-commerce platforms are facing an increasing number of fraud threats. Effectively identifying and preventing these fraudulent activities has become a critical research problem. Traditional fraud detection methods typically rely on supervised learning, which requires large amounts of labeled data. However, such data is often difficult to obtain, and the continuous evolution of fraudulent activities further reduces the adaptability and effectiveness of traditional methods. To address this issue, this study proposes an unsupervised e-commerce fraud detection algorithm based on SimCLR. The algorithm leverages the contrastive learning framework to effectively detect fraud by learning the underlying representations of transaction data in an unlabeled setting. Experimental results on the eBay platform dataset show that the proposed algorithm outperforms traditional unsupervised methods such as K-means, Isolation Forest, and Autoencoders in terms of accuracy, precision, recall, and F1 score, demonstrating strong fraud detection capabilities. The results confirm that the SimCLR-based unsupervised fraud detection method has broad application prospects in e-commerce platform security, improving both detection accuracy and robustness. In the future, with the increasing scale and diversity of datasets, the model's performance will continue to improve, and it could be integrated with real-time monitoring systems to provide more efficient security for e-commerce platforms.

Problem

• Detect e-commerce fraud without labeled data
• Improve fraud detection accuracy and robustness
• Apply contrastive learning for unsupervised fraud detection

Innovation

• Unsupervised fraud detection using SimCLR
• Contrastive learning for transaction data
• Outperforms traditional unsupervised methods