FraudJudger: Fraud Detection on Digital Payment Platforms with Fewer Labels

Abstract

Automated fraud detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of users to defraud money, steal passwords, do money laundering, etc., which causes enormous losses to digital payment platforms and users. There are many challenges for fraud detection in practice. Traditional fraud detection methods require a large-scale manually labeled dataset, which is hard to obtain in reality. Manually labeled data cost tremendous human efforts. In our work, we propose a semi-supervised learning detection model, FraudJudger, to analyze user behaviors on digital payment platforms and detect fraud users with fewer labeled data in training. FraudJudger can learn the latent representations of users from raw data with the help of Adversarial Autoencoder (AAE). Compared with other state-of-the-art fraud detection methods, FraudJudger can achieve better detection performance with only 10% labeled data. Besides, we deploy FraudJudger on a real-world financial platform, and the experiment results show that our model can well generalize to other fraud detection contexts.