Horizontal Association Modeling: Deep Relation Modeling

The rapid development of internet finance has caused increasing concern in online payment fraud due to its great threat. Online payment fraud detection, a challenge faced by online service, plays an important role in rapidly evolving e-commerce. At present, most platforms use rule systems or machine learning based technologies to detect fraud. It is usually taken for granted that the occurrence of unauthorized behaviors is necessary for fraud detection in online payment services. Behavior-based methods are recognized as promising methods for online payment fraud detection. However, building high-performance behavior models for fraud detection faces several huge challenges, e.g., ex-ante anti-fraud and new fraud attacks. To this end, we have designed two horizontal association modeling solutions: $$\bullet $$ ∙ We strive to design an ex-ante anti-fraud method that can work before unauthorized behaviors occur. The feasibility of our solution is supported by the cooperation of a characteristic and a finding in online payment fraud scenarios: The well-recognized characteristic is that online payment frauds are mostly caused by account compromise. Our finding is that account theft is indeed predictable based on users’ high-risk behaviors, without relying on the behaviors of thieves. Accordingly, we propose an account risk prediction scheme to realize the ex-ante fraud detection. It takes in an account’s historical transaction sequence, and outputs its risk score. The risk score is then used as an early evidence of whether a new transaction is fraudulent or not, before the occurrence of the new transaction. We examine our method on a real-world B2C transaction dataset from a commercial bank. Experimental results show that the ex-ante detection method can prevent more than $$80\%$$ 80 % of the fraudulent transactions before they actually occur. When the proposed method is combined with an interim detection to form a real-time anti-fraud system, it can detect more than $$94\%$$ 94 % of fraudulent transactions while maintaining a very low false alarm rate (less than $$0.1\%$$ 0.1 % ). $$\bullet $$ ∙ We pursue an adaptive learning approach to detect fraudulent online payment transactions with automatic sliding time windows. Accordingly, we make efforts on optimizing the setting of windows and improving the adaptability. We design an intelligent window, called learning automatic window (LAW). It utilizes the learning automata to learn the proper parameters of time windows and adjust them dynamically and regularly according to the variation and oscillation of fraudulent transaction patterns. By the experiments over a real-world dataset of the online payment service from a commercial bank, we validate the gain of LAW in terms of detection effectiveness and robustness. To the best of our knowledge, this is the first work to make a sliding time window for fraud detection capable of learning its proper size in changing situations.