Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection

• URL: http://arxiv.org/abs/2101.04285v1
• Date: Tue, 12 Jan 2021 04:12:18 GMT
• Title: Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection
• Authors: Wei Min, Weiming Liang, Hang Yin, Zhurong Wang, Mei Li, Alok Lal
• Abstract summary: We propose a Deep learning based behavior data representation method for Clustering (FinDeepBehaviorCluster) to detect fraudulent transactions. To utilize the behavior sequence data, we treat click stream data as event sequence, use time attention based Bi-LSTM to learn the sequence embedding in an unsupervised fashion, and combine them with intuitive features generated by risk experts to form a hybrid feature representation. Our experimental results show that the proposed FinDeepBehaviorCluster framework is able to catch missed fraudulent transactions with considerable business values.
• Score: 3.9505606841402607
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In e-commerce industry, user behavior sequence data has been widely used in many business units such as search and merchandising to improve their products. However, it is rarely used in financial services not only due to its 3V characteristics - i.e. Volume, Velocity and Variety - but also due to its unstructured nature. In this paper, we propose a Financial Service scenario Deep learning based Behavior data representation method for Clustering (FinDeepBehaviorCluster) to detect fraudulent transactions. To utilize the behavior sequence data, we treat click stream data as event sequence, use time attention based Bi-LSTM to learn the sequence embedding in an unsupervised fashion, and combine them with intuitive features generated by risk experts to form a hybrid feature representation. We also propose a GPU powered HDBSCAN (pHDBSCAN) algorithm, which is an engineering optimization for the original HDBSCAN algorithm based on FAISS project, so that clustering can be carried out on hundreds of millions of transactions within a few minutes. The computation efficiency of the algorithm has increased 500 times compared with the original implementation, which makes flash fraud pattern detection feasible. Our experimental results show that the proposed FinDeepBehaviorCluster framework is able to catch missed fraudulent transactions with considerable business values. In addition, rule extraction method is applied to extract patterns from risky clusters using intuitive features, so that narrative descriptions can be attached to the risky clusters for case investigation, and unknown risk patterns can be mined for real-time fraud detection. In summary, FinDeepBehaviorCluster as a complementary risk management strategy to the existing real-time fraud detection engine, can further increase our fraud detection and proactive risk defense capabilities.

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