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Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection by Min, Wei; Liang, Weiming; Yin, Hang; Wang, Zhurong; Li, Mei; Lal, Alok is a scholarly article available to read on EtoBox.

What is Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection about?

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 detect

Author

Min, Wei; Liang, Weiming; Yin, Hang; Wang, Zhurong; Li, Mei; Lal, Alok

Published

2021

Language

EN