Learning Transactional Behavioral Representations for Credit Card Fraud Detection

Document Type

Article

Publication Date

4-1-2024

Abstract

Credit card fraud detection is a challenging task since fraudulent actions are hidden in massive legitimate behaviors. This work aims to learn a new representation for each transaction record based on the historical transactions of users in order to capture fraudulent patterns accurately and, thus, automatically detect a fraudulent transaction. We propose a novel model by improving long short-term memory with a time-aware gate that can capture the behavioral changes caused by consecutive transactions of users. A current-historical attention module is designed to build up connections between current and historical transactional behaviors, which enables the model to capture behavioral periodicity. An interaction module is designed to learn comprehensive and rational behavioral representations. To validate the effectiveness of the learned behavioral representations, experiments are conducted on a large real-world transaction dataset provided to us by a financial company in China, as well as a public dataset. Experimental results and the visualization of the learned representations illustrate that our method delivers a clear distinction between legitimate behaviors and fraudulent ones, and achieves better fraud detection performance compared with the state-of-the-art methods.

Identifier

85139821741 (Scopus)

Publication Title

IEEE Transactions on Neural Networks and Learning Systems

External Full Text Location

https://doi.org/10.1109/TNNLS.2022.3208967

e-ISSN

21622388

ISSN

2162237X

PubMed ID

36197863

First Page

5735

Last Page

5748

Issue

4

Volume

35

Grant

2018YFB2100801

Fund Ref

National Key Research and Development Program of China

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