CFTNet: a robust credit card fraud detection model enhanced by counterfactual data augmentation

Menglin Kong, Ruichen Li, Jia Wang, Xingquan Li, Shengzhong Jin, Wanying Xie, Muzhou Hou, Cong Cao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Establishing a reliable credit card fraud detection model has become a primary focus for academia and the financial industry. The existing anti-fraud methods face challenges related to low recall rates, inaccurate results, and insufficient causal modeling ability. This paper proposes a credit card fraud detection model based on counterfactual data enhancement of the triplet network. Firstly, we convert the problem of generating optimal counterfactual explanations (CFs) into a policy optimization of agents in the discrete–continuous mixed action space, thereby ensuring the stable generation of optimal CFs. The triplet network then utilizes the feature similarity and label difference of positive example samples and CFs to enhance the learning of the causal relationship between features and labels. Experimental results demonstrate that the proposed method improves the accuracy and robustness of the credit card fraud detection model, outperforming existing methods. The research outcomes are of significant value for both credit card anti-fraud research and practice while providing a novel approach to causal modeling issues across other fields.

Original languageEnglish
Pages (from-to)8607-8623
Number of pages17
JournalNeural Computing and Applications
Volume36
Issue number15
DOIs
Publication statusPublished - May 2024

Keywords

  • Counterfactual data augmentation
  • Credit card fraud detection
  • Deep neural network
  • Reinforcement learning

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