Credit Card Fraud Detection Using Convolutional Neural Networks

  • Kang Fu
  • Dawei Cheng
  • Yi Tu
  • Liqing ZhangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9949)


Credit card is becoming more and more popular in financial transactions, at the same time frauds are also increasing. Conventional methods use rule-based expert systems to detect fraud behaviors, neglecting diverse situations, extreme imbalance of positive and negative samples. In this paper, we propose a CNN-based fraud detection framework, to capture the intrinsic patterns of fraud behaviors learned from labeled data. Abundant transaction data is represented by a feature matrix, on which a convolutional neural network is applied to identify a set of latent patterns for each sample. Experiments on real-world massive transactions of a major commercial bank demonstrate its superior performance compared with some state-of-the-art methods.


Credit card fraud Convolutional neural network Imbalanced data 



The work was supported by the National Natural Science Foundation of China (61272251), the Key Basic Research Program of Shanghai Municipality, China (15JC1400103) and the National Natural Science Foundation of China (91420302).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

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