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Generative Adversarial Network-Based Credit Card Fraud Detection

  • Xiaobo XieEmail author
  • Jian Xiong
  • Liguo Lu
  • Guan Gui
  • Jie Yang
  • Shangan Fan
  • Haibo Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)

Abstract

With the development of the financial industry, the number of credit cards has greatly increased. However, credit card fraud is still a major concern for financial security. Credit card fraud detection is a typical imbalanced classification problem, in which fraudulent cardholders are far less than non-fraudulent cardholders. The training on imbalanced samples will cause that the classifier ignores the minor fraudulent samples. To solve this problem, a generative adversarial network (GAN) based classification method is proposed for credit card fraud detection. GAN consists of a generative model and a discriminative model, and the two models are trained in a competitive way to get the Nash equilibrium. Specifically, the generative model tries to fit the real distribution of the non-fraudulent samples. The discriminative model determines the probability of a sample belongs to the distribution of non-fraudulent samples. To improve the discrimination performance, the fraudulent samples are also used in the training of discriminative model, which is different from the traditional GAN training scheme. The experimental results show that the recall reaches 82.7%.

Keywords

Fraud Imbalanced GAN Discriminative model 

Notes

Acknowledgements

This work was partially supported by National Natural Science Foundation of China Grants (No. 61701258), Natural Science Foundation of Jiangsu Province Grant (No. BK20170906), Natural Science Foundation of Jiangsu Higher Education Institutions Grant (No. 17KJB510044), Jiangsu Specially Appointed Professor Grant (RK002STP16001), and Innovation and Entrepreneurship of Jiangsu High-level Talent Grant (CZ0010617002).

References

  1. 1.
    Maloof, MA.: Learning when data sets are imbalanced and when costs are unequal and unknown. In: ICML-2003 Workshop on Learning from Imbalanced Data Sets II (2003)Google Scholar
  2. 2.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16(1), 321–357 (2002)CrossRefGoogle Scholar
  3. 3.
    Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsl. 6(1), 20–29 (2004)CrossRefGoogle Scholar
  4. 4.
    Hanif, A., Azhar, N.: Resolving class imbalance and feature selection in customer churn dataset. In: International Conference on Frontiers of Information Technology. IEEE, pp. 82–86 (2018)Google Scholar
  5. 5.
    Gaffer, S.M., Yahia, M.E., Ragab, K.: Genetic fuzzy system for intrusion detection: Analysis of improving of multiclass classification accuracy using KDDCup-99 imbalance dataset. In: International Conference on Hybrid Intelligent Systems. IEEE, pp. 318–323 (2013)Google Scholar
  6. 6.
    Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., et al.: Generative adversarial nets. In: International Conference on Neural Information Processing Systems. MIT Press, pp. 2672–2680 (2014)Google Scholar
  7. 7.
    Mirza, M., Osindero, S.: Conditional generative adversarial nets. Comput. Sci. 2672–2680 (2014)Google Scholar
  8. 8.
    Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. Comput. Sci. (2015)Google Scholar
  9. 9.
    Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks (2017)Google Scholar
  10. 10.
    Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Xiaobo Xie
    • 1
    Email author
  • Jian Xiong
    • 1
  • Liguo Lu
    • 1
  • Guan Gui
    • 1
  • Jie Yang
    • 1
  • Shangan Fan
    • 1
  • Haibo Li
    • 1
  1. 1.College of Telecommunication and Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina

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