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Bank Fraud Detection with Graph Neural Networks

Automatic Control and Computer Sciences volume 56, pages 865–873 (2022)Cite this article

Abstract—

This study proposes a method for detecting bank fraud based on graph neural networks. Financial transactions are represented in the form of a graph and analyzed with a graph neural network with the goal of detecting transactions typical of fraud schemes. The results of experimental tests indicate the high potential of the proposed approach.

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REFERENCES

  1. Scarselli, F., Gori, M., Tsoi, A.Ch., Hagenbuchner, M., and Monfardini, G., The graph neural network model, IEEE Trans. Neural Networks, 2008, vol. 20, no. 1, pp. 61–80. https://doi.org/10.1109/TNN.2008.2005605

    Article  Google Scholar 

  2. Zhou, J., Cui, G., Hu, Sh., Zhang, Zh., Yang, Ch., Liu, Zh., Wang, L., Li, Ch., and Sun, M., Graph neural networks: A review of methods and applications, AI Open, 2020, vol. 1, pp. 57–81. https://doi.org/10.1016/j.aiopen.2021.01.001

    Article  Google Scholar 

  3. Khamsi, M.A. and Kirk, W.A., An Introduction to Metric Spaces and Fixed Point Theory, John Wiley & Sons, 2011. https://doi.org/10.1002/9781118033074

    Book  MATH  Google Scholar 

  4. Carcillo, F., Le Borgne, Ya.-A., Caelen, O., Kessaci, Ya., Oblé, F., and Bontempi, G., Combining unsupervised and supervised learning in credit card fraud detection, Inf. Sci., 2021, vol. 557, pp. 317–331. https://doi.org/10.1016/j.ins.2019.05.042

    Article  MathSciNet  Google Scholar 

  5. Save, P., Tiwarekar, P., Jain, K.N., and Mahyavanshi, N., A novel idea for credit card fraud detection using decision tree, Int. J. Comput. Appl., 2017, vol. 161, no. 13, pp. 6–9.

    Google Scholar 

  6. Gyanfi, N.K. and Abdulai, J.-D., Bank fraud detection using support vector machine, IEEE 9th Annu. Information Technology, Electronics and Mobile Communication Conf. (IEMCON), Vancouver, 2018, IEEE, 2018, pp. 37–41. https://doi.org/10.1109/IEMCON.2018.8614994

  7. Xuan, Sh., Liu, G., Li, Zh., Zheng, L., Wang, Sh., and Jiang, Ch., Random forest for credit card fraud detection, IEEE 15th Int. Conf. on Networking, Sensing and Control (ICNSC), Zhuhai, China, 2018, IEEE, 2018, pp. 1–6. https://doi.org/10.1109/ICNSC.2018.8361343

  8. Lei, Sh., Xu, Ke, Huang, Yi., and Sha, X., An Xgboost based system for financial fraud detection, E3S Web Conf., 2020, vol. 214, p. 02042.  https://doi.org/10.1051/e3sconf/202021402042

  9. Zou, J., Zhang, J., and Jiang, P., Credit card fraud detection using autoencoder neural network, 2019. arXi-v:1908.11553 [cs.LG]

  10. Zhong, Q., Liu, Ya., Ao, X., Hu, B., Feng, J., Tang, J., and He, Q., Financial defaulter detection on online credit payment via multi-view attributed heterogeneous information network, WWW ’20: Proc. The Web Conf. 2020, Taipei, Taiwan, 2020, New York: Association for Computing Machinery, 2020, pp. 785–795.  https://doi.org/10.1145/3366423.3380159

  11. Dou, Yi., Liu, Zh., Sun, L., Deng, Yu., Peng, H., and Yu, P.S., Enhancing graph neural network-based fraud detectors against camouflaged fraudsters, CIKM ’20: Proc.29th ACM Int. Conf. on Information & Knowledge Management, 2020, New York: Association for Computing Machinery, 2020, pp. 315–324.  https://doi.org/10.1145/3340531.3411903

  12. Pavlenko, E.Yu. and Kudinov, K.V., Ensuring cyber resilience of large-scale network infrastructure using the ant algorithm, Autom. Control Comput. Sci., 2020, vol. 54, no. 8, pp. 793–802. https://doi.org/10.3103/S0146411620080258

    Article  Google Scholar 

  13. Pavlenko, E. and Poltavtseva, M., Mathematical methods for implementing homeostatic control in digital production systems, Technological Transformation: A New Role for Human, Machines and Management. TT 2020, Schaumburg, H., Korablev, V., and Ungvari, L., Eds., Lecture Notes in Networks and Systems, vol. 157, Cham: Springer, 2021, pp. 1–9. https://doi.org/10.1007/978-3-030-64430-7_1

  14. Cho, H., Andreev, A., Kalinin, M., Moskvin, D., and Zegzhda, D., Mismatch-resistant intrusion detection with bioinspired suffix tree algorithm, Algorithms and Solutions Based on Computer Technology, Jahn, C., Ungvári, L., and Ilin, I., Eds., Lecture Notes in Networks and Systems, vol. 387, Cham: Springer, 2022, pp. 1–16.  https://doi.org/10.1007/978-3-030-93872-7_1

  15. Kalinin, M. and Krundyshev, V., Computational intelligence technologies stack for protecting the critical digital infrastructures against security intrusions, Fifth World Conf. on Smart Trends in Systems Security and Sustainability (WorldS4), London, 2021, IEEE, 2021, pp. 118–122. https://doi.org/10.1109/WorldS451998.2021.9514004

  16. Kalinin, M.O., Application of neuro-fuzzy inference for network scanning detection, Autom. Control Comput. Sci., 2021, vol. 55, no. 8, pp. 908–917. https://doi.org/10.3103/S0146411621080150

    Article  Google Scholar 

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ACKNOWLEDGMENTS

Project results are achieved using the resources of supercomputer center of Peter the Great St.Petersburg Polytechnic University—SCC Polytechnichesky (http://www.spbstu.ru).

Funding

The research is funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program “Priority 2030” (agreement 075-15-2021-1333 dated November 30, 2021).

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    A. I. Sergadeeva, D. S. Lavrova & D. P. Zegzhda

Authors
  1. A. I. Sergadeeva
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  2. D. S. Lavrova
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  3. D. P. Zegzhda
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Corresponding author

Correspondence to D. S. Lavrova.

Additional information

Translated by A. Ovchinnikova

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Sergadeeva, A.I., Lavrova, D.S. & Zegzhda, D.P. Bank Fraud Detection with Graph Neural Networks. Aut. Control Comp. Sci. 56, 865–873 (2022). https://doi.org/10.3103/S0146411622080223

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  • DOI: https://doi.org/10.3103/S0146411622080223

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