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Automatic Control and Computer Sciences

Erschienen in:

01.12.2022

Bank Fraud Detection with Graph Neural Networks

verfasst von: A. I. Sergadeeva, D. S. Lavrova, D. P. Zegzhda

Erschienen in: Automatic Control and Computer Sciences | Ausgabe 8/2022

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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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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.2008.2005605CrossRef

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.2021.01.001CrossRef

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

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.2019.05.042MathSciNetCrossRef

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.

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

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

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

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/S0146411620080258CrossRef

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/S0146411621080150CrossRef

Titel: Bank Fraud Detection with Graph Neural Networks
verfasst von: A. I. Sergadeeva
D. S. Lavrova
D. P. Zegzhda
Publikationsdatum: 01.12.2022
Verlag: Pleiades Publishing
Erschienen in: Automatic Control and Computer Sciences / Ausgabe 8/2022
Print ISSN: 0146-4116
Elektronische ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411622080223

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