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