Bank Fraud Detection with Graph Neural Networks

Bank Fraud Detection with Graph Neural Networks

— 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...

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Bibliographic Details
Published in:Automatic control and computer sciences Vol. 56; no. 8; pp. 865 - 873
Main Authors: Sergadeeva, A. I., Lavrova, D. S., Zegzhda, D. P.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01-12-2022
Springer Nature B.V
Subjects:
Computer Science
Control Structures and Microprogramming
Fraud
Graph neural networks
Graphical representations
information security
graph neural networks
anomaly detection
convolutional neural networks
bank fraud
financial data analysis
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Summary:— 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.
ISSN:0146-4116
1558-108X
DOI:10.3103/S0146411622080223