Article: Adaptive identification and warning method for financial fraud behaviour based on deep Q-learning Journal: International Journal of Intelligent Systems Technologies and Applications (IJISTA) 2025 Vol.23 No.1/2 pp.103 - 115 Abstract: In order to improve the accuracy of identifying financial fraud and reduce the false alarm rate, a new adaptive identification and warning method for financial fraud based on deep Q-learning is proposed. Firstly, analyse the extraction of financial behaviour characteristics, including transaction funds, networks, cycles, and supervised features. Secondly, deep Q-learning combines deep neural networks and reinforcement learning to automatically extract key features from financial transaction data, and continuously optimise strategies through interaction with the environment to achieve accurate identification of financial fraud behaviour. Finally, based on the deep Q-learning model, financial fraud behaviour is assessed for risk and classified into warning levels, and corresponding warning response strategies are implemented according to different risk levels. The experimental results show that the adaptive identification accuracy of financial fraud behaviour using our method can reach up to 98.9%, with a maximum false alarm rate of around 1%. Inderscience Publishers - linking academia, business and industry through research

Title: Adaptive identification and warning method for financial fraud behaviour based on deep Q-learning

Authors: Qin Wang; Wei Qing Diao; Fei Ren; Yi Heng Zhang; Mary Jane C. Samonte

Addresses: School of Tourism and Financial Media, Xi'an Siyuan University, Xi'an Shaanxi, 710038, China; School of Information Technology, Mapua University, Manila, Philippines ' School of Tourism and Financial Media, Xi'an Siyuan University, Xi'an Shaanxi, 710038, China ' Nanjing Supercontrol Electronic Technology Co., Ltd., Nanjing Jiangsu, 210000, China ' Academic Affairs Office, Xi'an Siyuan University, Xi'an Shaanxi, 710038, China ' School of Information Technology, Mapua University, Manila, Philippines

Abstract: In order to improve the accuracy of identifying financial fraud and reduce the false alarm rate, a new adaptive identification and warning method for financial fraud based on deep Q-learning is proposed. Firstly, analyse the extraction of financial behaviour characteristics, including transaction funds, networks, cycles, and supervised features. Secondly, deep Q-learning combines deep neural networks and reinforcement learning to automatically extract key features from financial transaction data, and continuously optimise strategies through interaction with the environment to achieve accurate identification of financial fraud behaviour. Finally, based on the deep Q-learning model, financial fraud behaviour is assessed for risk and classified into warning levels, and corresponding warning response strategies are implemented according to different risk levels. The experimental results show that the adaptive identification accuracy of financial fraud behaviour using our method can reach up to 98.9%, with a maximum false alarm rate of around 1%.

Keywords: deep Q-learning; financial fraud behaviour; adaptive discrimination; warning methods.

DOI: 10.1504/IJISTA.2025.145630

International Journal of Intelligent Systems Technologies and Applications, 2025 Vol.23 No.1/2, pp.103 - 115

Received: 01 Aug 2024
Accepted: 24 Sep 2024
Published online: 09 Apr 2025 *

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Title: Adaptive identification and warning method for financial fraud behaviour based on deep Q-learning

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