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The transactional activity of a bank’s customers contains a wealth of information regarding their behavior. By examining the transactions of either a specific group or all clients of the bank, it is possible to gain insight into the macroeconomic environment and utilize this to anticipate various outcomes. In this paper, we proposed a dynamic behavioral model for bank clients based on econophysics principles. Additionally, we identified the non-homogeneous function in the dynamic equation using physics-informed neural network. We also interpreted this non-homogeneity through the lens of news reports from social media platforms and news agencies. The model demonstrated accurate results in a numerical simulation of the restoration of the initial dependency. We also showed the potential for creating scenarios in which news events impact the behavior of bank customers.

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A Dynamic Model of Customers Behavior: Integrating Econophysics and Physics-Informed Neural Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Kirill Zakharov,
  • Anton Kovantsev,
  • Alexander Boukhanovsky

摘要

The transactional activity of a bank’s customers contains a wealth of information regarding their behavior. By examining the transactions of either a specific group or all clients of the bank, it is possible to gain insight into the macroeconomic environment and utilize this to anticipate various outcomes. In this paper, we proposed a dynamic behavioral model for bank clients based on econophysics principles. Additionally, we identified the non-homogeneous function in the dynamic equation using physics-informed neural network. We also interpreted this non-homogeneity through the lens of news reports from social media platforms and news agencies. The model demonstrated accurate results in a numerical simulation of the restoration of the initial dependency. We also showed the potential for creating scenarios in which news events impact the behavior of bank customers.