International Journal of Finance & Managerial Accounting

International Journal of Finance & Managerial Accounting

A Hybrid Neural Network based Model for Liquidity Management in Bank Branches

Document Type : Original Article

Authors
1 Accounting Ph.D student, Department of acconting, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 Associate Professor Accounting, Qazvin Branch, IslaAssociate professor of QIAU, Department of accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iranmic Azad University, Qazvin, Iran.
10.30495/ijfma.2023.73872.2029
Abstract
Abstract
In this article, the impact of modern machine learning technologies on optimizing banks' liquidity is investigated. Additionally, the daily financial information of 32 bank branches within a provincial network has been extracted and analyzed for two years using general ledgers. First, the data is transferred to Jupyter Notebook software using Python programming language for machine learning. The liquidity requirements of the branches in the next few days are predicted, and the accuracy of the estimates is evaluated. In the final stage, optimization is performed through surplus reassignment using the neural network method. The experimental results demonstrate the effectiveness of the proposed model in optimal liquidity management and minimizing the demands of bank branches through the internal financing of the branch network. Moreover, the likelihood percentage of liquidity demand of branches is 95% on average, which is acceptable and more favorable than other similar studies.
risk appetite, Artificial Neural Networks, optimization, liquidity management, artificial intelligence
Keywords

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