Enhancing Sustainable Social Banking Performance through Artificial Intelligence: A System Dynamics Analysis of Iranian Cooperative Banks

Articles in Press

Document Type : Research Article

Authors

• ramin khoshchehreh mohammadi ¹
• Mehrdad Hosseini Shakib ²
• mahmood khodam ¹
• Ali Ramezani ¹

¹ Department of Industrial Management, Karaj Branch, Islamic Azad University, Karaj, Iran

² Department of Industrial Management , Karaj Branch, Islamic Azad University, Karaj, Iran.

10.22067/jstinp.2025.93087.1152

Abstract

With the expansion of innovative technologies, the banking industry has faced profound transformations. Artificial intelligence, as one of the most important of these technologies, has significant potential to change the nature of banking services; however, its impact on social banking, especially in cooperative banks, has received less attention. This research aims to study the influence of artificial intelligence functions on social banking performance in Iranian cooperative banks using a system dynamics approach. The study adopts a mixed approach (qualitative-quantitative). In the qualitative section, key variables were identified using an expert panel, and in the quantitative section, a system dynamics model was developed using Vensim software. The stock-flow model simulated the relationships between main variables including sustainable development, bank reputation, unpredictable liquidity, non-performing loans, and artificial intelligence infrastructure over a 10-year period (2021-2031). The results of sensitivity analysis and scenario development demonstrated that strategic investments in artificial intelligence infrastructure, enhanced data protection protocols, and improved financial transparency contribute significantly to enhanced bank reputation, substantially reduced unpredictable liquidity fluctuations, and notably decreased non-performing loans, thereby supporting sustainable banking operations. Model validation tests including boundary conditions test, structural test, uncertainty test, and integration test confirmed the accuracy of the relationships. This model can serve as a tool for decision-making and policy-making regarding the application of artificial intelligence in the country's social banking system.

Keywords

• Social Banking
• Artificial Intelligence
• System Dynamics
• Liquidity
• Cooperative Banks

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