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

With the expansion of innovative technologies, the banking industry has faced profound transformations. Artificial intelligence, as one of the most significant of these technologies, has the potential to transform the nature of banking services; however, its impact on social banking, particularly in cooperative banks, has received less attention. This research aims to investigate the impact of artificial intelligence functions on the performance of social banking in Iranian cooperative banks, utilizing 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 10 years (2021-2031). The results of the sensitivity analysis and scenario development demonstrated that strategic investments in artificial intelligence infrastructure, enhanced data protection protocols, and improved financial transparency contribute significantly to an enhanced bank reputation, substantially reduce unpredictable liquidity fluctuations, and notably decrease non-performing loans, thereby supporting sustainable banking operations. Model validation tests, including boundary conditions tests, structural tests, uncertainty tests, and integration tests, 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.