Designing an Interpretive Structural Model (ISM) for Financial Strategies of the Social Security Organization Using Artificial Intelligence
Keywords:
Financial strategies, Artificial intelligence, Social Security Organization, Interpretive Structural Modeling (ISM), Financial transparencyAbstract
This study aimed to design an Interpretive Structural Model (ISM) for the financial strategies of the Social Security Organization using artificial intelligence to enhance financial efficiency, transparency, and agility. The research is exploratory in purpose and qualitative in approach. Data were collected through semi-structured interviews with 17 financial and AI experts selected via snowball sampling. Thematic analysis was employed to extract key themes, followed by the ISM technique to determine hierarchical relationships among them. Thematic analysis revealed 1 overarching theme, 7 organizing themes, 14 basic themes, and 66 initial codes. The ISM results identified financial justice and sustainability strategies as the foundational level, forming the basis for other strategies. The intermediate levels comprised financial transparency and auditing, intelligent budgeting and resource allocation, financial risk management, and financial diversification. The top level included financial productivity strategies as the ultimate outcome of the system. The model demonstrated that AI, through data mining, machine learning, and optimization algorithms, enhances resource allocation, forecasting accuracy, and risk mitigation within financial management. The proposed ISM provides a systematic framework for integrating artificial intelligence into the financial strategies of the Social Security Organization. It enables data-driven, transparent, and sustainable financial decision-making, strengthening efficiency, public trust, and economic resilience.
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Copyright (c) 2025 Yaser Jafaiean, Reza Seyedkhani, Mojtaba Moradpour, Rahmatollah Mohammadipour (Author)
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