A Process-Oriented Policy Model for AI Development in Iran's FinTech Industry

Document Type : Original Article

Authors

1 Ph.D. in Information Technology Management, Faculty of Industrial and Technology Management, University of Tehran, Iran.

2 Professor, Faculty of Industrial and Technology Management, School of Management, University of Tehran, Iran

3 Associate Professor, Faculty of Management and Accounting, Farabi School, University of Tehran, Iran

4 Associate Professor, Faculty of Public Administration and Organizational Sciences, School of Management, University of Tehran, Iran.

10.22034/jstp.2025.12014.1912

Abstract

Financial technology (FinTech) has emerged as a pioneering industry in digital transformation by leveraging artificial intelligence to create substantial competitive advantages, including enhanced intelligence capabilities, big data utilization, and improved operational performance. However, the expanding application of AI in this sector has simultaneously introduced numerous financial, legal, ethical, and social challenges for businesses, consumers, and regulatory institutions, highlighting the critical need for systematic policymaking in this domain. This research aims to develop a process-oriented policy model for AI implementation within the FinTech industry. The study adopts a meta-model framework for AI development in FinTech and employs a case study methodology focusing on Iran's FinTech sector. The research methodology involved examining 24 distinct cases, comprising 6 relevant documentary sources and 18 in-depth interviews. To ensure methodological rigor, the interview process followed the SPICKARD protocol and consisted of semi-structured, intensive discussions with both AI policy experts and FinTech industry practitioners, continuing until theoretical saturation was achieved. Snowball sampling was utilized for participant selection, while data analysis was conducted through thematic analysis methods. Textual interpretation followed Rabiee and Gillham's eight-stage approach, with thematic analysis performed using Attride-Stirling's thematic network framework. The study ultimately proposes a comprehensive AI development policy model for the FinTech industry, structured around five core phases: investigation and identification, proposal and analysis, policy adoption, policy implementation, and evaluation and revision, encompassing a total of 22 specific stages. Furthermore, the research elucidates strategic and operational considerations derived from thematic analysis for each phase, providing valuable insights to enhance the model's implementation quality and effectiveness. The findings offer significant contributions to both academic discourse and practical policymaking in the evolving FinTech landscape.
 

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References

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