Providing a Model for Assessing the Implementation and Deployment of Artificial Intelligence in the Banking Industry
Keywords:
Artificial Intelligence, Assessment, Implementation, Banking IndustryAbstract
Artificial intelligence (AI) technology, through its self-learning algorithms, has played a significant role in enhancing banking processes. By reducing human resource costs, analyzing data, and identifying patterns, this technology has assisted policymakers in achieving organizational objectives. Despite these advantages, a precise evaluation and the provision of adequate infrastructure prior to its implementation in banks, particularly in developing countries, is essential. In Iranian banks, due to infrastructural weaknesses, the successful execution of AI has been challenging, necessitating technological and organizational preparations to minimize issues of incompatibility and rising costs. This study employs a mixed-methods (quantitative and qualitative) research design. The expert community for the qualitative phase consisted of 18 individuals selected via purposive snowball sampling. For the quantitative phase, involving factor analysis, a sample of 342 specialists was selected using Cochran's sampling formula. Questionnaire reliability for the factor analysis was assessed using Cronbach's alpha coefficient and SPSS software. In the qualitative phase, 34 factors were initially extracted from prior literature using a content analysis approach. These were then refined to 27 factors through a three-round Fuzzy Delphi process based on expert opinion. Subsequently, these confirmed factors were structured into indicators and sub-indicators using Exploratory Factor Analysis (EFA) with specialist input. Finally, Confirmatory Factor Analysis (CFA) was applied to formulate structural equations and validate the final model. The indicators, ranked by order of importance based on standardized coefficients, are: Data Management and Infrastructure (0.90), AI Systems and Algorithms (0.84), Customer Experience and Interaction (0.76), Security and Risk Management (0.73), and Business Process and Optimization (0.53).
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Copyright (c) 2024 Leila Mollaei , Seyyed Mohammad Ali Khatami Firoozabadi, Kiamars Fathi Hafshejani , Mahnaz Rabiei (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
