كنفرانس بين المللی مهندسی صنايع و سيستم ها

صفحه اصلی / دوازدهمین كنفرانس بين المللی مهندسی صنايع و سيستم ها

A Sustainable Framework for Evaluating the Attractiveness of AI in Banking: Insights from the Best-Worst Method

نویسندگان :

Seyed Amir Mirzaseyedi¹ Rohollah Ghasemi²

1- دانشگاه تهران - دانشکدگان مدیریت - دانشکده مدیریت صنعتی و فناوری 2- دانشگاه تهران - دانشکدگان مدیریت - دانشکده مدیریت صنعتی و فناوری

کلمات کلیدی :

Artificial intelligence،Sustainability،Technology Transformation،Best‑Worst Method،PRISMA-ScR،Banking Industry

چکیده :

This study develops an empirically weighted Triple Bottom Line (TBL) framework to evaluate AI attractiveness in banking, addressing fragmented literature and providing a practical decision-support tool for AI investments and governance. A PRISMA-ScR scoping review of 207 peer-reviewed articles identified 13 criteria across economic, social, and environmental dimensions, which were subsequently validated by an independent panel of banking and AI experts. Using the Best-Worst Method (BWM) with six multidisciplinary experts, the criteria were prioritized, demonstrating high reliability (CR<0.10) and strong expert consensus (Kendall's W=0.83). Results reveal that economic criteria dominate with 68.8% weight, led by Operational Cost Reduction (38.7%), followed by Risk and Fraud Mitigation (16.1%) and ROI (9.5%). Social criteria (22.6%) are primarily driven by Data and Account Security (9.5%), while the environmental dimension (8.7%) is dominated by Data Center Efficiency (5.8%). The findings confirm that AI value creation remains predominantly economic, anchored in cost efficiency and risk control, though social aspects are well-established and environmental factors are emergent. Proactive attention to environmental priorities can offer banks a strategic competitive advantage as regulatory and technological pressures intensify.