Raiha Imran, Kifayat Ullah, Zeeshan Ali, Yilun Shang, Tapan Senapati
In the rapidly changing world of banking systems, intelligent banking has emerged as a revolutionary force in the rapidly growing financial technology world. Smart banking provides a seamless, personalized experience, leveraging intelligent systems that apply technologies such as artificial intelligence (AI), blockchain, and big data to deliver mobile financial services. However, the risks of such innovations can also be confusing, such as cyber threats, data breaches, and vulnerabilities in work, and these aspects require strict assessment and control. This article presents a systematic multi-criteria group decision-making (MCGDM) model for risk assessment in intelligent banking, which incorporates the Circular Intuitionistic Fuzzy Bonferroni Mean (CIFBM) to assess the state of expert judgments and effectively manage uncertainty and ambiguity. Moreover, important theorems and properties, such as idempotency, monotonicity, and boundedness, have ensured its efficiency and reliability in risk assessment. A case study shows that the proposed approach has real-world applicability and can be used to prioritize risks, make decisions, and improve the security, efficiency, and flexibility of banking operations. This work provides a new, practical framework for addressing emerging risks in intelligent banking systems. It makes a valuable contribution to researchers and practitioners in the field of financial technology by combining rigorous theoretical development with practical implementation.