Title: Modelling barriers of artificial intelligence in banking sectors using total interpretive structural modelling
Authors: Aditi Singhal; Praveen Dube; Vijay Kumar Jain
Addresses: School of Liberal Arts and Management, DIT University, Dehradun, Uttarakhand, India ' School of Liberal Arts and Management, DIT University, Dehradun, Uttarakhand, India ' School of Liberal Arts and Management, DIT University, Dehradun, Uttarakhand, India
Abstract: The banking industry is changing at a fast pace. To keep pace with the ever-changing industry, banks are required to embrace technological advancements. One such technological advancement is artificial intelligence. The banking industry is one of the first adopters of artificial intelligence. Implementation of artificial intelligence has advantages like fraud deduction, enhanced decision-making, enhanced customer experience, etc. and disadvantages like lack of data privacy, employee resistance, unemployment, etc. To realise the full benefit of artificial intelligence, it is a prerequisite for banks to remove barriers. The objective of the present research is to identify and establish relationships among these barriers using total interpretive structural modelling (TISM). The study's result states that lack of talent and qualified workers, regulation policies, data availability and quality and awareness are the critical barriers of artificial intelligence as they lie at the base of the TISM hierarchy. These barriers are also present in the third and fourth clusters of MICMAC analysis signifying these are critical barriers.
Keywords: artificial intelligence; data privacy; total interpretive structural modelling approach; cyber security.
International Journal of Applied Decision Sciences, 2022 Vol.15 No.3, pp.311 - 335
Received: 04 Jan 2021
Accepted: 23 Feb 2021
Published online: 04 May 2022 *