A methodology for determining the factors that increase artificial intelligence usage in mobile banking applications

Abstract

Recent developments in digital transformation (DT) and artificial intelligence transformation (AIT) are pushing businesses toward becoming intelligent enterprises. The banking is one of the affected businesses from this trend: DT and AIT continue to change the nature of banking. The interaction of customers who use mobile banking channels is now six time greater than customers that prefer traditional banking channels. Nevertheless, the use of artificial intelligence (AI) in mobile banking applications in Turkey is not at the desired level. Present AI technologies rely on the availability of right and relevant data. Therefore, the main aim of this study is to to determine the factors affecting the use rate of AI in mobile banking in Turkey. Additional objectives are as follows: to determine the use rate of mobile application and AI in mobile banking in terms of customer demographics; and to determine the most common queries and actions via smart assistant in the bank. Analytical Hierarchy Process (AHP) approach was utilized to examine the most influential factors affecting the use of AI in mobile banking. Six main criteria and seventeen sub-criteria are determined to construct a hierarchical model. The data collected through a developed questionnaire from a sample of users are analyzed to prioritize the criteria. Based on the AHP solution, main criteria for digital experts in descending order of importance are response (output) quality (RQ), user experience (UX), performance (P), security (S), bank brand perception (BBP) and personal factors (PF); for mobile banking and AI users are S, RQ, UX, P, PF and BBP; for only mobile banking users are S, UX, PF, RQ, P and BBP; and for all sample are S, RQ, UX, P, PF and BBP which is the same order as mobile banking and AI users. The developed strategic guide may aid to the bank decision-makers to prioritize their strategic goals and invest their resources wisely in AI. It is also hoped that the study will contribute to the development of more efficient AI products in banking and related fields.