Advancements in finance technology (FinTech) and Artificial Intelligence (AI) have erupted in the banking industry in the last few years (Gomber et al., 259 ). Furthermore, there has been an increase in the academic literature on the utilization of AI and FinTech in the banking industry. This has been escalated by various factors including the changes in consumer behavior, the digitalization of banking service, and the increasing competition in the industry. The current consumer behavior and the existing banking habits such as the protechnology attitude have facilitated the rapid advancement of the disruptive revolution of AI and FinTech. Banking institutions have been adopting the technologies to cater for a variety of reasons that effect the consumers and the institutions, including the increasing need to assimilate effective risk management processes and the need to utilize effective anti-fraud systems (Rohit & Patel, 129). The existing researches tend to be insufficiently connected, providing inadequate information on the effects of the two technological advancements to the consumers and the banking institutions. Therefore, significant research questions and gaps remain insufficiently defined. This paper provides a consistent study examining the factors that drive banking institutions to adopt FinTech and AI and their effects on the traditional banking processes, both to the consumers and the institutions. It utilizes a variety of literature to validate statements by providing agreeing and contrasting information based on researches. Through a qualitative semi-structured interview with two professionals and two consumers in the industry, the paper analyzes the importance of FinTech and AI in the institution and also shed light on its associated concerns.
Gomber, Peter, et al. "On the fintech revolution: interpreting the forces of innovation, disruption, and transformation in financial services." Journal of Management Information Systems 35.1 (2018): 220-265.
Rohit, Kamlesh D., and Dharmesh B. Patel. "Review on detection of suspicious transaction in anti-money laundering using data mining framework." International Journal for Innovative Research in Science and Technology 1.8 (2015): 129-133.