This study aims to identify and prioritize the applications of Artificial Intelligence (AI) in financial technologies (FinTech), highlighting the benefits for FinTech companies.
An exploratory mixed-method approach was utilized. The qualitative phase involved reviewing literature on AI applications in FinTech and conducting semi-structured interviews with experts to identify and categorize 66 AI applications across various financial industries, such as payments (PayTech), insurance (InsurTech), lending (LendTech), banking (BankTech), investment (WealthTech), and cryptocurrencies. These applications were coded using MAXQDA software. The quantitative phase involved two sections. First, the Delphi method for approving the identified AI applications from the qualitative phase, and then prioritizing the approved applications through a questionnaire and the Best-Worst Method (BWM) technique.
The results indicated that fraud detection, identity verification, risk assessment, loss prediction, stock forecasting, trend prediction, theft and fraud prevention, security enhancement, validation, automation of banking processes, and reduction of service costs were identified as the top priority applications across six selected financial industries.
This study contributes to the field by offering a comprehensive prioritization of AI applications in financial technologies, based on a combination of qualitative and quantitative research methods. The findings provide valuable insights for FinTech companies seeking to enhance their efficiency and profitability through AI integration.
