How Traditional Financial Institutions have adapted to Artificial Intelligence, Machine Learning and FinTech?

Sharbek Nermin ()
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Sharbek Nermin: Bucharest University of Economic Studies, Bucharest, Romania

Proceedings of the International Conference on Business Excellence, 2022, vol. 16, issue 1, 837-848

Abstract: This article analyzes the implications of the financial sector’s recent adoption of artificial intelligence (AI) and machine learning (ML). It identifies the advantages of these technologies in terms of fraud protection, cost savings, and efficiency, while also highlighting worries about conventional financial institutions’, such as banks, insurance and reinsurance institutions, and inability to compete with Fintech firms. The report contributes to the conversation about the influence of modern technology by distilling and classifying the tools deployed in established conventional institutions that resulted in continual development and simplification of internal procedures as well as client service delivery. Given the increased competition in the financial industry, Fintech businesses are critical for conventional financial institutions to stay afloat in today’s changing world. The study is directed at researchers who are still in the early phases of investigating the artificial intelligence field in the financial sector. The study reviews prior research that documents the changes occurring inside financial institutions from a global perspective as a result of Fintech and the integration of new technologies. The goals of the paper are fulfilled by (1) furthering theoretical studies on the issue, (2) increasing awareness of the financial industry’s developments, and (3) gathering proof of the influence artificial intelligence and machine learning have had so far.

Keywords: Financial services; Artificial intelligence; Machine Learning; Fintech; Banks; Financial institutions; Insurance (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:16:y:2022:i:1:p:837-848:n:28

DOI: 10.2478/picbe-2022-0078

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