FinTech and Artificial Intelligence in Relationship Banking and Computer Technology

Introduction

Banks cannot afford to be complacent in their operations. Due to the dramatic alterations caused by improvements in computing technology as well as competing demands from FinTech businesses, they should re-assess their competing benefits. As a result of technological advances in big data analytics, social networks, telecommunications and AI, societies are on the cusp of undergoing a profound shift. Knowing banking in these turbulent times is a difficult undertaking. It is a contention that the economics of banking has not altered. The purpose of banks has always been, and will remain, the reduction of asymmetric facts between lenders and depositors (Lai et al., 2019). When estimating the trustworthiness of bank customers, banks must analyse minimal data, which is often difficult to quantify, due to the fact that their motivations may be fundamentally mismatched with those of the bank. Relationship banking, build intimate links with the clients via continuing collaboration must not be regarded as outmoded in an environment characterised by information asymmetries. A relationship bank lessens information asymmetries via the intensive possession of soft information—information that is challenging to measure, accumulate, and communicate in unbiased manner (Ogura, Y. 2014)—that is private in essence, generally all through the long-term relationship including its clients (Moro, S. et. al., 2015). A relationship bank's ability to respond on soft information is based on its considerable flexibility and discretion, as well as its reliance on secrecy and trust.

As a result of IT-driven advancements, however, relationship banking is facing significant problems that must be addressed and highlighted by (Backhaus, K. et al.,2019). The initial possible issue of relationship banking is that it is less efficient than transaction-driven technology when compared to traditional banking. IT advancements have boosted effectiveness in bank transaction (particularly in transactions, payment and settlement, transactional lending and online banking), hence altering the duties of isolations in the financial services industry. With artificial intelligence computers doing transaction banking operations, a slew of issues have been raised about the role of human financiers, who are critical to the development of long-term relationships with customers. Human beings are still required in banks, but bankers must rethink their position in the industry. Understanding people's thoughts and actions is becoming more crucial as time goes on. Banks must be aware of behavioural biases, herding behaviour, limited rationality, and people's emotional states in order to operate effectively. Because information travels swiftly across social networks, society is more susceptible to herding, information manipulation, and baseless panics than it has ever been. Perfect reason, in this context, may seem to be an unduly straightforward idea. The growth of information technology has increased competition in banking, which has had an impact on the strategic decision of banks between relationship banking and transactional banking. When it comes to transaction banking, scaling up is simple, which makes leveraging relationships a problem. We believe that information technology is becoming more conducive to interpersonal connections.

For example, some FinTech businesses have scaled their relationship banking technologies to great success. Melese, F. (Melese, F. 2021) explain the “hold-up issue”, which is a downside of relationship banking in which relationship banks leverage private information for personal gain and make borrowers unduly reliant on them. Competition then works to alleviate this disadvantage. The implications of these approaches for stability are discussed. Risks arising from information technology-based developments in banking must be thoroughly investigated in order to avoid a detrimental influence on bank stability.