Exploring The Impact of Machine Learning on Financial Decision-Making in The Nigerian Banking Sector

Anthony Adepetun1, Olayinka Odutola2 & Elizabeth Modupe Dopemu3
Independent Researcher
DOI – http://doi.org/10.37502/IJSMR.2022.51215

Abstract

This study examines the impact of machine learning on decision-making in Nigeria’s banking sector, highlighting its potential to enhance credit risk evaluation, fraud detection, personalized banking, and predictive analytics. However, challenges like data quality, privacy, and regulatory compliance are significant barriers. The research applies the Technology Acceptance Model (TAM) and Diffusion of Innovation Theory (DOI) to understand adoption drivers, focusing on perceived usefulness and compatibility.

The findings suggest that Nigerian banks can improve decision-making and customer experiences by effectively adopting machine learning. However, they must address challenges like bias and operational inefficiencies. Strong data governance, transparent models, and ethical practices are essential for successful integration and sustained growth.

Keywords: Machine Learning, Financial Decision-Making, Banking, Technology Adoption, Data Quality, Risk Assessment, Customer Experience, Compliance, Ethics.

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