The use of artificial intelligence for business optimisation in banking: case of Nigeria

Akobe, David

The use of artificial intelligence for business optimisation in banking: case of Nigeria

Thesis / Dissertation

2025

Publisher

University of Cape Town

Department

Department of Information Systems

Faculty

Faculty of Commerce

Abstract

Background: The continuous increase in the amount of customer data stored by commercial banks necessitates the use of artificial intelligence (AI) technologies. AI technologies may be used for analysing customer data and predicting customer behaviour, with the aim of customer retention and satisfaction. Such technologies include machine learning algorithms and deep learning models. Commercial banks in Nigeria are employing these predictive analysis tools. However, not all Nigerian commercial banks currently use them. Objective: The objective of this study is to explore how Nigerian commercial banks use AI technologies for business optimisation. Methodology: This study was interpretivist, abductive, and followed a qualitative approach deploying a multiple case study design. This study used the Organisational information processing (OIP) theory as a theoretical framework. Through the concept of matching information processing capabilities to information processing needs, the study explored the use of AI in Nigerian commercial banks. The multiple case study design consisted of three banks, selected from a total of nineteen commercial banks. A purposive sampling approach was used to select 20 experienced data professionals from business intelligence (BI) departments of the selected banks. Data was collected through semi-structured interviews. The study used thematic analysis to analyse the data. Findings: Findings show that information processing needs such as customer and data needs motivated commercial banks to utilise AI technologies as information processing capabilities. AI technologies such as the Sentiment intensity analyser, LR, K-means algorithm, Naïve Bayes algorithm, K-nearest neighbour, and Recommendation engines, were used for various tasks. For example, sentiment analysis, customer segmentation, customer churn prediction, predicting loan collection credibility and product recommendations. Findings also show that deep learning models were not used by the commercial banks, due in part to a lack of computational resources Contribution: The research confirmed that the use of AI in commercial banks in Nigeria contributes to customer retention and satisfaction. It provided knowledge of how AI technologies were used by commercial banks. This knowledge is important for other banks in Nigeria that may eventually use AI technologies due to the constant growth of customer data. The study also refined the organisational information processing theory to capture the findings. The refined version was titled ‘Nigerian Commercial Bank Information Processing View'.

Keywords

AI, Banking

Nigeria

Reference:

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Masters

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