Financial Institution Readiness and Adoption of Machine Learning Algorithm and Performance of Select Banks in Rivers State, Nigeria

Aug 3, 2026

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The study examined how banks’ readiness and adoption of machine learning algorithms influence performance in Rivers State. It evaluated financial institutions’ preparedness to incorporate ML to strengthen efficiency, effectiveness, and productivity within the banking sector. Organizational leadership clarity, employees’ willingness to change, and access to technology were analyzed as key determinants of improved service delivery outcomes. Data were collected through structured questionnaires from 133 respondents, yielding 120 valid responses, and assessed using Spearman rank correlation at a 5% significance threshold.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

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ABSTRACT

1. INTRODUCTION

Asian Journal of Economics, Finance and Management Volume 5, Issue 1, Page 180-192, 2023; Article no.AJEFM.1298 Financial Institution Readiness and Adoption of Machine Learning Algorithm and Performance of Select Banks in Rivers State, Nigeria Achara, Miriam a, Emeka J. Okereke a, Nwulu Stephen Onyemere a and Ufuoma Earnest Ofierohora* a University of Port Harcourt Business School, Nigeria. Authors’ contributions This work was carried out in collaboration among all authors. All authors read and approved the final manuscript. Original Research Article Received: 01/05/2023 Accepted: 03/07/2023 Published: 17/07/2023 ABSTRACT The study investigated how banks’ readiness and adoption of machine learning algorithms affect performance of banks in Rivers State. The study assessed the preparedness of financial institutions in incorporating MLA to enhance their performance. The concentration is on the banking sector and the aim is to uncover the level of readiness and the elements that may guide the adoption of ML in the financial business. This study focused on how organizational leadership clarity, employees' willingness to change, and access to technology affects efficiency, effectiveness, and productivity of banks in Rivers State. Data for the study were collected using structured questionnaires distributed to 133 respondents , with only 120 valid questionnaires. The Spearman rank correlation coefficient (SRPCC) method was employed in the study at the 5% threshold. The SRPCC test revealed that organizational leadership clarity, employees' attitude to change, and access to technology all had a significant impact on measures of improved service delivery (efficiency, effectiveness, and productivity) in banks in Rivers State. Finally, banks'readiness and adoption of machine learning algorithms have beneficial and consequential relationship with service delivery performance. According to the study, banks should implement strong organizational leadership clarity to ensure their readiness and willingness to use machine learning algorithms to improve service delivery outcomes. Employees' attitudes towards the _____________________________________________________________________________________________________ *Corresponding author: Email: [ufuomaearnest@gmail.com](ufuomaearnest@gmail.com); Asian J. Econ. Fin. Manage., vol. 5, no. 1, pp. 180-192, 2023 acceptance and use of machine learning algorithms should be encouraged and improved through knowledge transfers, as it serves as a springboard for improved service delivery performance amongst banks. Keywords: Machine learning algorithms; service deliver; financial; rivers; performance, improvement. 1. INTRODUCTION There are many new technologies shaping the future, such as the cloud computing, Internet of Things (IoT), blockchain, simulated and augmented reality, e-commerce, machine learning algorithm, and e-commerce. The automation of tried-and-true (like surveys, questionnaire, interviews, records and documents, focus groups) methods of gathering and analyzing data is hastened by technological advancements. The relationship between technological progress and regulation is threatened. However, with the concerns over data security and privacy that automation brings up, traditional financial institutions have had to comply with more stringent regulations than newer, more technologically advanced fintech startups [1] . Traditional financial institutions face difficulty due to technological advancements in the financial sector [2] . On one hand, as regulation becomes more stringent, banks are compelled to reduce risks, boost capital adequacy, and stabilize their revenue streams (Buchak et al. 2018) . However, banks face huge competition from ‘machine learning algorithm ready firms’, which could cut into their market share and force them to make riskier investments [3] . This is owing to these innovations provide banks with more accurate quantitative and anal

FAQs

What was the main focus of the study on banks in Rivers State?

The study investigated how banks’ readiness and adoption of machine learning algorithms affect their performance and service delivery outcomes in Rivers State.

Which factors were examined as drivers of improved service delivery?

The study assessed organizational leadership clarity, employees’ willingness/attitude to change, and access to technology as determinants of improved efficiency, effectiveness, and productivity.

How were the research data collected and analyzed?

Structured questionnaires were distributed to 133 respondents and 120 valid responses were used. Spearman rank correlation coefficient was applied at the 5% significance threshold.

Aug 3, 2026

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