Artificial Intelligence (AI) In ICT Management and Performance of Commercial Banks in Nairobi City County, Kenya
DOI:
https://doi.org/10.47941/jts.3854Keywords:
Artificial Intelligence (AI) In ICT Management, Machine Learning, Natural Language Processing, Commercial BanksAbstract
Purpose: The general objective of the study is to assess the effect of artificial intelligence (AI) in ICT management on performance of commercial banks in Nairobi City County, Kenya. Specifically, the study sought to establish the influence of machine learning and natural language processing on performance of commercial banks in Nairobi City County.
Methodology: The descriptive research design was employed. The unit of analysis for the study was 40 commercial banks in Kenya (CBK, 2024) while the unit of observation was management employees. The accessible population was 246 individuals comprising of 41 top managers, 82 middle level managers and 123 lower-level managers. The study used Krejcie and Morgan (1970) formula to arrive at the sample size. The study sample size was therefore 152 employees. Stratified random sampling was applied to get the respondents. Data was collected using a self-administered semi-structured questionnaire. Data obtained from the field was coded, cleaned, and entered into the computer for analysis using the SPSS version 25. Inferential statistical analysis used was multiple regression and correlation analysis.
Findings: The study found that machine learning has a positive and significant effect on performance of commercial banks in Nairobi City County, Kenya. In addition, the study showed that natural language processing has a positive and significant effect on performance of commercial banks in Nairobi City County, Kenya.
Unique Contribution to Theory, Practice and Policy: Based on the findings, the study recommends that commercial banks in Kenya should invest in the adoption and integration NLP technologies to enhance customer engagement and operational efficiency. By deploying NLP-powered tools such as chatbots, automated customer support systems, and sentiment analysis platforms, banks can provide faster, more accurate, and personalized responses to customer inquiries while reducing service costs.
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References
Abbaspour, P. (2023). Role of artificial intelligence and IT governance on industrial pollution management in Canada. Retrieved From, https://www.researchgate.net/profile/Pouya-Abbaspour/publication/
Adebayo, S. O., & Akinyemi, B. A. (2022). Artificial Intelligence and ICT Management in Nigerian Financial Institutions. African Journal of Information Systems, 14(2), 112–128.
Central Bank of Kenya. (2023). Bank Supervision Annual Report 2023. https://www.centralbank.go.ke
Chimbunde, E, Sigwadhi, L. N., & Tamuzi, J. L. (2023). Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa. Retrieved from file:///C:/Users/user/Downloads/
Communications Authority of Kenya. (2022). ICT Sector Statistical Report for Q4 2021–2022. https://www.ca.go.ke
Crowther, D. & Lancaster, G. (2018). Research Methods: A Concise Introduction to Research in Management and Business Consultancy. New York: Butterworth-Heinemann.
Deloitte. (2022). AI and the Banking Sector: Transforming Performance through Intelligence. https://www2.deloitte.com
Feng, C, Kephart, G., & Juarez-Colunga, E. (2021). Predicting COVID-19 mortality risk in Toronto, Canada: a comparison of tree-based and regression-based machine learning methods. BMC Medical Research Methodology, 21(267), 1-14
Katsuki, M, Narita, N., & Matsumori, Y. (2020). Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire. Surgical Neurology International, 11(475), 1-6
Kenya Institute for Public Policy Research and Analysis (KIPPRA). (2022). Digital Readiness and the Adoption of Emerging Technologies in Kenya. https://www.kippra.or.ke
Kimutai, H., & Muchelule, Y. (2025). Integration of artificial intelligence and performance of broadcasting companies in Nairobi City County, Kenya. International Journal of Management and Business Research, 7(1), 474-491.
Kituku, B, Nganga, W., & Muchemi, L. (2021). Leveraging on cross linguistic similarities to reduce grammar development effort for the under-resourced languages: a case of Kenyan Bantu languages. Retrieved from https://repository.dkut.ac.ke:8080/xmlui/
Munga, C. (2024). Artificial Intelligence integration and business management metrics in the telecommunication sector in Kenya. Retrieved From, http://repository.kcau.ac.ke:8080/bitstream/handle/
Muriu, P. (2016). Mobile based expert system model for animal health monitoring : cows disease monitoring in Kenya. Retrieved from http://su-plus.strathmore.edu/handle/11071/4853
Mwangi, J. (2024). Analyzing the role of artificial intelligence and machine learning in optimizing supply chain processes in Kenya. International Journal of Supply Chain Management, 9(1), 39-50
Mziray, E. J., & Author, J. (2023). Effectiveness of information systems on automation of business processes for nonprofit organizations in Nairobi city. European Journal of Theoretical and Applied Sciences, 1(5), 1147-1154
Otundo, M. R. (2024). Robotic Process Automation (RPA) and AI: an empirical analysis in Kenya. Retrieved from file:///C:/Users/user/Downloads/
Pair, E, Vicas, N., & Weber, A. M. (2021). Quantification of gender bias and sentiment toward political leaders over 20 years of Kenyan news using natural language processing. Retrieved from file:///C:/Users/user/Downloads/
Punjab Information Technology Board. (2022). Annual Report on AI and ICT Management in Pakistan. https://www.pitb.gov.pk
Salim, M. M. H. M. (2024). The impact of Artificial Intelligence acceptance on customer satisfaction in the telecommunication industry in Egypt. Reprieved From, https://journals.ekb.eg/article
Sauer, J, Overos, H., & Newlands, J. (2022). Analyzing multilingual discussions of the standard gauge railway project in Kenya using natural language processing and social media analysis. Retrieved from https://apps.dtic.mil/sti/trecms/pdf/
Sobczak, A. (2021). Robotic process automation implementation, deployment approaches and success factors – an empirical study in Canada. Entrepreneurship and Sustainability Issues, 8(4), 122-147
State Council of China. (2022). New Generation Artificial Intelligence Development Plan. https://www.gov.cn
Stevens, W. (2023). Robotic process automation in supply chain in South Africa. European Journal of Supply Chain Management, 1(1), 1-10
Turyamushanga, L. (2023). Accounting expert systems and financial performance in Rwanda. Retrieved from https://irbackend.kiu.ac.ug/server/
Uganda Communications Commission. (2022). ICT Development and Emerging Technologies in Uganda. https://www.ucc.co.ug
Wanjohi, N., & Waithaka, S. (2024). Information expert systems and performance of insurance firms in Nairobi County, Kenya. International Journal of Technology and Systems, 9(2), 1–20
Zeng, Y., Lu, E., & Huangfu, C. (2022). Artificial Intelligence in Chinese Financial Services: A Review. Journal of Asian Finance, Economics and Business, 9(4), 87–98.
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