AI-Driven Credit Scoring and Financial Inclusion in Kenya: Balancing Innovation with Governance Safeguards
DOI:
https://doi.org/10.53819/81018102t2612Abstract
Financial inclusion has attracted attention from practitioners, policymakers, and researchers as a strategy for expanding financial access and supporting socioeconomic development among individuals, households, businesses, and economies. It refers to adequate access to affordable financial services, including payments, credit, savings, and insurance. As Kenya pursues Vision 2030 and Agenda 2063, financial inclusion remains central to inclusive growth. This study examined the role of artificial intelligence-driven credit scoring in advancing financial inclusion in Kenya and the importance of balancing innovation with governance safeguards. A systematic review was conducted using peer-reviewed journal articles, reports, and publications from authoritative institutions, including the World Bank and Central Bank of Kenya. The review found that AI-driven credit scoring expands credit assessment beyond conventional credit histories and collateral through alternative data such as mobile-money transactions, utility payments, rental records, and behavioural information. It can improve predictive accuracy, lending efficiency, and access to credit among underserved populations. However, governance challenges remain, including algorithmic bias, discrimination, privacy risks, limited transparency, model reliability concerns, weak accountability, and data protection gaps. The study concludes that the contribution of AI-driven credit scoring to financial inclusion depends on balancing technological innovation with governance safeguards. Financial institutions should implement fairness testing, data-quality controls, privacy protection, cybersecurity measures, and transparent model governance. Regulators, particularly the Central Bank of Kenya, should strengthen adaptive frameworks governing alternative-data use, consumer consent, algorithmic fairness, transparency, accountability, and data protection. Further cross-sectional and longitudinal studies are recommended to assess long-term inclusion outcomes and sustainable AI-enabled credit systems in Kenya.
Keywords: AI-Driven Credit Scoring, Financial Inclusion, Balancing Innovation, Governance Safeguards, Kenya
References
Alaba, S. J., Okolo, A. O., Koleoso, I., Daopuye, E., & Ishola, O. P. (2026). AI-Powered Credit Scoring Models for Inclusive Finance: Evaluating the Role of AI in Bridging Nigeria’s Credit Gap (No. hal-05459931).
Allen, F., & Santomero, A. M. (1997). The theory of financial intermediation. Journal of banking & finance, 21(11-12), 1461-1485.
Ayari, H., Guetari, P. R., & Kraïem, P. N. (2025). Machine learning powered financial credit scoring: a systematic literature review. Artif Intell Rev 59, 13 (2026). https://doi.org/10.1007/s10462-025-11416-2
Bethune, Z., Sultanum, B., & Trachter, N. (2021). An information-based theory of financial intermediation. The Review of Economic Studies, 89(5), 2381–2444. https://doi.org/10.1093/restud/rdab092
Boafo, J. A., Avevor, M., & Danzerl, N. O. P. (2025). AI-powered credit risk assessment in development finance: Opportunities and ethical challenges in emerging markets. World Journal of Advanced Research and Reviews, 26(3), 68-74.
Bongomin, G. O., Yosa, F., Lubega, J. B., Yourougou, P., & Amani, A. M. (2022). Financial intermediation by microfinance banks in rural Sub-Saharan africa: Financial intermediation theoretical approach. Journal of Comparative International Management, 24(2), 1–27. https://doi.org/10.7202/1085565ar
CBK (2025). Kenya National Financial Inclusion Strategy (2025-2028). https://www.centralbank.go.ke/wp-content/uploads/2025/12/Kenya-National-Financial-Inclusion-Strategy-2025-2028.pdf.
Chant, J. (1992). The New Theory of Financial Intermediation. In: Dowd, K., Lewis, M.K. (eds) Current Issues in Financial and Monetary Economics. Current Issues in Economics. Palgrave, London. https://doi.org/10.1007/978-1-349-21908-7_3
Cong-Lem, N. (2024). A comprehensive guide to conducting systematic reviews. In Considerations and Techniques for Applied Linguistics and Language Education Research (pp. 115-135). IGI Global Scientific Publishing.
Dang, A. V. (2025). AI-Driven Credit Scoring and Financial Inclusion: Performance, Fairness, And Transparency in Alternative-Data Lending. AIJFR-Advanced International Journal for Research, 6(6).
Dias, S. F., & Perera, K. L. W. (2026). Exploring the role of digital finance in advancing financial inclusion with challenges and future directions. Discov Sustain 7, 205 (2026). https://doi.org/10.1007/s43621-026-02629-8
Essien, I. A., Nwokocha, G. C., Erigha, E. D., Obuse, E., & Akindemowo, A. O. (2019). AI-Driven Credit Scoring Systems and Financial Inclusion in Emerging Markets. IRE Journals 2 (11), 517-534.
Garzozi-Pincay, R. F., Garzozi-Pincay, Y. S., Castillo-Burbano, J. L., Correa-Soto, S. R., Benavides-Ramírez, M., & Gallegos-Marcillo, B. X. (2025, April). The Sociodemographic Characteristics Use for Credit Scoring and Financial Inclusion in Credits Granting, Case: Ecuador 2024. In International Conference on Computational Finance and Business Analytics (pp. 193-211). Cham: Springer Nature Switzerland.
Grassi, L., Lanfranchi, D., Faes, A., & Renga, F. M. (2022). Do we still need financial intermediation? The case of decentralized finance–DeFi. Qualitative Research in Accounting & Management, 19(3), 323-347.
Gurley, John G. & Shaw, Edward S. (1960). Money in a Theory of Finance. Political Research Quarterly, 13(3), 812.
Jama, A., Eshetu, F., & Ali, H. (2025). Drivers of financial inclusion in sub-Saharan Africa countries. Cogent Social Sciences, 11(1), 2590235.
Kenya Bankers Association (2026). East African Banks Urged to Embrace AI, Alternative Data to Expand Credit Access. https://www.kba.co.ke/east-african-banks-urged-to-embrace-ai-alternative-data-to-expand-credit-access/
Khatri, A., Alvi, A. M., Mariyum, S. S., Zhang, X., Bhandari, R. B., & Patil, B. K. (2025). AI-Driven Credit Scoring in Microfinance: Enhancing Financial Inclusion for SDG 5 and SDG 10. Enterprise Development and Microfinance, 35(2), 164-177.
Li, C., Wang, H., Jiang, S., & Gu, B. (2024). The effect of AI-enabled credit scoring on financial inclusion: Evidence from an underserved population of over one million. MIS Quarterly, 48(4), 1803-1834.
Marak, N. R., & Ayyagari, L. R. (2025). Artificial intelligence for financial inclusion and sustainable development: A systematic literature review. Discover Artificial Intelligence, 5(1), 390.https://doi.org/10.1007/s44163-025-00668-0
Mhlanga, D. (2021). Financial inclusion in emerging economies: The application of machine learning and artificial intelligence in credit risk assessment. International journal of financial studies, 9(3), 39.
Ng'ang'a, A. A. (2026). AI-Driven Credit Scoring Using Alternative Data: Unlocking Financial Access In Emerging Markets (Dissertation, Dublin, National College of Ireland).
Omambia, F. M. (2025). Artificial intelligence (AI) and financial performance of the financial service industry in Kenya. African Journal of Commercial Studies, 6(4).
Oyasiji, O., Okesiji, A., Imediegwu, C. C., Elebe, O., & Filani, O. M. (2024). AI-Driven Credit Scoring Models: Fairness, Accuracy, and Regulatory Risk in Lending Markets. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(4), 586-605.
Riskhouse (2025). Artificial intelligence (AI) in credit risk assessment and scoring for financial institutions in Kenya. https://riskhouse.co.ke/artificial-intelligence-ai-in-credit-risk-assessment-and-scoring-for-financial-institutions-in-kenya/
Scholtens, B., & Van Wensveen, D. (2003). The theory of financial intermediation: An essay on what it does (not) explain (No. 2003/1). SUERF Studies.
Tambari, F. N., & Amos, A. O. (2024). AI and machine learning as tools for financial inclusion: challenges and opportunities in credit scoring. INTERNATIONAL JOURNAL, 13(2), 1052-1067.
Theuri, J., & Olukuru, J. (2022). The impact of Artficial Intelligence and how it is shaping banking (No. 61). KBA Centre for Research on Financial Markets and Policy Working Paper Series.
Verma, A., Sankhyayan, S., Sheoran, K., & Dhaliwal, R. (2026). Artificial Intelligence Powered Credit Scoring Models: Advancing Financial Inclusion Amidst Legal and Ethical Challenges in India. In World Conference on Information Systems for Business Management (pp. 336-345). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-12987-1_31
World Bank (2024). Financial Inclusion in Sub-Saharan Africa—Overview. https://www.worldbank.org/en/publication/globalfindex/brief/financial-inclusion-in-sub-saharan-africa-overview
World Bank (2024). Financial Inclusion. https://www.worldbank.org/ext/en/topic/financial-sector/financial-inclusion
Yah, S. (2025). The Impact of AI Driven Credit Scoring on Financial Inclusion in Cameroon. Available at SSRN 5685644.
