Machine Learning Approaches to Credit Risk Prediction in Mobile Lending Platforms in Kenya: A Rapid Scoping Review
Keywords:
Kenya; mobile lending; credit risk; machine learning; scoping reviewAbstract
Research on credit-risk prediction in Kenyan mobile lending uses different populations, outcomes and evaluation designs, making published model scores difficult to interpret. Licensed digital lenders issued about 9.6 million loans by August 2026, so the evidence behind scoring models matters. This rapid scoping review maps model families, data sources, prediction targets and validation approaches and identifies priorities for repayment-risk research. English-language theses, research projects and journal articles published between 30 October 2016 and 30 October 2026 were eligible. Web, repository and citation discovery was supplemented by OpenAlex and Crossref searches, restricted to the first 25 relevance-ranked records for each of eight query–index combinations (200 records; 186 after deduplication). Reporting was informed by PRISMA-ScR; screening and extraction in the update were single-pass and AI-assisted. Ten records were included, nine assessed in full text and one through an institutional abstract. Two analysed observed outcomes from Kenyan lenders: a study of 16,861 microfinance loans reported Random Forest AUC 0.7558, and a scorecard study of 495 digital-lender borrowers reported ROC-AUC 0.825. The other eight used foreign loans, simulations, a household-survey access proxy, an engineered risk label, household risk profiles or a scoring prototype. These tasks and evaluation conditions do not support a common performance ranking, and methodological variety exceeds directly validated evidence of future repayment. Priorities are independently observed repayment outcomes with explicit default definitions, local external validation, borrower-separated and temporal testing, and calibration and class-specific reporting. Practitioners should validate candidate models on their own lending population. Relevance-capped searches, one abstract-only source and the absence of independent human verification limit completeness and confidence; no universally superior algorithm is established.
References
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627–635. https://doi.org/10.1057/palgrave.jors.2601545
Barasa, I. N., & Wanyonyi, S. W. (2026). Predictive modeling of digital credit risk in commercial banks using machine learning algorithms. Asian Journal of Advanced Research and Reports, 20(3), 110–132. https://doi.org/10.9734/ajarr/2026/v20i31305
Bequé, A., Coussement, K., Gayler, R., & Lessmann, S. (2017). Approaches for credit scorecard calibration: An empirical analysis. Knowledge-Based Systems, 134, 213–227. https://doi.org/10.1016/j.knosys.2017.07.034
Berg, T., Burg, V., Gombović, A., & Puri, M. (2020). On the rise of FinTechs: Credit scoring using digital footprints. The Review of Financial Studies, 33(7), 2845–2897. https://doi.org/10.1093/rfs/hhz099
Bharadwaj, P., Jack, W., & Suri, T. (2019). Fintech and household resilience to shocks: Evidence from digital loans in Kenya (NBER Working Paper No. 25604). National Bureau of Economic Research. https://doi.org/10.3386/w25604
Björkegren, D., Blumenstock, J. E., & Knight, S. (2026). Manipulation-robust prediction. American Economic Review, 116(9), 3263–3293. https://doi.org/10.1257/aer.20241087
Björkegren, D., & Grissen, D. (2020). Behavior revealed in mobile phone usage predicts credit repayment. The World Bank Economic Review, 34(3), 618–634. https://doi.org/10.1093/wber/lhz006
Central Bank of Kenya. (2022). The Central Bank of Kenya (Digital Credit Providers) Regulations, 2022. https://www.centralbank.go.ke/2022/03/21/central-bank-of-kenya-digital-credit-providers-regulations-2022/
Central Bank of Kenya. (2026, September 30). Licensing of 29 additional digital credit providers [Press release]. https://www.centralbank.go.ke/uploads/press_releases/366319113_Press%20Release%20-%20Licensing%20of%2029%20Additional%20Digital%20Credit%20Providers.pdf
Garritty, C., Gartlehner, G., Nussbaumer-Streit, B., King, V. J., Hamel, C., Kamel, C., Affengruber, L., & Stevens, A. (2021). Cochrane Rapid Reviews Methods Group offers evidence-informed guidance to conduct rapid reviews. Journal of Clinical Epidemiology, 130, 13–22. https://doi.org/10.1016/j.jclinepi.2020.10.007
Gathu, A. (2020). The role of alternative data in accurately determining credit score for mobile lending on digital wallets in Kenya [Master’s thesis, Strathmore University]. http://hdl.handle.net/11071/10193
Githenya, A. M. (2026). AI-based credit risk modelling for financially excluded M-Pesa users in Kenya using alternative data [Master’s thesis, International Hellenic University]. https://repository.ihu.gr/handle/11544/49990
Kapoor, S., & Narayanan, A. (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns, 4(9), Article 100804. https://doi.org/10.1016/j.patter.2023.100804
Karingithi, L. M. (2020). A centralized credit scoring prototype for micro lending institutions in Kenya [Master’s thesis, Strathmore University]. http://hdl.handle.net/11071/12172
Kisutsa, G. T. (2021). Loan default prediction using machine learning: A case of mobile based lending [Master’s project, University of Nairobi]. https://erepository.uonbi.ac.ke/items/caacdfc3-4259-45c2-a4ac-c418970a361a
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136. https://doi.org/10.1016/j.ejor.2015.05.030
Murphy, D. (2018). Prediction of loan defaulters in micro finance using social network data [Master’s research project, National College of Ireland]. https://norma.ncirl.ie/3414/
Mwesigwa, D., Jackson, S. J., & Csikszentmihalyi, C. (2026). Risk, data, alignment: Making credit scoring work in Kenya. In Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26). ACM. https://doi.org/10.1145/3772318.3790924
Ng’ang’a, A. A. (2025). AI-driven credit scoring using alternative data: Unlocking financial access in emerging markets [Master’s research project, National College of Ireland]. https://norma.ncirl.ie/9577/
Ngonde, M. M., Bowen, M., & Nzomoi, J. N. (2026). An empirical analysis of the determinants of digital credit default in Kenya. International Journal of Research and Innovation in Social Science, 10(3), 4078–4086. https://doi.org/10.47772/IJRISS.2026.100300303
Ntwiga, D. B., Ogutu, C., & Kirumbu, M. K. (2018). Inclusion of peer group and individual low-income earners in M-Shwari micro-credit lending: A hidden Markov model approach. International Journal of Electronic Finance, 9(2), 121–133. https://doi.org/10.1504/IJEF.2018.092195
Ntwiga, D. B., Ogutu, C., Kirumbu, M. K., & Weke, P. (2018). A hidden Markov model of risk classification among the low income earners. Journal of Finance and Economics, 6(6), 242–249. https://doi.org/10.12691/jfe-6-6-6
Oindi, B. (2024). A credit scoring model for mobile lending [Master’s thesis, Strathmore University]. http://hdl.handle.net/11071/15651
Omukami, H., Mwau, C., Wangui, D. K., & Mbuya, A. (2025). Credit scoring in digital lending: A stepwise logistic regression and weight of evidence approach in Kenya. The Egyptian Statistical Journal, 69(2), 229–244. https://doi.org/10.21608/esju.2025.415357.1115
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, Article n71. https://doi.org/10.1136/bmj.n71
Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis, 18(10), 2119–2126. https://doi.org/10.11124/JBIES-20-00167
Saito, T., & Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLOS ONE, 10(3), Article e0118432. https://doi.org/10.1371/journal.pone.0118432
Tricco, A. C., Antony, J., Zarin, W., Strifler, L., Ghassemi, M., Ivory, J., Perrier, L., Hutton, B., Moher, D., & Straus, S. E. (2015). A scoping review of rapid review methods. BMC Medicine, 13, Article 224. https://doi.org/10.1186/s12916-015-0465-6
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7),
