TITLE:
Performance of Different Machine Learning Algorithms for Credit Risk Classification
AUTHORS:
Martin M. Kasina, John M. Kihoro, Alex Kibet
KEYWORDS:
Supervised Machine Learning, Loan Payment Default, Random Forest, XG Boost, Logistic Regression, Confusion Matrix, F1-Score
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.13 No.4,
November
4,
2025
ABSTRACT: Microfinance institutions in Kenya play a unique role in promoting financial inclusion, loans, and savings provision, especially to low-income individuals and small-scale entrepreneurs. However, despite their benefits, most of their products and programs in Machakos County have been reducing due to repayment challenges, threatening their financial ability to extend further credit. This could be attributed to ineffective credit scoring models which are not able to establish the nuanced non-linear repayment behavior and patterns of the loan applicants. The research objective was to enhance credit risk scoring for microfinance institutions in Machakos County using supervised machine learning algorithms. The study adopted a mixed research design under supervised machine learning approach. It randomly sampled 6771 loan application account records and repayment history. Rstudio and Python programming languages were deployed for data pre-processing and analysis. Logistic regression algorithm, XG Boosting and the random forest ensemble method were used. Metric evaluations used included the performance accuracy, Area under the Curve and F1-Score. Based on the study findings: XG Boosting was the best performer with 83.3% accuracy and 0.202 Brier score. Development of legal framework to govern ethical and open use of machine learning assessment was recommended. A similar research but using different machine learning algorithms, locations, and institutions, to ascertain the validity, reliability and the generalizability of the study findings was recommended for further research.