A Study Using Machine Learning to Predict Loan Default in Nigerian Microfinance Banks

Akinyemi, Akintomiwa Tomisin (2023) A Study Using Machine Learning to Predict Loan Default in Nigerian Microfinance Banks. Masters thesis, Dublin, National College of Ireland.

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Abstract

The Nigerian microfinance banking industry is active and ever growing. However, a big problem for many of the banks that work in this sector is dealing with people defaulting on loans. This research project sets out to identify loan defaulters using machine learning. After implementing seven classifiers and evaluating each result using accuracy, precision, and other metrics, the boosting classifiers, Random Forest and XGBoost came out on top as the best in detecting loan defaults. Both techniques had an accuracy of 80.10% and 82.06% respectively, and a precision rate greater than 75%.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Sahni, Anu UNSPECIFIED
Uncontrolled Keywords:	Nigeria; Microfinance banks; loan; default; machine learning; Random Forest; XGBoost
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HG Finance > Banking H Social Sciences > HG Finance > Financial Services Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	06 May 2025 17:49
Last Modified:	06 May 2025 17:49
URI:	https://norma.ncirl.ie/id/eprint/7489

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