Borrowers' Loan Repayment Coefficient Prediction Using Machine Learning

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dc.contributor.authorMensorale, O.
dc.contributor.authorOdumuyiwa, V.
dc.date.accessioned2024-11-04T10:28:22Z
dc.date.available2024-11-04T10:28:22Z
dc.date.issued2024-10-28
dc.description.abstractNano loan and unsecured loans is a fast-growing trend in the Nigeria fintech space. User creditworthiness and loan default is a problem for the whole industry. Traditional credit scoring methods have limitations in assessing the creditworthiness of diverse applicants by Loan Credit officers. In most cases, businesses lose money due to high defaults in customer loans on their loan investment portfolios. The loan repayment coefficient is a measurement of how soon a user is likely to repay a loan based on the user's historical data or similar users in the same demographic data historical data. This paper presents an extensive analysis of data on 212,042 creditworthy loan applicants' repayment patterns. This pattern was explored and studied, finding key parameters like age, gender, loan amount etc. in building a machine learning model to predict a user loan repayment coefficient. Two machine learning algorithm was considered namely Linear Regression and Random Forest algorithm to run the prediction. The model was deployed and embedded into the application programming interface (API) which receives user data and computes the repayment coefficient. The computed repayment coefficient after using the machine learning models ranges from 0% to 100% where 100% is a perfect score implying the user will pay on time. The anticipated outcomes of this project are significant, promising a paradigm shift in the lending industry. It is expected to yield improved credit decisions and contribute valuable insights to the field of machine learning in credit assessment if adopted.
dc.description.sponsorshipScholarly article
dc.identifier.citationMensorale O. & Odumuyiwa V. (2023). “Borrowers' Loan Repayment Coefficient Prediction Using Machine Learning". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2023), pp. 25-32, MIRG
dc.identifier.isbn978-978-767-697-4
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/13020
dc.language.isoen
dc.publisherMIRG
dc.relation.ispartofseriesMIRG-ICAIR 2023
dc.titleBorrowers' Loan Repayment Coefficient Prediction Using Machine Learning
dc.typeArticle
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