Rodrigo, KLSSandanayake, TCSilva, ATPPiyatilake, ITSThalagala, PDGanegoda, GUThanuja, ALARRDharmarathna, P2024-02-062024-02-062023-12-07http://dl.lib.uom.lk/handle/123/22199Loan defaults affect the financial sector, particularly impacting banks and lending institutions, resulting in a rise of non-performing assets and financial strain. To counteract this trend, traditional credit assessments use methods like credit scores and exploitation of socio-demographic composition of the customers. However, customers may possess numerous debt obligations that credit bureaus uncover, which can help to measure their repayment ability. This study proposed a comparative methodology that leverages five machine learning algorithms to predict personal loan defaults using debt-to-income ratio apart from the credit scoring models that prevail at banks. It analyzed the impact of debt payments on loan defaults and applied ensemble clustering to categorize customers’ risk levels based on their debt-to-income ratio. Experimental results indicated that ensemble clustering has enhanced the prediction power compared to conventional classification models to predict loan defaults.enPersonal loan defaultMachine learningEnsemble clusteringDebt-to-income ratioClassificationConference-Full-textITInformation Technology Research Unit, Faculty of Information Technology, University of Moratuwa.20238th International Conference in Information Technology Research 2023Moratuwa, Sri Lankapp. 1-6Proceedings of the 8th International Conference in Information Technology Research 2023samadhileesha@gmail.comthanujas@uom.lkthusharip@uom.lk