Abstract:
Loan 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.
