Predicting Loan Defaults Using Ensemble Machine Learning And Ai-Driven Credit Scoring Models: A Comparative Study

Abstract

Loan defaults are important to correctly forecast, so as to ensure the survival and profitability of financial institutions. The
commonly used traditional credit scoring models do not always reflect nonlinear connections between borrower behavior
and macroeconomic circumstances, which are rather complex. This paper examines the relative efficacy of ensemble
machine learning models and state-of-the-art AI-based credit scoring systems when it comes to predicting loan defaults.
We train, compare and test various models: Random Forest, Gradient Boosting, and deep learning-based hybrids using
an actual dataset of lending. These results prove that ensemble approaches are much more effective in predicting the
outcome compared to older models and AI-based models using alternative data sources offer an even higher potential of
risk assessments. These findings support the benefits of explainable AI approaches as a way to strike the balance between
interpretability and performance on the one hand and provide real-world advising to the lending sector, regulatory
authorities, and fintech start-ups on how to streamline the management of credit risk.