AI in Loan Prediction

Nguyen, Huy Hoang (2025) AI in Loan Prediction. Masters thesis, Dublin, National College of Ireland.

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Abstract

Low loan approval rates and inefficient lending procedures have hindered access to essential financing for housing, education, small business expansion, and healthcare—factors that can significantly improve quality of life. This study investigates the effectiveness of machine learning (ML) models in predicting loan amounts based on individual financial history, addressing the need for a simple, accessible evaluation tool for borrowers with limited financial literacy. Unlike previous studies that focused on classification models for loan approval or default prediction, this research innovates by using a regression-based ML approach to estimate the loan amount. Five machine learning models were trained, with the XGBoost model achieving the best performance, yielding an R-squared score of 0.82 and an RMSE of 2,417.89 on the test data. These results demonstrate robust accuracy and the potential of ML applications in finance. In practice, the model provides a scalable tool for lending institutions to streamline loan evaluation and for borrowers to better understand their borrowing potential, thereby enhancing financial inclusion. However, challenges remain in addressing AI transparency and mitigating bias in training data, which can impact the fairness and reliability of model outcomes. Future research should focus on improving model interpretability and ensuring unbiased predictions to fully realize the potential of machine learning in transforming lending practices.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Onwuegbuche, Faithful Chiagoziem UNSPECIFIED
Subjects:	Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence H Social Sciences > HG Finance > Credit. Debt. Loans. H Social Sciences > HG Finance > Fintech T Technology > T Technology (General) > Information Technology > Fintech
Divisions:	School of Computing > Master of Science in FinTech
Depositing User:	Ciara O'Brien
Date Deposited:	24 Jun 2026 10:48
Last Modified:	24 Jun 2026 10:48
URI:	https://norma.ncirl.ie/id/eprint/9395

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