1 Department of Computer Science and Quantitative Methods, Austin Peay State University, Clarksville, USA.
2 Department of Computer Science, University of Lagos, Lagos State Nigeria.
3 School of Law, University of Washington, Seattle, USA.
4 School of Business, George Washington University, Washington DC, USA.
5 Department of Geology, Anambra State University Uli, Anambra State Nigeria.
6 Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom.
Research Article
World Journal of Advanced Research and Reviews, 2024, 22(02), 942–955
Article DOI: 10.30574/wjarr.2024.22.2.1455
Received on 03 April 2024; revised on 11 May 2024; accepted on 13 May 2024
Money-Lending financial institutions face risk, which necessitates adopting a robust framework to manage it effectively. While traditional methods have been applied across the financial industry, the advent of artificial intelligence offers organizations the opportunity to utilize advanced methods to manage credit risk. This paper focuses on the application of machine learning techniques for credit risk analysis. Secondary data on information related to borrowers was extracted from Kaggle database simulating Credit Bureau data. Two ensemble models in random forest and gradient boosting were adopted for this study. The findings showed that percentage of income for loan repayment, borrower’s income, and interest rates on loans are the most important features for determining defaulters. Furthermore, the evaluation results revealed that both the random forest and the gradient boosting algorithms performed well, with F1 scores of 92.9% and 93% respectively. It was recommended that financial institutions should priorities the verification and comprehensiveness of their data, as precise data is essential for developing resilient models.
Machine Learning; Algorithm; Credit; Credit Risk; Credit Risk Analysis; Financial Institution
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Sevidzem Simo Yufenyuy, Sulaimon Adeniji, Emmanuel Elom, Somto Kizor-Akaraiwe, Abdul-Waliyyu Bello, Emmanuel Kanu, Oluwadamilola Ogunleye, Juliet Ogutu, Chinenye Obunadike, Valentine Onih and Callistus Obunadik. Machine learning for credit risk analysis across the United States. World Journal of Advanced Research and Reviews, 2024, 22(2), 942-955. Article DOI: https://doi.org/10.30574/wjarr.2024.22.2.1455