Comparative Analysis of the Performance of Machine Learning- Models in the Prediction of Credit Risk Assessment

Authors

  • Moses A. Kolnagbayan Department: Computer and Information Sciences, University of Liberia
  • Martin K. Wallace Department: Computer and Information Sciences, University of Liberia
  • Li Chow Department of Computer Science and Artificial Intelligence, Amity University, Jaipur
  • Darnette M. Herron Department: Computer and Information Sciences, University of Liberia
  • Francis Saah Department: Computer and Information Sciences, University of Liberia
  • Titus G. Gooding Department: Computer and Information Sciences, University of Liberia
  • Melvin I. Soclo Department of Information Technology, The United Methodist University, Monrovia, Liberia

Keywords:

credit risk assessment, financial institutions, machine learning, predictive

Abstract

This article conducts a comparative analysis of various machine learning models in predicting credit risk assessment. The study aims to discern the most effective model for enhancing accuracy and efficiency in this domain. Leveraging a comprehensive historical credit dataset with diverse borrower attributes and credit performance indicators, several machine learning algorithms, including K-Nearest Neighbors, decision trees, support vector machines, random forests, and Naive Bayes, were rigorously evaluated. Through meticulous data preprocessing and feature extraction techniques, the performance of each model was assessed using key evaluation metrics such as accuracy, precision, and recall. The findings highlight the superior predictive capabilities of certain models over others in identifying credit defaults and non-performing loans, shedding light on nuanced variable interactions influencing credit risk. This analysis serves as a valuable guide for financial institutions seeking to adopt the most effective machine learning model in their credit risk assessment processes.

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Published

2024-12-16

Issue

Vol. 52 No. 1 (2024)

Section

Articles

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Copyright (c) 2024 Moses A. Kolnagbayan, Martin K. Wallace, Li Chow, Darnette M. Herron, Francis Saah, Titus G. Gooding, Melvin I. Soclo

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How to Cite

Moses A. Kolnagbayan, Martin K. Wallace, Li Chow, Darnette M. Herron, Francis Saah, Titus G. Gooding, & Melvin I. Soclo. (2024). Comparative Analysis of the Performance of Machine Learning- Models in the Prediction of Credit Risk Assessment. International Journal of Computer (IJC), 52(1), 79-88. https://www.ijcjournal.org/InternationalJournalOfComputer/article/view/2281