Credit Rating Prediction Using Different Machine Learning Techniques. International

Article


Aiyegbeni, G., Li, Y., Annan, J. and Adebayo, F. 2023. Credit Rating Prediction Using Different Machine Learning Techniques. International. International Journal of Data Science and Advanced Analytics. 5 (5), pp. 219-238.
AuthorsAiyegbeni, G., Li, Y., Annan, J. and Adebayo, F.
Abstract

Credit rating prediction is a crucial task in the banking and financial industry. Financial firms want to identify the
likelihood of customers repaying loans or credit. With the advent of machine learning algorithms and big data analytics, it is now possible to automate and improve the accuracy of credit rating prediction. In this research, we aim to develop a machine learning-based approach for customer credit rating prediction. Machine learning algorithms, including decision trees, random forests, support vector machines, and logistic regression, were evaluated and compared in terms of accuracy, precision, and AUC. Feature selection was also performed to analyze the importance of different features in predicting credit ratings. Findings suggested that status, duration, credit history, amount, savings, other debtors, property, and employment duration are the most important features in predicting credit ratings. Results showed that the support vector machine algorithm did best in predicting bad credits. This research demonstrates the potential of machine learning algorithms for customer credit rating prediction and could have significant implications for the banking and financial industry by enabling more accurate and efficient credit rating predictions and reducing the risk of defaults and financial losses.

KeywordsCredit rating; Credit default; Machine learning; Default prediction; Model optimization
JournalInternational Journal of Data Science and Advanced Analytics
Journal citation5 (5), pp. 219-238
ISSN2563-4429
Year2023
PublishereSystem Engineering Society (eSES) with The American University of Iraq – Baghdad (AUIB)
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Web address (URL)https://www.ijdsaa.com/index.php/welcome/article/view/193
Publication dates
Print29 Oct 2023
Publication process dates
Deposited15 Apr 2024
Copyright holder© 2023, The Authors
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