Predicting Bank Loan Risks Using Machine Learning Algorithms

Alsaleem, Maan Y.; Hasoon, Safwan O.

doi:10.33899/csmj.2020.164686

	Predicting Bank Loan Risks Using Machine Learning Algorithms
AL-Rafidain Journal of Computer Sciences and Mathematics
Article 11, Volume 14, Issue 1, 2020, Pages 149-158 PDF (986.83 K)
Document Type: Research Paper
DOI: 10.33899/csmj.2020.164686
Authors
Maan Y. Alsaleem^* ¹; Safwan O. Hasoon²
¹Directorate of Education in Nineveh, Mosul, Iraq
²College of Computer Science and Mathematics University of Mosul, Mosul, Iraq
Abstract
Bank loans play a crucial role in the development of banks investment business. Nowadays, there are many risk-related issues associated with bank loans. With the advent of computerization systems, banks have become able to register borrowers' data according their criteria. In fact, there is a tremendous amount of borrowers’ data, which makes the process of load management a challenging task. Many studies have utilized data mining algorithms for the purpose of loans classification in terms of repayment or when the loans are not based on customers’ financial history. This kind of algorithms can help banks in making grant decisions for their customers. In this paper, the performance of machine learning algorithms has been compared for the purpose of classifying bank loan risks using the standard criteria and then choosing (Multilayer Perceptron) as it has given best accuracy compared to RandomForest, BayesNet, NaiveBayes and DTJ48 algorithms.
Keywords
Bank loans; machine learning algorithms; Multilayer Perceptron; Classification; Accuracy; ROC

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