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Loan Prediction Using Machine Learning Methods
- 1 West Vancouver Secondary School, West Vancouver, BC, Canada
- 2 College of Information Engineering, Zhejiang University of technology, Wenzhou, China
- 3 College of Information Engineering, Shenzhen University, Shenzhen, China
* Author to whom correspondence should be addressed.
Abstract
Credit risk has always been the most important risk faced by commercial banks. Credit risk management has important practical significance for preventing credit risk. With the emerging of machine learning algorithms, numerous frameworks, including linear regression, support vector machine, random forest and decision tree are proposed with satisfying performance and robust accuracy. This paper will focus on predicting credit outcomes and calculating forecast accuracy from a given dataset. This paper adopts three algorithms, decision tree, random forest and logistic regression, to calculate the dataset from the Bank of Portugal separately and obtain relevant conclusions. Finally, the authors evaluate the advantages and disadvantages of the three methods according to the accuracy of the prediction results, and the conclusion is described as follow, First, all three methods have great potential on handling loan prediction task. Second, the logistic regression algorithm is the most accurate, which obtains 86.4% accuracy.
Keywords
Loan prediction, Machine learning, Logistic regression
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Cite this article
Wang,S.;You,S.;Zhou,S. (2023). Loan Prediction Using Machine Learning Methods. Advances in Economics, Management and Political Sciences,5,210-215.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2022 International Conference on Financial Technology and Business Analysis (ICFTBA 2022), Part 1
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