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Credit risk detection based on machine learning algorithms

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https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.inderscience.com%2Flink.php%3Fid%3D126871;h=repec:ids:ijfsmg:v:11:y:2022:i:3:p:183-189
Time Added
2022/11/21 19:29
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Machine Learning
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0
Authors
Xin Wang Kai Zong and Cuicui Luo
Abstract
As the global economic environment has become more complicated in recent years more and more credit bonds have defaulted. The credit risk early warning model plays a very effective role in preventing and controlling financial risk and debt default. This paper uses machine learning methods to establish a credit default risk prediction framework. In this paper the oversampling technique is first applied to deal with imbalanced credit default data sets and then the credit risk detection performance of several machine learning algorithms is compared. The empirical results show that the performance of the ensemble learning algorithms is the best.
Keywords
machine learning ; credit risk detection ; ensemble learning. (search for similar items in EconPapers)
Year Published
2022
Series
International Journal of Financial Services Management 2022 vol. 11 issue 3 183-189
Rank
0.5
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