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Application of AI in Credit Scoring Modeling.
・ISBN 978-3-658-40179-5 paper EUR 99.99
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| 著者・編者 | Popovych, Bohdan, |
|---|---|
| シリーズ | BestMasters |
| 出版社 | (Springer Gabler, GW) |
| 出版年月 | 2022.12 |
| ページ数 | 83 pp. |
| 言語 | ENG |
| ニュース番号 | ＜693-323＞ |
解説
The scope of this study is to investigate the capability of AI methods to accurately detect and predict credit risks based on retail borrowers' features. The comparison of logistic regression, decision tree, and random forest showed that machine learning methods are able to predict credit defaults of individuals more accurately than the logit model. Furthermore, it was demonstrated how random forest and decision tree models were more sensitive in detecting default borrowers.