Predicting Credit Scores with Boosted Decision Trees
Credit scoring models help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a classifier given by a weighted majority vote of classi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Forecasting |
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| Online Access: | https://www.mdpi.com/2571-9394/4/4/50 |
| _version_ | 1797458560341245952 |
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| author | João A. Bastos |
| author_facet | João A. Bastos |
| author_sort | João A. Bastos |
| collection | DOAJ |
| description | Credit scoring models help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a classifier given by a weighted majority vote of classifications predicted by individual decision trees. The performance of boosted decision trees is evaluated using two publicly available credit card application datasets. The prediction accuracy of boosted decision trees is benchmarked against two alternative machine learning techniques: the multilayer perceptron and support vector machines. The results show that boosted decision trees are a competitive technique for implementing credit scoring models. |
| first_indexed | 2024-03-09T16:39:50Z |
| format | Article |
| id | doaj.art-1e20d2da1f9744afbe13ef9276543deb |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-09T16:39:50Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-1e20d2da1f9744afbe13ef9276543deb2023-11-24T14:52:58ZengMDPI AGForecasting2571-93942022-11-014492593510.3390/forecast4040050Predicting Credit Scores with Boosted Decision TreesJoão A. Bastos0Lisbon School of Economics and Management (ISEG) and CEMAPRE/REM, Universidade de Lisboa, 1200-781 Lisboa, PortugalCredit scoring models help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a classifier given by a weighted majority vote of classifications predicted by individual decision trees. The performance of boosted decision trees is evaluated using two publicly available credit card application datasets. The prediction accuracy of boosted decision trees is benchmarked against two alternative machine learning techniques: the multilayer perceptron and support vector machines. The results show that boosted decision trees are a competitive technique for implementing credit scoring models.https://www.mdpi.com/2571-9394/4/4/50forecastingcredit scoringcredit riskboosted decision treesmachine learning |
| spellingShingle | João A. Bastos Predicting Credit Scores with Boosted Decision Trees Forecasting forecasting credit scoring credit risk boosted decision trees machine learning |
| title | Predicting Credit Scores with Boosted Decision Trees |
| title_full | Predicting Credit Scores with Boosted Decision Trees |
| title_fullStr | Predicting Credit Scores with Boosted Decision Trees |
| title_full_unstemmed | Predicting Credit Scores with Boosted Decision Trees |
| title_short | Predicting Credit Scores with Boosted Decision Trees |
| title_sort | predicting credit scores with boosted decision trees |
| topic | forecasting credit scoring credit risk boosted decision trees machine learning |
| url | https://www.mdpi.com/2571-9394/4/4/50 |
| work_keys_str_mv | AT joaoabastos predictingcreditscoreswithboosteddecisiontrees |