Credit scoring with boosted decision trees
AbstractThe enormous growth experienced by the credit industry has led researchers to develop sophisticated credit scoring models that 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 data mining techniques: the multilayer perceptron and support vector machines. The results show that boosted decision trees are a competitive technique for implementing credit scoring models.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 8034.
Date of creation: 01 Apr 2007
Date of revision:
Credit scoring; Boosting; Decision tree; neural network; support vector machine;
Find related papers by JEL classification:
- C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-04-15 (All new papers)
- NEP-CFN-2008-04-15 (Corporate Finance)
- NEP-CMP-2008-04-15 (Computational Economics)
- NEP-RMG-2008-04-15 (Risk Management)
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