Credit scoring with boosted decision trees

João Bastos

MPRA Paper from University Library of Munich, Germany

Abstract: The 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.

Keywords: Credit scoring; Boosting; Decision tree; neural network; support vector machine (search for similar items in EconPapers)
JEL-codes: C44 G32 (search for similar items in EconPapers)
Date: 2007-04-01
New Economics Papers: this item is included in nep-cfn, nep-cmp and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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https://mpra.ub.uni-muenchen.de/8156/1/MPRA_paper_8156.pdf revised version (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:8034

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