Credit scoring with boosted decision trees

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 deci- sion trees to form a classier given by a weighted majority vote of classications predicted by individual decision trees. The performance of boosted decision trees is evaluated using two publicly available credit card application datasets. The predic- tion accuracy of boosted decision trees is benchmarked against two alternative data mining techniques: the multilayer perceptron and support vector machines. The re- sults show that boosted decision trees are a competitive technique for implementing credit scoring models.