A hybrid model using decision tree and neural network for credit scoring problem


Amir Arzy Soltan and Mohammad Mehrabioun Mohammadi


Nowadays credit scoring is an important issue for financial and monetary organizations that has substantial impact on reduction of customer attraction risks. Identification of high risk customer can reduce finished cost. An accurate classification of customer and low type 1 and type 2 errors have been investigated in many studies. The primary objective of this paper is to develop a new method, which chooses the best neural network architecture based on one column hidden layer MLP, multiple columns hidden layers MLP, RBFN and decision trees and assembling them with voting methods. The proposed method of this paper is run on an Australian credit data and a private bank in Iran called Export Development Bank of Iran and the results are used for making solution in low customer attraction risks.


DOI: j.msl.2012.04.021

Keywords: BPNN ,Neural network ,Data mining ,Information Technology

How to cite this paper:

Soltan, A & Mehrabioun Mohammadi, M. (2012). A hybrid model using decision tree and neural network for credit scoring problem.Management Science Letters, 2(5), 1683-1688.


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