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Variable Selection for Credit Risk Model Using Data Mining technique

Author(s): Kuangnan Fang | Hong Huang

Journal: Journal of Computers
ISSN 1796-203X

Volume: 6;
Issue: 9;
Start page: 1868;
Date: 2011;
Original page

Keywords: Credit Default Risk | Logit | Logistic Regression Model

With the emergence of the current financial crisis, societies see the increasing importance of credit risks management in financial institutions. Four mainstream credit risk rating models have been developed, however, their applicability in the Taiwan market is yet to be evaluated. In this paper, six major credit risk models, including Merton Option Pricing Model,Discriminant Analysis Model, Logistic Regression (Logit) Model, Probit Model, Survival Analysis Model, and Artificial Neural Network Model were examined, in order to identify the common variables applicable to each model.  The common variables were then applied to each respective model directly. Using Transition Matrix and mapping methods to estimate long term default probability, for developing appropriate credit risk model with the estimated default probability.
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