A Two-Phase Model Based on SVM and Conjoint Analysis for Credit Scoring

Authors:
Kin Keung Lai;Ligang Zhou;Lean Yu
Affiliations:
Department of Management Sciences, City University of Hong Kong, Hong Kong;Department of Management Sciences, City University of Hong Kong, Hong Kong;Department of Management Sciences, City University of Hong Kong, Hong Kong
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Year:
2007

Citing 2
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Abstract

In this study, we use least square support vector machines (LSSVM) to construct a credit scoring model and introduce conjoint analysis technique to analyze the relative importance of each input feature for making the decision in the model. A test based on a real-world credit dataset shows that the proposed model has good classification accuracy and can help explain the decision. Hence, it is an alternative model for credit scoring tasks.