Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions

摘要

This paper compares support vector machine (SVM) based credit-scoring models built using Broad (less than 90 days past due) and Narrow (greater than 90 days past due) default definitions. When contrasting these two types of models, it was shown that models built using a Broad definition of default can outperform models developed using a Narrow default definition. In addition, this paper sought to create accurate credit-scoring models for a Barbados based credit union. Here, the results of empirical testing reveal that credit risk evaluation at the Barbados based institution can be improved if quantitative credit risk models are used as opposed to the current judgmental approach.