Department of Management Studies
Research Abstracts

 

Title: Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions
Subject Area: Accounting, Investment and Financial Management
Author(s): Terry Harris
Citation: Harris, T. (2013). Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions. Expert Systems with Applications, 40(11), 4404-4413.
Abstract: 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.
Document Type: Paper
Read More: http://www.sciencedirect.com/science/article/pii/S0957417413000754
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Department of Management Studies
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