کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382670 660778 2013 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Quantitative credit risk assessment using support vector machines: Broad versus Narrow default definitions
چکیده انگلیسی

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.


► This paper comparisons credit scoring models built using Broad and Narrow default definitions.
► It was shown that models built using a Broad definition could lead to better model performance.
► Also, this paper applied the SVM algorithm to credit scoring in a Barbados based credit union.
► It was showed that credit scoring can lead to better decision making at the study institution.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 40, Issue 11, 1 September 2013, Pages 4404–4413
نویسندگان
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