A support vector machine approach to credit scoring
A support vector machine approach to credit scoring
Driven by the need to allocate capital in a profitable way and by the recently suggested Basel II regulations, financial institutions are being more and more obliged to build credit scoring models assessing the risk of default of their clients. Many techniques have been suggested to tackle this problem.
Support Vector Machines (SVMs) is a promising new technique that has recently emanated from different domains such as applied statistics, neural networks and machine learning. In this paper, we experiment with least squares support vector machines (LS-SVMs), a recently modified version of SVMs, and report significantly better results when contrasted with the classical techniques.
basel ii, internal rating based system, credit scoring, support vector machines
73-82
Van Gestel, Tony
e917bd96-d291-4132-958b-e54cb1b9eaf9
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Garcia, Joao
8652f13b-37c4-4df6-84de-8cc761acf683
Van Dijcke, Peter
b6de696a-011a-47ea-a7dd-a1e7e9dcac0c
2003
Van Gestel, Tony
e917bd96-d291-4132-958b-e54cb1b9eaf9
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Garcia, Joao
8652f13b-37c4-4df6-84de-8cc761acf683
Van Dijcke, Peter
b6de696a-011a-47ea-a7dd-a1e7e9dcac0c
Van Gestel, Tony, Baesens, Bart, Garcia, Joao and Van Dijcke, Peter
(2003)
A support vector machine approach to credit scoring.
Bank en Financiewezen, (2), .
Abstract
Driven by the need to allocate capital in a profitable way and by the recently suggested Basel II regulations, financial institutions are being more and more obliged to build credit scoring models assessing the risk of default of their clients. Many techniques have been suggested to tackle this problem.
Support Vector Machines (SVMs) is a promising new technique that has recently emanated from different domains such as applied statistics, neural networks and machine learning. In this paper, we experiment with least squares support vector machines (LS-SVMs), a recently modified version of SVMs, and report significantly better results when contrasted with the classical techniques.
This record has no associated files available for download.
More information
Published date: 2003
Keywords:
basel ii, internal rating based system, credit scoring, support vector machines
Identifiers
Local EPrints ID: 37172
URI: http://eprints.soton.ac.uk/id/eprint/37172
PURE UUID: 032d0ff9-4d99-4aeb-af15-ad5fc9e00fb9
Catalogue record
Date deposited: 24 May 2006
Last modified: 12 Dec 2021 03:27
Export record
Contributors
Author:
Tony Van Gestel
Author:
Joao Garcia
Author:
Peter Van Dijcke
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
