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http://hdl.handle.net/11129/2207
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Title: | A Comparative Analysis of Machine Learning Techniques for Credit Scoring |
Authors: | Nwulu, Nnamdi Oroja, Shola İlkan, Mustafa School of Computing And Technology |
Keywords: | Artificial Neural Network Credit Scoring Machine Learning Support Vector Machines |
Issue Date: | Oct-2012 |
Publisher: | Information - Yamaguchi |
Abstract: | Abstract Credit Scoring has become an oft researched topic in light of the increasing
volatility of the global economy and the recent world financial crisis. Amidst the many
methods used for credit scoring, machine learning techniques are becoming increasingly
popular due to their efficient and accurate nature and relative simplicity. Furthermore
machine learning techniques minimize the risk of human bias and error and maximize
speed as they are able to perform computationally difficult tasks in very short times. |
Description: | The file in this item is the publisher version (published version) of the article. |
URI: | http://hdl.handle.net/11129/2207 |
ISSN: | 1343-4500 |
Appears in Collections: | SCT – Journal Articles: Publisher & Author Versions (Post-Print Author Versions) – SCT
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