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A Comparative Analysis of Machine Learning Techniques for Credit Scoring. (Nwulu, Nnamdi I.)
Bibliographical information (record 267709)
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A Comparative Analysis of Machine Learning Techniques for Credit Scoring.
Author:
 Nwulu, Nnamdi I. Search in Online Databases

Publisher:
Int Information Inst.,
Edition:
 2012.
Classification:
 TA153
Detailed notes
    - 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. In this work, a comparative analysis is performed between two machine learning techniques namely Support Vector Machines and Artificial Neural Networks. This study compares both techniques in terms of accuracy, computational complexity and processing times. In order to assure meaningful comparisons, a real world dataset precisely the Australian Credit Scoring data set is used for this task. Obtained experimental results show that although both machine learning techniques can be applied successfully, Artificial Neural Networks slightly outperform Support Vector Machines.
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Status
Library
Section
EOL-1563
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NEU Grand LibraryOnline (TA153 .C66 2012)
Online electronic
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