A survey of applying machine learning techniques for credit rating: Existing models and open issues

Wang, X; Xu, M; Pusatli, ÖT

A survey of applying machine learning techniques for credit rating: Existing models and open issues

Wang, X Xu, M

Pusatli, ÖT

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 9490 pp. 122 - 132
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Wang, X	en_US
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849	en_US
dc.contributor.author	Pusatli, ÖT	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, 9490 pp. 122 - 132	en_US
dc.identifier.isbn	9783319265346	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122603
dc.description.abstract	© Springer International Publishing Switzerland 2015. In recent years, machine learning techniques have been widely applied for credit rating. To make a rational comparison of performance of different learning-based credit rating models, we focused on those models that are constructed and validated on the two mostly used Australian and German credit approval data sets. Based on a systematic review of literatures, we further compare and discuss about the performance of existing models. In addition, we identified and illustrated the limitations of existing works and discuss about some open issues that could benefit future research in this area.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-26535-3_15	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A survey of applying machine learning techniques for credit rating: Existing models and open issues	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9490	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	9490	en_US

Abstract:

© Springer International Publishing Switzerland 2015. In recent years, machine learning techniques have been widely applied for credit rating. To make a rational comparison of performance of different learning-based credit rating models, we focused on those models that are constructed and validated on the two mostly used Australian and German credit approval data sets. Based on a systematic review of literatures, we further compare and discuss about the performance of existing models. In addition, we identified and illustrated the limitations of existing works and discuss about some open issues that could benefit future research in this area.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/122603