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A Survey of Applying Machine Learning Techniques for Credit Rating: Existing Models and Open Issues

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9490))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Global Big Data Technologies Centre, University of Technology Sydney, Sydney, Australia

    Xiang Wang & Min Xu

  2. Department of Mathematics and Computer Science, Cankaya University, Ankara, Turkey

    Özgür Tolga Pusatli

Authors
  1. Xiang Wang
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  2. Min Xu
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  3. Özgür Tolga Pusatli
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Corresponding author

Correspondence to Min Xu .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Wang, X., Xu, M., Pusatli, Ö.T. (2015). A Survey of Applying Machine Learning Techniques for Credit Rating: Existing Models and Open Issues. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_15

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  • DOI: https://doi.org/10.1007/978-3-319-26535-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26534-6

  • Online ISBN: 978-3-319-26535-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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