• DocumentCode
    127221
  • Title

    Feature selection of nonperforming loans in Chinese commercial banks

  • Author

    Zhang Yu ; Yu Guang ; Guan Yong-sheng ; Yang Dong-hui

  • Author_Institution
    Sch. of Manage., Harbin Inst. of Technol., Harbin, China
  • fYear
    2014
  • fDate
    17-19 Aug. 2014
  • Firstpage
    1208
  • Lastpage
    1215
  • Abstract
    In recent years, huge amounts of nonperforming loans (NPLs) of commercial banks have become one of the biggest obstacles constraining reform and development in Chinese commercial banks. Finding a way to control the banks´ NPLs is a core issue that it continues to be explored and researched in the finance. In this paper, PCA and relief algorithm in data mining methods were adopted to extract and analyze NPLs characteristics in commercial banks through contrasting the performing and nonperforming loans records, based on the predecessors´ literatures. In this paper, a bank´s loans data with 96 features and 10415 samples is collected. At last, we construct nonperforming loans of commercial banks classification model. Our research is very important for capturing warning signal timely, detection of NPLs and sound operation of commercial banks.
  • Keywords
    banking; data mining; feature selection; financial data processing; fraud; pattern classification; principal component analysis; Chinese commercial banks; NPL characteristics; PCA; bank loan data; commercial bank classification model; data mining methods; feature selection; nonperforming loans records; reform and development; relief algorithm; warning signal capturing; Analytical models; Data mining; Economics; Feature extraction; Indexes; Predictive models; Principal component analysis; PCA-relief algorithm; commercial banks; feature selection; nonperforming loans;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science & Engineering (ICMSE), 2014 International Conference on
  • Conference_Location
    Helsinki
  • Print_ISBN
    978-1-4799-5375-2
  • Type

    conf

  • DOI
    10.1109/ICMSE.2014.6930367
  • Filename
    6930367