• DocumentCode
    2344299
  • Title

    Green Credit Scoring System and Its Risk Assessemt Model with Support Vector Machine

  • Author

    Wang, Qiang ; Lai, Kin Keung ; Niu, Dongxiao

  • Author_Institution
    Sch. of Bus. & Manage., North China Electr. Power Univ., Beijing, China
  • fYear
    2011
  • fDate
    15-19 April 2011
  • Firstpage
    284
  • Lastpage
    287
  • Abstract
    Green financial products such as green loans have developed quickly worldwide in the last 5 years. Green credit extended so far is already more than 1 trillion Yuan in China, and huge growth is expected with further development of a low carbon economy. As green loan undertakes social responsibility, such as environmental protection and energy saving, and is one of the key factors in policy decisions, the traditional credit scoring system and risk evaluation model can not be used since here only financial and management factors are considered. In this paper, a green credit scoring system is presented which introduces new environment and energy factors. A SVM risk assessment model is created on this basis. Finally, a real-world dataset is applied to test the green credit scoring system and the SVM risk assessment model. The result shows that the new green credit scoring and SVM risk assessment models are effective.
  • Keywords
    banking; corporate social responsibility; environmental economics; risk management; support vector machines; sustainable development; China; financial management; green credit scoring system; green financial products; green loans; low carbon economy; policy decisions; real-world dataset; risk assessment model; risk evaluation model; social responsibility; support vector machine; Air pollution; Green products; Indexes; Industries; Kernel; Risk management; Support vector machines; SVM risk assessment model; green credit scoring; low carbon economy; support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Sciences and Optimization (CSO), 2011 Fourth International Joint Conference on
  • Conference_Location
    Yunnan
  • Print_ISBN
    978-1-4244-9712-6
  • Electronic_ISBN
    978-0-7695-4335-2
  • Type

    conf

  • DOI
    10.1109/CSO.2011.143
  • Filename
    5957662