A multiple intelligent agent system for credit risk prediction via an optimization of localized generalization error with diversity

  • Daniel S. Yeung
  • Wing W. Y. Ng
  • Aki P. F. Chan
  • Patrick P. K. Chan
  • Michael Firth
  • Eric C. C. Tsang


Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank’s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today’s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system (embedded in a multiple intelligent agent system) is proposed to predict the financial health of a company. In our model, each individual agent (classifier) makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge (represented by features) about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.


Credit rating business intelligence localized generalization error multiple classifier system feature grouping 


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Copyright information

© Systems Engineering Society of China & Springer-Verlag 2007

Authors and Affiliations

  • Daniel S. Yeung
    • 1
  • Wing W. Y. Ng
    • 1
    • 2
  • Aki P. F. Chan
    • 2
  • Patrick P. K. Chan
    • 2
  • Michael Firth
    • 3
  • Eric C. C. Tsang
    • 2
  1. 1.Media and Life Science Computing Laboratory, Shenzhen Graduate SchoolHarbin Institute of TechnologyShenzhenChina
  2. 2.Department of ComputingHong Kong Polytechnic UniversityHong Kong, China
  3. 3.Department of Finance and InsuranceLingnan UniversityHong Kong

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