Title
A multiple intelligent agent system for credit risk prediction via an optimization of localized generalization error with diversity
Document Type
Journal article
Source Publication
Journal of Systems Science and Systems Engineering
Publication Date
Spring 1-1-2007
Volume
16
Issue
2
First Page
166
Last Page
180
Keywords
Business intelligence, Credit rating, Feature grouping, Localized generalization error, Multiple classifier system
Abstract
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.
DOI
10.1007/s11518-007-5048-4
Print ISSN
10043756
E-ISSN
18619576
Publisher Statement
Copyright © Systems Engineering Society of China and Springer 2007
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Yeung, D. S., ng, W. W. Y., Chan, A. P. F., Chan, P. P. K., Firth, M., & Tsang, E. C. C. (2007). A multiple intelligent agent system for credit risk prediction via an optimization of localized generalization error with diversity. Journal of Systems Science and Systems Engineering, 16(2), 166-180. doi: 10.1007/s11518-007-5048-4
