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

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