Credit Risk Assessment: A Nonlinear Multi-parameter Model

Author

Wu, Jia-lin ; Shi, Yuan

Author_Institution

Sch. of Software, Sun Yat-sen Univ., Guangzhou, China

fYear

2009

fDate

15-17 May 2009

Firstpage

887

Lastpage

891

Abstract

Modern credit risk management aims at assess the default probability (DP) of a debtor according to his historical and current financial data. Due to its prominent importance in credit loan decisions, the DP assessment becomes a research focus in the filed of financial data mining. To tackle this problem, we propose a nonlinear multi-parameter model (NMM) based on domain knowledge. Additionally, the parameters of the model are estimated using one of recent evolutionary algorithm - differential evolution (DE). In the experiment, real-world financial data is utilized to test the performance of NMM. Experimental results reveal that NMM performs much better than back-propagation neural network (BPNN).

Keywords

backpropagation; data mining; evolutionary computation; financial data processing; neural nets; risk management; backpropagation neural network; credit loan decisions; credit risk assessment; credit risk management; default probability; differential evolution algorithm; financial data mining; nonlinear multiparameter model; Contracts; Data mining; Evolutionary computation; Neural networks; Pricing; Risk management; Stock markets; Support vector machine classification; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

2009 International Conference on Signal Processing Systems

Conference_Location

Singapore

Print_ISBN

978-0-7695-3654-5

Type

conf

DOI

10.1109/ICSPS.2009.201

Filename

5166918