A Modified Genetic Programming for Behavior Scoring Problem

Author

Qing-Shan, Chen ; De-fu, Zhang ; Li-Jun, Wel ; Huo-Wang, Chen

Author_Institution

Dept. of Comput. Sci., Xiamen Univ.

fYear

2007

fDate

March 1 2007-April 5 2007

Firstpage

535

Lastpage

539

Abstract

Behavior scoring is an important part of risk management in financial institutions, which is used to help financial institutions make better decisions in managing existing customers by forecasting their future credit performance. In this paper, a modified genetic programming (MGP) is introduced to solve the behavior scoring problems. A real life credit data set in a Chinese commercial bank is selected as the experimental data to demonstrate the classification accuracy of this method. MGP is compared with back-propagation neural network (BPN), and another GP that uses normalized inputs (NGP), the experimental results show that the MGP has slight better classification accuracy rate than NGP, and outperforms BPN in dealing with behavior scoring problems because of less historical samples of credit data in Chinese commercial banks

Keywords

backpropagation; customer relationship management; financial data processing; genetic algorithms; Chinese commercial bank; backpropagation neural network; behavior scoring problem; financial institutions; future credit performance forecasting; genetic programming; real life credit data set; risk management; Artificial intelligence; Artificial neural networks; Computational intelligence; Computer science; Data mining; Decision trees; Genetic programming; Neural networks; Risk management; Statistical analysis; Back-propagation Neural Network (BPN); Behavior Scoring; Data Mining; Genetic Programming (GP);

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on

Conference_Location

Honolulu, HI

Print_ISBN

1-4244-0705-2

Type

conf

DOI

10.1109/CIDM.2007.368921

Filename

4221345