DocumentCode :
1876191
Title :
Data Mining in Building Behavioral Scoring Models
Author :
Hsieh, Horng-I ; Lee, Tsung-Pei ; Lee, Tian-Shyug
Author_Institution :
Grad. Inst. of Bus. Adm., Fu-Jen Catholic Univ., Taipei, Taiwan
fYear :
2010
fDate :
10-12 Dec. 2010
Firstpage :
1
Lastpage :
4
Abstract :
Credit scoring and behavioral scoring have become very important credit risk management tasks during the past few years due to the impact of several financial crises. The objective of the proposed study is to explore the performance of behavioral scoring using three commonly discussed data mining techniques-linear discriminant analysis (LDA), backpropagation neural networks (BPN), and support vector machine (SVM). To demonstrate the effectiveness of behavioral scoring using the above-mentioned techniques, behavioral scoring tasks are performed on one bank credit card dataset in Taiwan. As the results reveal, BPN outperforms other techniques in terms of overall scoring accuracy and hence is an efficient alternative in implementing behavioral scoring tasks.
Keywords :
backpropagation; bank data processing; data mining; neural nets; support vector machines; backpropagation neural networks; bank credit card dataset; behavioral scoring; credit risk management task; credit scoring; data mining; linear discriminant analysis; support vector machine; Accuracy; Artificial neural networks; Biological neural networks; Data mining; Risk management; Support vector machines; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-5391-7
Electronic_ISBN :
978-1-4244-5392-4
Type :
conf
DOI :
10.1109/CISE.2010.5677005
Filename :
5677005
Link To Document :
بازگشت