An empirical study on credit scoring model for credit card by using data mining technology

Proceedings - 2011 7th International Conference on Computational Intelligence and Security, CIS 2011

Volumn , Issue , 2011, Pages 1279-1282

  Li, Wei ^a   Liao, Jibiao ^b  

^a DONGGUAN UNIVERSITY OF TECHNOLOGY   (China)

^b DGTVU   (China)

Author keywords

Credit card; Credit scoring; Data mining; Default; Probability of non default

Indexed keywords

AUTOMATIC INTERACTIONS; BANK CREDIT; CHI-SQUARED; CLASSIFICATION AND REGRESSION TREE; CREDIT CARDS; CREDIT SCORING; CREDIT SCORING MODEL; DATA MINING TECHNOLOGY; DEFAULT; EMPIRICAL STUDIES; LOGISTIC MODELS; PRINCIPLE COMPONENT ANALYSIS;

DECISION TREES; FORESTRY; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; TREES (MATHEMATICS);

DATA MINING;

DATA PROCESSING; DECISION THEORY; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBABILITY; SCORING;

EID: 84863073359     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIS.2011.283     Document Type: Conference Paper

References (5)

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3. Stefan Blochwitz, Marcus R. W. Martin, and Carsten S. Wehn. XIII. "Statistical Approaches to PD Validation in The Basel II Risk Parameters," pp. 289-305. Springer, 2006.
4. Xuezhen Wu, "Credit Scoring Model Validation", Master Thesis, University of AMTERDAM, 2009 www.science.uva.nl/onderwijs/thesis/centraal/files/ f1668180724.pdf
5. Wei Li, "Credit Card Customer Segmentation and Target Marketing Based on Data Mining", pp.73-76. IEEE, 2010, doi:10.1109/CIS.2010.23