{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T22:16:39Z","timestamp":1767046599315},"reference-count":32,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Info. Tech. Dec. Mak."],"published-print":{"date-parts":[[2005,12]]},"abstract":"<jats:p> A major challenge in credit card portfolio management is to classify and predict credit cardholders' behaviors in a reliable precision because cardholders' behaviors are rather dynamic in nature. This is crucial for creditors because it allows them to take proactive actions and minimize charge-off and bankruptcy losses. Although the methods used in the area of credit portfolio management have improved significantly, the demand for alternative and sophisticated analytical tools is still strong. <\/jats:p><jats:p> The objective of this paper is to propose a multiple criteria quadratic programming (MCQP) to classify credit card accounts for business intelligence and decision making. MCQP is intended to predict credit cardholders' behaviors from a nonlinear perspective that is justifiable because both the objective functions and constraints in credit card accounts classification may be nonlinear. Using a real-life credit card dataset from a major US bank, the MCQP method is compared with popular and similar classification methods: linear discriminant analysis, decision tree, multiple criteria linear programming, support vector machine, and neural network. The results indicate that MCQP is a promising business intelligence method in credit card portfolio management. <\/jats:p>","DOI":"10.1142\/s0219622005001775","type":"journal-article","created":{"date-parts":[[2005,12,1]],"date-time":"2005-12-01T11:33:22Z","timestamp":1133436802000},"page":"581-599","source":"Crossref","is-referenced-by-count":63,"title":["CLASSIFYING CREDIT CARD ACCOUNTS FOR BUSINESS INTELLIGENCE AND DECISION MAKING: A MULTIPLE-CRITERIA QUADRATIC PROGRAMMING APPROACH"],"prefix":"10.1142","volume":"04","author":[{"given":"YONG","family":"SHI","sequence":"first","affiliation":[{"name":"Chinese Academy of Sciences Research Center on Data Technology and Knowledge Economy, Beijing 100039, China"},{"name":"Peter Kiewit Institute of Information Science, Technology &amp; Engineering, University of Nebraska, Omaha, NE 68182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YI","family":"PENG","sequence":"additional","affiliation":[{"name":"Peter Kiewit Institute of Information Science, Technology &amp; Engineering, University of Nebraska, Omaha, NE 68182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"GANG","family":"KOU","sequence":"additional","affiliation":[{"name":"Peter Kiewit Institute of Information Science, Technology &amp; Engineering, University of Nebraska, Omaha, NE 68182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"ZHENGXIN","family":"CHEN","sequence":"additional","affiliation":[{"name":"Peter Kiewit Institute of Information Science, Technology &amp; Engineering, University of Nebraska, Omaha, NE 68182, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,20]]},"reference":[{"key":"rf1","volume-title":"An Introduction to Multivariate Statistical Analysis","author":"Anderson T. 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