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Credit risk evaluation with least square support vector machine. (English) Zbl 1196.91060
Wang, Guoyin (ed.) et al., Rough sets and knowledge technology. First international conference, RSKT 2006, Chongqing, China, July 24--26, 2006. Proceedings. Berlin: Springer (ISBN 978-3-540-36297-5/pbk). Lecture Notes in Computer Science 4062. Lecture Notes in Artificial Intelligence, 490-495 (2006).
Summary: Credit risk evaluation has been a major focus of financial and banking industry due to recent financial crises and regulatory concern of Basel II. Recent studies have revealed that emerging artificial intelligent techniques are advantageous to statistical models for credit risk evaluation. In this study, we discuss the use of the least squares support vector machine (LSSVM) technique to design a credit risk evaluation system to discriminate good creditors from bad ones. Relative to the Vapnik’s support vector machine, the LSSVM can transform a quadratic programming problem into a linear programming problem thus reducing the computational complexity. For illustration, a published credits dataset for consumer credit is used to validate the effectiveness of the LSSVM. For the entire collection see [Zbl 1113.68013].

MSC:
91G40Credit risk
68T05Learning and adaptive systems
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