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A Support Vector Machine Approach to Credit Scoring

by Tony Van Gestel of Dexia Group,
Bart Baesens of Katholieke Universiteit Leuven,
Joao Garcia of Dexia Group, and
Peter Van Dijcke of Dexia Bank Belgium

July 8, 2003

Abstract: Driven by the need to allocate capital in a profitable way and by the recently suggested Basel II regulations, financial institutions are being more and more obliged to build credit scoring models assessing the risk of default of their clients. Many techniques have been suggested to tackle this problem. Support Vector Machines (SVMs) is a promising new technique that has recently emanated from different domains such as applied statistics, neural networks and machine learning. In this paper, we experiment with least squares support vector machines (LS-SVMs), a recently modified version of SVMs, and report significantly better results when contrasted with the classical techniques.

Keywords: Basel II, Internal Rating Based System, credit scoring, Support Vector Machines.

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