A support vector machine approach to credit scoring

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A support vector machine approach to credit scoring

Tony Van Gestel,Bart Baesens,João Garcia,Peter Van Dijcke-2003-01-01-ePrints Soton (University of Southampton)

105

TL;DRAbstract

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. <br/>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.

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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. <br/>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

Support vector machineMachine learningArtificial intelligenceLeast squares support vector machineComputer scienceBasel IIArtificial neural networkProbability of default

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