Article2003

A support vector machine approach to credit scoring

Tony Van Gestel · Bart Baesens · João Pedro Silva Garcia · Peter Van Dijcke

103 citations0/0 files storedePrints Soton (University of Southampton)

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Financial Distress and Bankruptcy Prediction Credit Risk and Financial Regulations Imbalanced Data Classification Techniques

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.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 2003Benchmarking state-of-the-art classification algorithms for credit scoring 2001Bankruptcy prediction for credit risk using neural networks: A survey and new results 2001Financial time series prediction using least squares support vector machines within the evidence framework 1966The Determination of Long-Term Credit Standing with Financial Ratios

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