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Feature selection methods involving support vector machines for prediction of insolvency in non‐life insurance companies

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  • Sancho Salcedo‐Sanz
  • Mario DePrado‐Cumplido
  • María Jesús Segovia‐Vargas
  • Fernando Pérez‐Cruz
  • Carlos Bousoño‐Calzón

Abstract

We propose two novel approaches for feature selection and ranking tasks based on simulated annealing (SA) and Walsh analysis, which use a support vector machine as an underlying classifier. These approaches are inspired by one of the key problems in the insurance sector: predicting the insolvency of a non‐life insurance company. This prediction is based on accounting ratios, which measure the health of the companies. The approaches proposed provide a set of ratios (the SA approach) and a ranking of the ratios (the Walsh analysis ranking) that would allow a decision about the financial state of each company studied. The proposed feature selection methods are applied to the prediction the insolvency of several Spanish non‐life insurance companies, yielding state‐of‐the‐art results in the tests performed. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Sancho Salcedo‐Sanz & Mario DePrado‐Cumplido & María Jesús Segovia‐Vargas & Fernando Pérez‐Cruz & Carlos Bousoño‐Calzón, 2004. "Feature selection methods involving support vector machines for prediction of insolvency in non‐life insurance companies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(4), pages 261-281, October.
    • Handle: RePEc:wly:isacfm:v:12:y:2004:i:4:p:261-281
    DOI: 10.1002/isaf.255
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