PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Feature Selection Methods involving Support Vector Machines for Prediction of Insolvency in Non-life Insurance Companies
Sancho Salcedo, Mario de Prado, Maria Jesus Segovia, Fernando Perez-Cruz and Carlos Bousño
Intelligent Systems in Accounting, Finance and Management Volume 12, Number 4, pp. 261-281, 2004.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1180
Deposited By:Fernando Perez-Cruz
Deposited On:19 November 2005