FEATURE SELECTION METHODS INVOLVING SUPPORT VECTOR MACHINES FOR PREDICTION OF INSOLVENCY IN NON-LIFE INSURANCE COMPANIES

摘要

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.