Genetic programming for the prediction of insolvency in non-life insurance companies
Prediction of non-life insurance companies insolvency has arised as an important problem in the field of financial research, due to the necessity of protecting the general public whilst minimizing the costs associated to this problem, such as the effects on state insurance guaranty funds or the responsibilities for management and auditors. Most methods applied in the past to predict business failure in non-life insurance companies are traditional statistical techniques, which use financial ratios as explicative variables. However, these variables do not usually satisfy statistical assumptions, what complicates the application of the mentioned methods. Emergent statistical learning methods like neural networks or SVMs provide a successful approach in terms of error rate, but their character of black-box methods make the obtained results difficult to be interpreted and discussed. In this paper, we propose an approach to predict insolvency of non-life insurance companies based on the application of genetic programming (GP). GP is a class of evolutionary algorithms, which operates by codifying the solution of the problem as a population of LISP trees. This type of algorithm provides a diagnosis output in the form of a decision tree with given functions and data. We can treat it like a computer program which returns an answer depending on the input, and, more importantly, the tree can potentially be inspected, interpreted and re-used for different data sets. We have compared the performance of GP with other classifiers approaches, a Support Vector Machine and a Rough Set algorithm. The final purpose is to create an automatic diagnostic system for analysing non-insurance firms using their financial ratios as explicative variables.
genetic programming insolvency non-life insurance companies support vector machines rough set