Abstract
In this paper we propose an approach to predict insolvency of non-life insurance companies based on the application of Support Vector Machines (SVMs), hybridized with two global search heuristics: a Genetic Algorithm (GA) and a Simulated Annealing (SA). A SVM is used to classify firms as failed or non-failed, whereas a GA and a SA are used to perform on-line feature selection in the ratios space of the SVM, in order to improve its perfor-mance. We use general financial ratios and also other specific ratios which have been proposed for evaluating insolvency of insurance sector. In the simulations section, we compare the performance of the GA and SA as part of the proposed algorithm. The results obtained with both techniques show that the proposed algorithm can be a useful tool for parties interested in evaluating insolvency of non-life insurance firms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
