Abstract
Past studies have documented the failure of the Insurance Regulatory Information System (IRIS) to provide adequate warning of insurer financial distress or insolvency. As a result, scholars have examined alternative parametric and non-parametric models to predict insurer insolvency. This study uses a neural network, a non-parametric alternative to past techniques, and shows how this methodology predicts insurer insolvency more effectively than parametric models.
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Goss, E.P., Ramchandani, H. Comparing classification accuracy of neural networks, binary logit regression and discriminant analysis for insolvency prediction of life insurers. J Econ Finan 19, 1–18 (1995). https://doi.org/10.1007/BF02920611
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DOI: https://doi.org/10.1007/BF02920611