Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network
Currently, legal requirements demand that insurance companies increase their emphasis on monitoring the risks linked to the underwriting and asset management activities. Regarding underwriting risks, the main uncertainties that insurers must manage are related to the premium su ciency to cover futur...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Alcalá (UAH) |
| Repositorio: | e_Buah Biblioteca Digital Universidad de Alcalá |
| Idioma: | inglés |
| OAI Identifier: | oai:ebuah.uah.es:10017/59171 |
| Acceso en línea: | http://hdl.handle.net/10017/59171 https://dx.doi.org/10.1016/j.eswa.2020.113782 |
| Access Level: | acceso abierto |
| Palabra clave: | Stochastic reserving Reserving Risk Machine Learning General insurance Run-off prediction Economía Economics |
| Sumario: | Currently, legal requirements demand that insurance companies increase their emphasis on monitoring the risks linked to the underwriting and asset management activities. Regarding underwriting risks, the main uncertainties that insurers must manage are related to the premium su ciency to cover future claims and the adequacy of the current reserves to pay outstanding claims. Both risks are calibrated using stochastic models due to their nature. This paper introduces a reserving model based on a set of machine learning techniques such as Gradient Boosting, Random Forest and Articial Neural Networks. These algorithms and other widely used reserving models are stacked to predict the shape of the runo . To compute the deviation around a former prediction, a log-normal approach is combined with the suggested model. The empirical results demonstrate that the proposed methodology can be used to improve the performance of the traditional reserving techniques based on Bayesian statistics and a Chain Ladder, leading to a more accurate assessment of the reserving risk. |
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