Centro de Documentación de Fundación MAPFRE - Modeling lapse rates using machine learning : a comparison between Survival Forests and Cox Proportional Hazards techniques

Notes: Sumario: This study undertakes a comparative analysis of the performance of machine learning and traditional survival analysis techniques in the insurance industry. The techniques compared are the traditional Cox Proportional Hazards (CPH), Random Survival Forests (RSF) and Conditional Inference Forests (CIF) machine learning models. These techniques are applied in a case study of insurance portfolio of one of Ecuador's largest insurer. This study demonstrates how machine learning techniques perform better in predicting survival function measured by the C-index and Brier Score. It also demonstrates that the predictive contribution of covariates in the RSF model is consistent with the traditional CPH modelRelated records: En: Anales del Instituto de Actuarios Españoles : Colegio Profesional. - Madrid : Instituto de Actuarios Españoles, 1943-. - 29/12/2021 Número 27 Epoca 4ª época - 2021 , p. 161-183Materia / lugar / evento: Análisis actuarial Machine learning Modelos de supervivencia Análisis comparativo Cartera de seguros Análisis predictivos Otros autores: Valencia, José Luis
Instituto de Actuarios Españoles
Other categories: 6

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