Search
Documentation Center

Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data
Acceso al documento/Access the document
MAP20240013554
Duval, Francis ‌
Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data / Francis Duval, Jean-Philippe Boucher, Mathieu Pigeon
Sumario: We present novel cross-sectional and longitudinal claim count models for vehicle insurance built upon the combinedd actuarial neural network (CANN) framework proposed by Wüthrich and Merz. The CANN approach combines a classical actuarial model, such as a generalized linear model, with a neural network. This blending of models results in a two-component model comprising a classical regression model and a neural network part. The CANN model leverages the strengths of both components, providing a solid foundation and interpretability from the classical model while harnessing the flexibility and capacity to capture intricate relationships and interactions offered by the neural network. In our proposed models, we use well-known log-linear claim count regression models for the classical regression part and a multilayer perceptron (MLP) for the neural network part
En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 15/05/2024 Volumen 54 Número 2 - mayo 2024 , p.239-262
1. Ciencias Actuariales y Financieras ‌. 2. Seguro de automóviles ‌. 3. Siniestros ‌. 4. Mercado de seguros ‌. I. Boucher, Jean-Philippe ‌. II. Pigeon, Mathieu ‌. III. Title.