GEOGRAPHIC RATEMAKING WITH SPATIAL EMBEDDINGS

DOI10.1017/asb.2021.25zbMath1484.91375arXiv2104.12852OpenAlexW4205923959MaRDI QIDQ5067876

Étienne Marceau, Christopher Blier-Wong, Hélène Cossette, Luc Lamontagne

Publication date: 4 April 2022

Published in: ASTIN Bulletin (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2104.12852

zbMATH Keywords

neural networksembeddingsrepresentation learningnonlife insuranceterritorial pricing

Mathematics Subject Classification ID

Mathematical geography and demography (91D20) Spatial models in economics (91B72) Actuarial mathematics (91G05)

Related Items (1)

The use of autoencoders for training neural networks with mixed categorical and numerical features

Uses Software

• mgcv
• GeoDa
• word2vec
• PyTorch
• Adam

Cites Work

• Unnamed Item
• Non-life rate-making with Bayesian GAMs
• Spatial modelling of claim frequency and claim size in non-life insurance
• Bayesian Premium Rating with Latent Structure
• Geographic Premium Rating by Whittaker Spatial Smoothing
• A data driven binning strategy for the construction of insurance tariff classes
• TERRITORIAL RISK CLASSIFICATION USING SPATIALLY DEPENDENT FREQUENCY-SEVERITY MODELS

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