Modeling Insurance Claims using Bayesian Nonparametric Regression

[2311.11487] Modeling Insurance Claims using Bayesian Nonparametric Regression Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Methodology arXiv:2311.11487 (stat) [Submitted on 20 Nov 2023 ( v1 ), last revised 6 Jan 2025 (this version, v2)] Title: Modeling Insurance Claims using Bayesian Nonparametric Regression Authors: Mostafa Shams Esfand Abadi , Kaushik Ghosh View a PDF of the paper titled Modeling Insurance Claims using Bayesian Nonparametric Regression, by Mostafa Shams Esfand Abadi and 1 other authors View PDF HTML (experimental) Abstract: The prediction of future insurance claims based on observed risk factors, or covariates, help the actuary set insurance premiums. Typically, actuaries use parametric regression models to predict claims based on the covariate information. Such models assume the same functional form tying the response to the covariates for each data point. These models are not flexible enough and can fail to accurately capture at the individual level, the relationship between the covariates and the claims frequency and severity, which are often multimodal, highly skewed, and heavy-tailed. In this article, we explore the use of Bayesian nonparametric (BNP) regression models to predict claims frequency and severity based on covariates. In particular, we model claims frequency as a mixture of Poisson regression, and the logarithm of claims severity as a mixture of normal regression. We use the Dirichlet process (DP) and Pitman-Yor process (PY) as a prior for the mixing distribution over the regression parameters. Unlike parametric regression, such models allow each data point to have its individual parameters, making them highly flexible, resulting in improved prediction accuracy. We describe model fitting using MCMC and illustrate their applicability using French motor insurance claims data. Subjects: Methodology (stat.ME) ; Applications (stat.AP) Cite as: arXiv:2311.11487 [stat.ME] (or arXiv:2311.11487v2 [stat.ME] for this version) https://doi.org/10.48550/arXiv.2311.11487 Focus to learn more arXiv-issued DOI via DataCite Journal reference: PLoS One 21(4): e0346734, 2026 Related DOI : https://doi.org/10.1371/journal.pone.0346734 Focus to learn more DOI(s) linking to related resources Submission history From: Mostafa Shams Esfand Abadi [ view email ] [v1] Mon, 20 Nov 2023 02:07:40 UTC (2,447 KB) [v2] Mon, 6 Jan 2025 20:03:35 UTC (2,447 KB) Full-text links: Access Paper: View a PDF of the paper titled Modeling Insurance Claims using Bayesian Nonparametric Regression, by Mostafa Shams Esfand Abadi and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ME < prev | next > new | recent | 2023-11 Change to browse by: stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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