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Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
Type: Invited
Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #328629
Title: A Bayesian Model for Multivariate Micro-Level Insurance Claims
Author(s): Marie-Pier Côté* and Christian Genest and David A Stephens
Companies: Universite Laval and McGill University and McGill University
Keywords: Bayesian; censoring; copula model

A Bayesian model for granular insurance claim amounts is proposed. It accounts for the multi-level, multivariate features of individual claims, e.g., multiple claimants for the same event, each of whom may receive bene fits under different coverages. To avoid sampling bias induced when relying only on closed fi les, a multiple imputation procedure exploiting open file data is proposed. For a given claim, the combination of coverages under which payments are made forms a type which is modeled with multinomial regression. The presence of legal and claims expert fees follows a logistic regression, given the type. The strictly positive severities are then modeled with log skewed normal regressions linked by a Student t copula. The Bayesian framework yields a predictive distribution for the amounts paid, including parameter risk and process risk, while handling missing covariates and open fi les. The approach is illustrated with Accident Benefi ts car insurance claims from a large Canadian company.

Authors who are presenting talks have a * after their name.

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