Abstract
In the underwriting and pricing of non-life insurance products, it is essential for the insurer to utilize both policyholder information and claim history to ensure profitability and proper risk management. In this presentation, we present a flexible regression model with random effects, called the Mixed LRMoE, which leverages both policyholder information and their claim history, to classify policyholders into groups with similar risk profiles, and to determine a premium that accurately captures the unobserved risks. Estimates of model parameters and the posterior distribution of random effects can be obtained by a stochastic variational algorithm, which is numerically efficient and scalable to large insurance portfolios. Our proposed framework is shown to outperform the classical benchmark models (Logistic and Lognormal GL(M)M) in terms of goodness-of-fit to data, while offering intuitive and interpretable characterization of policyholders’ risk profiles to adequately reflect their claim history.

Bio: X. Sheldon Lin, ASA, ACIA, is a Professor of Actuarial Science at the University of Toronto and serves as an Editor for Insurance: Mathematics and Economics. His recent research is on data-driven nonlinear regression modelling for insurance rate-making and risk management of large insurance portfolios. The research aims to develop new and implementable methodology and technologies for insurance. 

Tencent:
https://meeting.tencent.com/dm/CHKJqAciZ3nE

ID：538-658-949
PW：202157