Date: 2021-11-19

Time: 15:30-16:30 (Montreal time)

https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09

Meeting ID: 834 3668 6293

Passcode: 12345

Abstract:

We propose a dependence-aware predictive modeling framework for multivariate risks stemmed from an insurance contract with bundling features – an important type of policy increasingly offered by major insurance companies. The bundling feature naturally leads to longitudinal measurements of multiple insurance risks. We build a novel predictive model that actively exploits the dependence among the evolution of multivariate repeated risk measurements. Specifically, the longitudinal measurement of each individual risk is first modeled using pair copula construction with a D-vine structure, and the multiple D-vines are then integrated by a flexible copula. While our analysis mainly focuses on the claim count as the measurement of insurance risk, the proposed model indeed provides a unified modeling framework that can accommodate different scales of measurements, including continuous, discrete, and mixed observations. A computationally efficient sequential method is proposed for model estimation and inference, and its performance is investigated both theoretically and via simulation studies. In the application, we examine multivariate bundled risks in multi-peril property insurance using the proprietary data obtained from a commercial property insurance provider. The proposed predictive model is found to provide improved decision making for several key insurance operations, including risk segmentation and risk management. In the underwriting operation, we show that the experience rate priced by the proposed model leads to a 9% lift in the insurer’s profit. In the reinsurance operation, we show that the insurer underestimates the risk of the retained insurance portfolio by 10% when ignoring the dependence among bundled insurance risks.

Speaker

Peng Shi is an associate professor in the Risk and Insurance Department at the Wisconsin School of Business. He is also the Charles and Laura Albright Professor in Business and Finance. His interests are problems at the intersection of insurance and statistics. Current research focuses on longitudinal data, dependence models, insurance analytics, and actuarial data science.

Professor Shi is an Associate of the Casualty Actuarial Society (ACAS) and a Fellow of the Society of Actuaries (FSA). He holds a Ph.D. in business with a minor in economics from the University of Wisconsin-Madison.