Joint model prediction and application

Joint model prediction and application
Joint model prediction and application to individual-level loss reserving

In non-life insurance, the payment history can be predictive of the timing of a settlement for individual claims. Ignoring the association between the payment process and the settlement process could bias the prediction of outstanding payments. To address this issue, we introduce into the literature of micro-level loss reserving a joint modeling framework that incorporates longitudinal payments of a claim into the intensity process of claim settlement. We discuss statistical inference and focus on the prediction aspects of the model. We demonstrate applications of the proposed model in the reserving practice and identify scenarios where the joint model outperforms macro-level reserving methods.

Peng Shi
Associate Professor @Wisconsin School of Business
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 research interests are problems at the intersection of insurance and statistics. Professor Shi is an Associate of the Casualty Actuarial Society and a Fellow of the Society of Actuaries. He has won several research awards, including the Charles A. Hachemeister Prize, the Ronald Bornhuetter Loss Reserve Prize, and the American Risk and Insurance Association Prize.
11/18/2020 8:00 AM - 9:00 AM


Wednesday, 18 November 2020

11/18/2020 8:00 AM