Parameter Uncertainty and Model Adequacy for GLMs Applied to Property/Casualty Insurance Data

Accurate forecasting is of crucial importance in managing insurance risks and ensuring a solvent and profitable operation. In recent years the property/Casualty insurance industry has adopted generalized linear models (GLMs) to improve the fit and prediction accuracy of their insurance portfolio models. Yet, the interdependence between the different insurance covers included in packaged products, such as car insurance, need to be explained in the GLM in order to include them in the predictive process. This objective of this project is the implementation and fine tuning of the model derived in a first internship last summer.

Faculty Supervisor:

Dr. Jose Garrido

Student:

Oscar Alberto Quijano Xacur

Partner:

AVIVA Canada

Discipline:

Mathematics

Sector:

Finance, insurance and business

University:

Concordia University