Optimizing Credibility Complements in Property and Casualty Insurance: A Data-Driven Approach for Enhanced Premium Estimation

Within the field of property and casualty insurance (P&C), one of the most pertinent dilemmas is the estimation of the insurance premiums required to accommodate potential losses. Due to the inherently random nature of losses, one of the tools utilized within P&C insurance is credibility theory, which utilizes a weighted average between past portfolio data and the portfolio’s general risk class. This allows insurance companies to give greater weight to more reliable data and theoretically increase the accuracy of their premiums.
However, the numerous options for a portfolio’s risk class, also denoted as the complement of credibility, create a subjective pricing model that may not be optimal for a given portfolio. Currently, the complements of credibility that are employed are finite and limited to traditional criteria. Therefore, generating a model capable of optimizing the complement of credibility may be beneficial by using more data from separate risk classes. Gathering data from various business units and portfolios could generate a prediction model that calculates a custom complement for each portfolio. This would increase the general relevancy of the complement of credibility and the associated profitability while ensuring that insurance premiums are more accurate and reliable.

Faculty Supervisor:

Anas Abdallah

Student:

Partner:

Co-operators (General Insurance)

Discipline:

Mathematics

Sector:

Finance and Insurance

University:

McMaster University

Program: