Join us on Friday, April 22 in 103A Walker Hall to hear Nii Okine speak on...
Title: Ratemaking in a Changing Environment
Abstract: In pricing insurance contracts for non-life insurers, the literature has mainly focused on using detailed information from policies and closed claims for multivariate analysis. Information regarding open claims from Reported But Not Settled and Incurred But Not Reported claims are usually ignored. However, the information on open claims can reflect shifts in the distribution of the expected claim payments better than closed claims. Such shifts may be needed to be reflected in the ratemaking process earlier rather than later, especially when insurers are experiencing environmental changes. Therefore, ignoring open claims during the ratemaking process may lead to biased estimates and, consequently, inaccurate premiums. This paper presents an intuitive ratemaking model, employing a marked Poisson process framework, which ensures that the multivariate risk analysis is done using all reported claims and makes an adjustment for Incurred But Not Reported claims based on the reporting delay distribution. Using data from the Wisconsin Local Government Property Insurance Fund, we find that by determining rates based on current data, the proposed ratemaking model leads to better alignment of premiums and provides insurers with a more profitable portfolio.
Abstract: In pricing insurance contracts for non-life insurers, the literature has mainly focused on using detailed information from policies and closed claims for multivariate analysis. Information regarding open claims from Reported But Not Settled and Incurred But Not Reported claims are usually ignored. However, the information on open claims can reflect shifts in the distribution of the expected claim payments better than closed claims. Such shifts may be needed to be reflected in the ratemaking process earlier rather than later, especially when insurers are experiencing environmental changes. Therefore, ignoring open claims during the ratemaking process may lead to biased estimates and, consequently, inaccurate premiums. This paper presents an intuitive ratemaking model, employing a marked Poisson process framework, which ensures that the multivariate risk analysis is done using all reported claims and makes an adjustment for Incurred But Not Reported claims based on the reporting delay distribution. Using data from the Wisconsin Local Government Property Insurance Fund, we find that by determining rates based on current data, the proposed ratemaking model leads to better alignment of premiums and provides insurers with a more profitable portfolio.