Thanks for the feedback Ben!
I did not explain this very clearly so that is one thing to add a bit more knowledge of.
Each row represents one year of business, so all the policies that are sold in 1988 are put into the 1988 cohort. The premium value is the volume of premium received for that business. The reason it is increasing down the years is that this particular insurer sold more and more as the years passed.
The ten column after that are the cumulative amount of claims paid out on the policies of that cohort: in insurance, depending on the types of risks, it can take many years for an insurer to learn exactly how much liability was incurred on policies written in any particular year. This may be because the claim was not reported or known about, or simply that the claim was working through the legal system and so the final amount due was not determined.
As a result, for a given year, the cumulative amount up to that year is shown and that is why those numbers tend to increase and then taper off as more and more is learned about the claims on a given year.
This is also why the data is represented like an upper triangle - we only have five years worth of development on claims for policies written five years ago.
I need to explain this much better in the case study!
It would not be a huge deal for future years as most business will focus more on the loss ratio and then scale that to the amount of premium. In most cases, insurance business will budget for a certain amount of GWP (Gross Written Premium) as part of their planning.
Yeah, I’ll get rid of a lot of them. I had just discovered
bayesplot so it was more about me playing with the new toys in that section I think. :)
Yes, that is a good point, I’ll look into adding that.
I don’t believe so. I’m not fully knowledgeable on the actuarial theory here, but I do remember reading that the Weibull tends to give fatter tails on the estimates and as a result tends to yield higher estimates of loss ratios. That was one of the things I was planning to investigate a little further in future case studies as I was conscious there is already a reasonable amount of content in this and I think it makes more sense to break it all up.
Thanks for your help Ben, I’ll make some edits today and tomorrow and repost.