I wrote a quick script to backtest one particular method of deriving claims inflation from loss data. I first came across the method in 'Pricing in General Insurance' by Pietro Parodi , but I'm not sure if the method pre-dates the book or not.
In order to run the method all we require is a large loss bordereaux, which is useful from a data perspective. Unlike many methods which focus on fitting a curve through attritional loss ratios, or looking at ultimate attritional losses per unit of exposure over time, this method can easily produce a *large loss* inflation pick. Which is important as the two can often be materially different.
Source: Willis Building and Lloyd's building, @Colin, https://commons.wikimedia.org/wiki/User:Colin
In which we recreate the previous analysis, but in Python this time. And then add a new submission using Mean rather than median to impute missing values.
I work as an actuary and underwriter at a global reinsurer in London.