You have two parts to your answer: data and noise.
Someone could fit a line, or a spline to the mean of the data and if they were honest they would say something like "given this model, the mean tendency is ...".
There is variation from the mean. It looks like a 3 million dollars claim got 0. If you had a line fit to predict the amount you might get a number like 3 million. When someone has a claim for anything above 7.5 million dollars the departure from the linear fit collapses.
First things first - I don't like your coordinates. They should be log-log. This would more clearly show the regions with the majority of the data. Your y-axis should be the claim de-rating. $$ y = claim - actual $$ or the log base 10 thereof.
Second - you want to account for variation. You should be able to put a +/- 95% confidence interval around whatever someone asserts as the mean de-rating given a particular value of claim.
Third - there is a clear difference between the 7.5 million dollar claim and the 3.0 million dollar claim. Access to a variable like "how much did you pay your lawyer" or "which firm did you use" might allow clustering of the claims into higher and lower yield buckets. You would then say "if you use x as lawyer and you agree to pay them y, then there is a 95% chance that if you claim z then you will actually get z-thisvalue."
A plot in the form requested (log-log, and transformed y-coordinate) is more likely to result in a resonable distribution. Could you make it one that looks like this? (link)
Best of luck.
PS: I bet a power law governs here. (link)
PPS: And Peter is a genius. If he suggests something and you can figure out how to do it - he is very likely to have given an insightful and informative answer.