Yes. I have tried using the standard deviation of the expected claim as a pricing factor for week 6&7. I actually thought it would be my killer for this game…
price = 1.15E(claim) + 0.01Std(claim)
However, the result was quite bad (but at that time i do not have large claim detection model). I decided not to go with this approach since week 8, only 3 weeks left and i still have a profit loading to test… cant afford to add more variables in my pricing strategy.
The implementation that I was referring to is:
Going one more step forward, there are actually two types of uncertainty,
a) enough data but the target variable itself is having high variance
b) not enough data so that the model can just extrapolate
The conditional variance measures (a) only, where for (b), i think most of us just manually add some loadings to the policies that we are not willing to take due to limited data. This can be solved by training a baynesian network (for example by tensorflow-probability), but I didnt have time to try it.
I will tidy up my code and share with everybody soon.