Abstract
Autocalibration is a desirable property since it ensures that the information contained in a candidate premium is used without any bias. It turns out to be intimately related to the method of marginal totals that predates modern risk classification methods. The present note aims to assess the impact of autocalibration on the goodness of lift. It is shown on a case study that autocalibration does not only restore global and local balances but also improve lift.
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Published version (
Free)
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
