As big data continues to increase, the insurance world seems to be moving towards more individualistic, granular pricing.
Consider ZhongAn, in China, for example. As an innovative digital insurer, utilising big data to its full extent, they are focused on micro-pricing premiums in a very personalised way. With more than 460 million customers acquired over the last 3 years, their digital and individual risk assessment approach seems to be working.
But is this move towards more individual risk-pricing a good thing from a societal point of view?
The insurance industry has traditionally claimed that not being able to accurately price insurance products, with regard to individual risk, will lead to the adverse selection spiral as the low-risk individuals in the pool reduce or forgo their insurance.
However, UK actuary, Guy Thomas, challenges this orthodox view by contesting that some adverse selection (and hence some restrictions on risk classification) is beneficial from a public policy perspective.
I have reviewed his, recently published, Loss Coverage book for The Actuary magazine.
Do you agree that some adverse selection (and hence restrictions on risk classification) may be beneficial for society as a whole? Or should insurers always be aiming for an “actuarially fair” premium?
