In the September 2015 edition of Australia ReView, in his article “Lessons from the Recent Australian Group Market Experience,” Colin Yellowlees, Chief Pricing Actuary for RGA Australia, commented that “without good data we are flying high up in the clouds with only the occasional glimpse of what’s happening on the ground.”
This was written from a risk management perspective, but also rings true for those of us involved in the financial reporting process, in particular for valuation actuaries.
A key focus of the valuation function is the calculation of policy liabilities in respect of business that an insurer or reinsurer has on its books and to reserve appropriately to meet these liabilities. The policy liability must provide for:
- a best estimate value of the liability of the company in respect of obligations
under life insurance contracts; and
- a uniform emergence of profit in respect of life insurance contracts.
To calculate policy liabilities, a valuation actuary needs to understand the insurance contracts that the insurer has written and make estimates about the future expected financial performance of those same contracts. This calculation process typically involves projecting key financial information such as premium income, claims outgo and expenses for the lifetime of each policy, and calculating reserves in line with relevant accounting standards.
So what does a valuation actuary use to inform these projections?
Policyholder and claimant data is the key input to the process. In particular, there are two major sets of data that an actuary needs:
- Current exposure data. This gives the actuary information on the types of contracts that have been written at a policyholder level, including policy information such as the benefit type (e.g. life cover, disability income) and demographic information for the policyholder (e.g. age, gender, smoking history).
- Historical exposure and claims information for the portfolio. This allows the actuary to analyse experience in historical periods such as claims incidence and termination rates (where relevant), as well as lapse rates and other experience items. This historical experience can then be used to inform assumptions about expected experience in future periods.