Critical Illness Experience Studies
Once an insurance product is developed and sold, the next challenge is maintaining profitability while remaining competitive and relevant to the needs of the market. As a comparative newcomer to the U.S. life insurance industry, Critical Illness products remain in flux with numerous design changes and enhancements over the past several years. In order to determine if the pricing assumptions used were correct, the actuaries must conduct ongoing experience analysis. For this blog we've worked with Cyriac Kottoor (our CI pricing actuary) and Lin Lin (our CI valuation actuary) to highlight an example of experience analysis.
A bit of background: Any analysis should go beyond a simple actual-to-expected comparison on a product performance level, but must delve deeper to determine if each of the individual pricing assumptions are tracking correctly. This enables the carrier to identify and correct for areas of concern or expand and enhance the product when components exceed expectations. For this discussion we are focusing on the study of specific benefit eligibility triggers as they relate to gender distribution, which is one of many assumptions that should be validated on an ongoing basis.
This example uses population incidence rates without factoring in underwriting selection or consumer anti-selection. We assume coverage was issued with unisex rates, and we will focus on distribution extremes to better illustrate the issue.
Scenario #1: A Critical Illness insurer has an in-force block of policies with an attained age of 47. Cancer claims (the primary cause of CI claims) have been running better than expected. In fact, the current year experience is running 38% better than anticipated (2.73 cancer claims per 1,000 lives rather than the expected 4.39 claims per 1,000 lives). This has been an ongoing trend and has been improving each year since issue.
Although this analysis suggests favorable results, more work must be done before releasing news about profits or lowering renewal rates. Further investigation shows that the insurer anticipated sales would be 100% female; however, they were actually 100% male (these extremes are used to illustrate the issue). When we review gender-distinct cancer rates (Table 1) we see their results are actually what would be expected with 100% male distribution. What’s worse is that male and female cancer incidence rates converge by age 53 and then males begin to rapidly outpace females. With 100% male distribution, we would expect cancer claims to be running 65% higher by age 74 than initially anticipated.
Without a detailed experience analysis, this problem could be undetected for years, thus limiting the insurer’s options to correct the problem.
Scenario #2: A Critical Illness insurer has an in-force block of policies with an attained age of 48. While conducting its experience analysis, the insurer notes that Heart Attack claims (the second most common claim cause) were running a bit better than expected.
They had originally assumed a 50/50 gender split and expected 2.36 claims per 1,000 lives at age 48. Instead, they have been running short of that and the spread has been widening each year. Heart attack claims were 25% lower than expected in the current year. Upon further investigation, the insurer learned that the actual gender mix was 75% female and only 25% male and that the claims incurred were consistent with that gender distribution.
Unlike the cancer case described in Scenario #1 where the incidence by gender would converge and eventually flip, Table 2 shows that females are expected to have lower heart attacks rates than males at nearly all ages and that the spread would only expand over time. This knowledge can be used as an additional pad to ensure adequacy of reserves or to offer more competitive rates.
These examples, while simplistic in nature, highlight the importance of segmenting experience analysis in order to get a true understanding of the business. In addition to the actual benefit eligibility triggers, detailed experience studies should be completed on age, gender, duration, selection factors, lapse rates (both voluntary and mortality), and claim lag time. Only with in-depth analysis can an insurer be able to truly project the long-term experience of the business and make the appropriate changes when experience misaligns with initial assumptions.