Are you maximizing your COLI plan results?
By Ken Godfrey
Life Insurance Financial Evaluations, LLC
Many corporations and financial institutions purchase corporate owned life insurance or bank owned life insurance to informally fund nonqualified benefit plans and offset the cost of providing these benefits. These life insurance portfolios can consist of just a few policies or hundreds of policies and are often referred to as multi-life cases.
The total premiums paid over the life of a COLI plan can easily reach tens of millions of dollars, and therefore ongoing policy service should not be taken lightly.
The design of these multi-life policies is often done on an aggregate funding basis. Aggregate funding means the life insurance policies are viewed as a group and premiums are distributed across the portfolio on a pro rata basis. The policies are designed based on an estimated premium schedule over the life of the policy and corresponding level of death benefit. The reasons given for using aggregate funding include obtaining guaranteed issue underwriting and simplifying policy service.
Aggregate funding is a beneficial way to design and issue a portfolio of life insurance policies. However, a potential pitfall of aggregate funding is some brokers oversimplify the policy service and manage the policies on a pre-determined basis. Particularly for COLI plans, this creates an environment where the policy performance can easily veer off course.
Future premium expectations may be very different than originally expected and are often dependent upon uncertainties such as benefit plan results, employee participation and participant turnover. In addition, for some COLI investment plans, the policy account values are allocated based on participant elections and/or designed to access the policy account values to provide cash to make future benefit payments.
Since the life insurance performance is so dependent on the benefit plan results, it is vital to determine which policies are not performing as well as others and make adjustments accordingly. Therefore, policy service for multi-life cases is not as simple as running a composite illustration showing the results based on a mortality assumption for each insured (e.g., age 85) and allocating future premiums pro rata.
During periodic plan reviews, the policies should be analyzed based on several iterations of hypothetical results. These iterations should include running the inforce life insurance illustrations based on various interest crediting rates and future premium levels.
Each iteration should include a hypothetical performance summary of each insurance policy at various mortality ages such as age 80, 90, and 100. The primary purposes of the analysis are to:
1) verify there are no policies projected to lapse prior to maturity,
2) develop a rationale to efficiently allocate future premiums payments (and withdrawals/loans from cash values for benefit payments) to enhance the overall portfolio return, and
3) highlight the sensitivity of various assumptions on expected plan results.