Tag Archives: Rebecca Connell

Changing a pension formula for success

Over the last five years, Milliman has worked closely with a pension administration client to transition its 30,000-participant defined benefit plan to a novel solution that continued to offer ongoing benefit accruals to participants, while maintaining cost-efficiency and contribution stability.

The plan, established in 1987, offers a cash balance formula as its primary benefit. When the client chose Milliman, it wanted to modify the cash balance formula to achieve both plan health and cost-efficiency. During a subsequent consulting session, it was determined that the plan’s goals could be better met by moving to a variable annuity benefit formula.

In this study, Tim Bernazza and Rebecca Connell discuss the administrative effects of changing to a variable annuity formula for this pension administration client.

Addressing problematic defined benefit data

Defined benefit (DB) plan sponsors often express how complicated their plans are to administer.  Many times, the reasons are due to unique plan provisions, but more frequently are related to missing and/or incomplete historical participant data.  As plan sponsors move to outsourcing their DB plan administration, these data issues need to be addressed during the conversion process. These issues directly affect the efficiency of the ongoing administration.  Tackling data issues can become overwhelming, but with thoughtful planning and consulting, data concerns can be mitigated, if not eliminated for the most part. 

What exactly is bad data?   A common request during the vendor search process is to have the plan sponsor provide some indication of the state of the data.  The initial reaction to this request is typically along the lines of “we have data and our actuary is using it to produce a valuation report each year, so what’s the concern?”  Actuarial quality data and administration quality data are not the same.  During the annual valuation process, the calculations used to determine the plan liabilities are based upon approved actuarial assumptions and are not designed to be exact; they only need be reasonable, justifiable, and within defined parameters. By contrast, individual participant calculations are based on the plan’s formula and historical data specific to each participant.  The participant is relying on these calculations to make an important financial decision; as such, it is critical and that the data used for benefit calculations be accurate.

“Bad” data can cause problems. For example, bad data could result in the participant receiving overstated or understated amounts when modeling benefits or when electing to begin receiving benefit(s).  The data needed to calculate a benefit that can be “bad” is specific to each DB plan.  The data elements frequently found to be problematic include historical salary and hours needed to calculate accruals as well as employment status details needed for service calculations.  This is true for both traditional and cash balance plans.  In the event the plan has had other plans merged into or divested from it, the historical participant indicators, grandfathered benefit amounts, and identification of deferred participants and vested amounts can be lost.  For plans that have existed for a long time, certain data elements may only exist on paper files in a basement storage space or another questionable location.  It is no wonder plan sponsors are anxious when this question is posed or do not fully understand the importance of a candid response. 

How can we clean it up?  Identifying what you don’t know is impossible; however, the annual valuation data is a good place to start.  This file will include any participants used to determine the plan’s liability and should list all participants actively accruing a benefit, those with a deferred benefit, and those currently receiving a payment from the plan.  This can be the starting point to determine what might be missing or incomplete.   Your actuary can also document the assumptions being used and can often indicate missing underlying data needed for calculations and provide direction on how to determine a value for these participants.   

Analysis of the data elements used in your calculation can be extracted from the valuation file.  If this review is taking place during a conversion to a new administrator, your conversion team can identify which participants are missing data elements and which elements could be suspect.  Once identified, data can be collected from other sources to be aggregated and used within the calculation.   Participants can also be flagged to indicate data is missing that must be collected before any projected benefits are presented to the participant.  For deferred participants missing an accrued benefit amount, once the data is identified, the benefits can be calculated and subsequently certified.  This clean-up process can be performed in conjunction with or following a conversion project, depending on resources.

Data challenges are common. Many DB plans have experienced the transition from paper-based data to digital data and, as a result, the plan’s underlying data has likely existed in many forms over time.  Once the issues are clearly identified, the results of the clean-up effort will benefit both the participants and the plan.  The data review could identify administrative errors that lead to the need for Voluntary Compliance Program (VCP) updates or the identification of missing participants, which will be beneficial to identify early for valuation purposes and compliance concerns in preparation for plan termination. Spending the effort to address data issues provides confidence in ongoing administration of the plan and will allow for more accurate valuations in the future.