AFS looks at funding analysis as a logical next step that follows plan design. Also, it is an ongoing process that must start off on the right foot in order to be effective. Once the benefit plan has been developed, we will model its financial impact and present alternatives for meeting the resulting financial obligations. The analysis will compare several methodologies and funding vehicles on a level playing field using the assumptions and parameters specific to the actual plan.
AFS will prepare detailed financial models that incorporate both qualitative and quantitative metrics for analysis, so that the employer sponsor can determine which vehicle best fits their needs based on the projected financial impact of the plan and its funding. The AFS design team is highly skilled in financial analysis and accounting and can assist in communicating and explaining the results to the consultant and the sponsor’s financial staff.
Our design and analysis utilizes the latest versions of our proprietary AFS Master System software. This financial modeling tool goes beyond the capabilities of a spreadsheet, is unmatched in the industry, and we use it exclusively for two important reasons.
First, by using our own technology we can create funding solutions that are usually impressively superior compared to what can be obtained anywhere else. We have developed algorithms to find the blend of asset classes (whether COLI, mutual funds or fully-taxable investments) that is objectively the best possible in accomplishing the explicit goals and objectives, as given by a corporate CFO. Our proprietary funding algorithms have many layers of optimization, including innovations such as Optimal De-MECing (minimizes the cost of keeping an insurance contract NON-MEC), Globally Optimal Aggregate Funding (finding the optimal allocation factors for funding benefits across a group), Survivorship-Adjusted Aggregate Funding, use of Death Benefit Side Funds and/or Economic Rabbi Trusts. Without using our own technology, we could not produce such outcomes, and trying to approach these solutions would take far greater time and effort.
A second reason we use our own technology is to maintain the integrated nature of our processes, which contribute to our high service quality, while also passing the scrutiny of experts who invariably will review our work. The alternative is to cobble together spreadsheets and non-optimized models in order to support the analysis with a time consuming and error-prone process. Furthermore, we must preserve the initial plan design and funding parameters so that they can be re-optimized during the annual plan funding review, which is critical to keeping the plan working effectively over time.