The AdvisorEngine platform has many strengths. It’s CRM module, which derived from Junxure CRM is one of the most robust, industry specific CRM applications available today, and it is competitively priced for the capabilities it provides. When it comes to portfolio management, AdvisorEngine is also highly competitive. It offers portfolio management and accounting, data consolidation, rebalancing, fee billing, and more. Other features of note include a robust client portal, digital onboarding, business intelligence dashboards, and document management.
If there is one area that AdvisorEngine has been historically weak, it is in the area of financial planning. AdvisorEngine began addressing the issue a few years ago with the release of their Goals Optimization Engine, and they have recently made some significant improvements that take this module to the next level.
The updated AdvisorEngine Goals Based Optimization Engine allows for BOTH the traditional Goal-Based Planning (GBP) model, and a new AdvisorEngine GBP Dynamic Workflow.
The Advisor can use the more traditional Goal-Based Planning approach that aligns investment decisions with the individual goals of clients while ensuring that the plan is consistent with the clients’ personal risk tolerance. Or the advisor can develop a personalized Dynamic Glidepath for the client, adjusting the asset allocation of the portfolio over time. Unlike static allocations, a dynamic glide path adjusts the portfolio’s risk profile based on various factors such as market conditions, changes in the investor’s financial situation, or proximity to a specific goal. For example, in retirement planning, a dynamic glide path might involve gradually reducing a portfolio’s exposure as the client nears retirement age, and then potentially increasing it again during retirement. Both models are available to both the Advisor and the Client in the updated AdvisorEngine portal.
AdvisorEngine GBP Core Workflow: In the traditional static model, the client answers the risk tolerance questionnaire, the advisor confirms the risk tolerance with the client, and then selects an appropriate portfolio model that aligns with the risk score. The client provides goals, for example funding retirement, and funding sources are identified. A Monte Carlo simulation is run, this results in a projected wealth path graph, a probability score, and a plan details summary as illustrated below.
Projected Wealth Path Graph (GBP Core and Dynamic):
AdvisorEngine GBP Dynamic Workflow: RTQ >>> In the dynamic model the workflow begins in a similar fashion; the client answers the risk tolerance questionnaire, the advisor confirms the risk tolerance with the client. The client then provides the goals and funding sources. Next, a Dynamic Programming Simulation is run. This results in a Projected Wealth Path Graph and a Probability Score. The software then proposes an initial asset allocation model and glide path. The major difference here being that the proposed asset allocation and glidepath is preliminary. Both can change over time dynamically as circumstances change. So, for example, if 15 years into a 30 year plan, due to outstanding market performance, the software shows that the plan is now projected to greatly exceed the funding needed to achieve all goals, it will dial back risk to protect against future adverse consequences.
Glidepath Graph (GBP Dynamic only):
Which Method Is Best?
Both the traditional method and the dynamic method can be highly effective. Fans of the more traditional approach will argue that good advisors review clients’ portfolios on a regular basis and make adjustments as necessary, thereby achieving results similar to the dynamic approach. While this does in fact happen within many firms, there are a number of factors that work against it. It takes more manual advisor interaction and it requires more time with clients to explain why changes are being made, both of which take time and cost in terms of resource. With the dynamic model, client expectations are set at the outset, and much of the work is done by the software. That said, advisors and clients can review the proposed changes and alter them if desired.
We believe that over time the dynamic glidepath approach is likely to be adopted by a larger population of advisors as it can help them more fully automate their businesses and lead to better client outcomes, however, the fact that Advisor Engine makes both options available to advisors differentiates them from most competitors and makes to offering a very appealing option.