Over the last decade, the wealth management industry has shifted away from financial advisors being incentivized by commission-based and transaction generated fee structures in favor of a more holistic approach. Given a regulatory push for fee transparency, clients today typically are charged an annual advisory fee structured as a percentage of their total portfolio account assets under management, within defined asset-based tiers. While this may seem simple, given fee structures vary across products, getting a top-line view of fees at the client level and benchmarking fee tolerances to attract and retain clients can be challenging. Wealth management firms, their advisors, and their supervisors must determine what constitutes “fair” fee levels, what a financial advisor should propose to a new client for the highest chance of conversion, and when to increase or decrease fees. Advisory fee tiers vary from firm to firm, and therefore, there are no standards or industry-wide insight into fee thresholds and tolerances.
Synechron’s Wealth Tech Accelerator for Pricing Insights employs a data analytics-driven engine that culls the intelligence necessary to optimize client fee pricing across an organization. It collects and organizes data, then produces advanced, graphical, data visualizations, including an advisory fee distribution curve which demonstrates minimums, medians and maximum fee levels. Pricing insights are displayed for the historical Trailing Twelve Months (TTM) across a client, a household, or based on other criteria. A multi-tiered, multi-user dashboard displays critical data. Further, Synechron’s technology provides valuable bi-level tools for both wealth managers and Supervisory Staff and empowers Advisors to track and benchmark individual performance vs. a selected peer group. Predictive Analytics lets wealth managers quantify the possible impact of fee changes against the probability of a positive or negative client relationship result (e.g., incremental revenue capture/loss, attrition, etc.). Revenue projections offer a modifiable model to gauge the impact of fee changes on future portfolio revenues.