Franklin Templeton Combines Award-Winning Research with Machine Learning in New Goals Optimization Engine

The new technology solution enables goals-based wealth management at scale

Franklin Templeton announced the introduction of its proprietary Goals Optimization Engine, or GOE^TM. The global offering provides investors with personalized investment paths for their unique goals, and allows financial professionals a scalable way to offer a differentiated investment solution and deepen client relationships. The Engine is built based on 2018 Markowitz Award winning proprietary research that defines investment success by whether or not the investor’s goals are achieved, recommending investment decisions that will help maximize that chance of success. Banks, advisers, financial professionals and defined contribution plans can leverage the technology to help provide better outcomes to their clients while gaining business efficiencies.

Read More: Can These Customer-To-Customer Marketing Platforms Come In Handy To Boost Your Business Goals?

“A New Approach to Goals-Based Wealth Management”

“We are seeing an increased demand for goals-based planning and personalized investment solutions globally, and the application of machine learning is enabling what was previously unimaginable,” said Jed Plafker, EVP, Global Alliances and New Business Strategies. “As society generally moves towards digital platforms and technology-based services, GOE is the technology that will enable advisers and financial services firms to deliver personalized, higher value services at greater scale.”

Read More: Moz To Update Proprietary Page Authority Metric To Better Predict Ranking Ability

GOE, a patent pending process, combines a proprietary algorithm based on award-winning research, detailed capital market expectations, and a set of parameters for each goal provided by the investor. GOE is designed to take these parameters and optimize the asset allocation to maximize the probability of successfully achieving the goal by applying machine learning. This optimization process occurs regularly through the time horizon of the investment and re-allocates assets to increase or decrease risk in the portfolio as needed. GOE will de-risk as the goal date approaches versus reaching for a higher return, with higher risk. GOE can also facilitate decisions across goals with different priorities.

Read More: Digital Leapfrogging: Is Your Online Business About To Get Jumped?

 Write in to psen@itechseries.com to learn more about our exclusive editorial packages and programs.