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
SAN MATEO, Calif.--(BUSINESS WIRE)--Franklin Templeton today announced the introduction of its proprietary Goals Optimization Engine, or GOETM. 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.
“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.”
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.
“Franklin Templeton’s digital distribution initiatives are focused on innovation, speed and agility to deliver value added services and personalized investment solutions to our financial professionals, advisers and investors,” said Harshendu Bindal, director of Digital Strategy and Wealth Management for Franklin Templeton. “GOE uses machine learning to scale up to the size of any practice and manage any number of portfolios for an investor. This technology provides the adviser or financial professional the mechanism to deliver personalized wealth management across their entire practice.”
GOE’s open-architecture offering will be delivered through AdvisorEngine, Franklin Templeton’s recently acquired platform. Franklin Templeton is also building relationships with third-party technology companies and financial institutions to enable access around the world. One such relationship is with NextCapital, with whom GOE will be brought to market as part of a discretionary managed advice solution for the defined contribution industry. GOE is one in a series of investments Franklin Templeton has made along the wealth tech ladder.
Franklin Templeton has expanded its offerings beyond traditional investment products to now include planning and advice, digital tools, and advisor and retirement platforms. The expanded offerings weave active investing strategies and advice into turnkey digital platforms, enabling financial planners to efficiently run their businesses with custom optimized portfolios in an open-architecture environment.
A paper detailing the academic research behind GOE, “A New Approach to Goals-Based Wealth Management,” by Sanjiv R. Das, Daniel Ostrov, Anand Radhakrishnan and Deep Srivastav received the 2018 Harry M. Markowitz Award from the Journal of Investment Management and New Frontier Advisors, LLC. This is an annual award honoring Dr. Harry M. Markowitz, a Nobel laureate in economics, for his legacy and to support future research and innovation in practical asset management. Candidates are taken from among papers published in the Journal of Investment Management each year.
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