How AI Is Expanding The Applications Of Robo Advisory
For the last couple of years, Artificial Intelligence (AI) has been changing many fields and increasing efficiency by using improved datasets. One of those areas where AI has accelerated evolution is the robo-advisory, which is a field having extensive financial big data to analyze.
Robo-advisors are the systems that use algorithms to automatically perform investment decisions or tasks which are mostly done by human advisors. “Robo advisors are a potential solution to the complexities of financial decision making,” said Jill E. Fisch, a law professor at the University of Pennsylvania at a conference of Pension Research Council.
In the main scheme, robo-advisors are merging customers’ information such as their financial goals, risk tolerances, timeframes, with the right asset allocation that qualifies customer’s needs. While making this merge, they use many algorithms including machine learning models to create the best fit for the customer. In the process of timeframe, they take lots of actions as well such as rebalancing the portfolio or performing tax-loss harvesting. This automatically increases efficiency while taking decisions at the right time for the portfolio.
AI usage in enterprises
Numerous enterprises have started to use AI in the robo-advisory field. Betterment is one of these robo-advisor enterprises that uses AI to reduce taxes on transactions where machine learning algorithms select the specific tax consequences of the transactions. Similar to Betterment, SigFig also uses its AI engine automatically to allocate assets and determines which investments will result in minimum taxes.
Another enterprise that uses AI is Wealthfront. Former CEO Adam Nash says, “We’re firm believers that artificial intelligence applied to your actual behavior will provide far more powerful advice than what traditional advisors offer today.”
Also, Fidelity has already started its robo-advisory service in 2016 as Fidelity Go and as the beginning of 2019, Fidelity Go took top ranking as the best overall robo-advisor in the 2019 winter edition of The Robo Ranking report from Backend Benchmarking.
The biggest impact of AI might be the time-saving base for human advisors. With AI’s deep learning capabilities which relieve advisors from having to perform much of the rote or mundane monitoring and administrative tasks that currently allocate a significant portion of their time. When allocations fall outside of certain parameters for the specific clients, an AI-based system can trigger it into the monitor of the human advisor.
To increase efficiency, AI requires vast amounts of data to give more accurate results. “Analysis of vast quantities of historical and financial data will uncover alpha opportunities that traditional analysis would otherwise overlook and give robo-advisors an edge over passive strategies and AI can process big data swiftly, allowing robo-advisors to adapt to changing market conditions and consumer behaviors much quicker in order to make better investment decisions. Time saved is key here,” says John Zhang, founder of a robo-advisory startup WealthGap which explores AI in hedge funds-like portfolios.
Enterprises that offer robo-advisory services may not abandon the human component completely, but it seems the adoption of artificial intelligence is enhancing the platforms and they will be more able to give clients the big picture in the course of time.
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