AI, Financial Services

The Robo-Advisor Revolution

The digital age has brought us countless innovations to make our lives more productive and efficient. From cars that can take the humiliation out of parallel parking to synced home security systems that keep our loved ones safe and secure, technology has changed our modern lives in ways we likely take for granted.

In the financial management space, innovation has made similar strides in how money is managed. By aligning portfolios to best match risk tolerances, behaviors and financial goals, no matter how large a client base might be, machine-learning technology has lent a systematic, highly customized and incredibly streamlined approach to investment management.

Commonly referred to as "robo-advisors," this integration of algorithmic-based efficiency with the precepts that define modern portfolio theory, is a relatively new technology that has quickly found a robust position within the already crowded investment landscape.

As Baby Boomers retire and Generations X, Z and Millennials occupy more of the target client base each day, these technologically-savvy investors seem both willing and able to fully embrace the robo-advisor revolution, assuring its rightful place within the industry for decades to come. Given the inherent nature of machine-learning, the efficiency and effectiveness of robo-advising platforms will only continue to grow with time.

History of Robo-Advising

There's no definitive history of robo-advising as it has evolved over time, just like many other technologies. Even the term robo-advisor itself has a somewhat ambiguous beginning, likely stemming from a 2002 research paper that discussed the future of investing in the aftermath of the Enron scandal, calling for a more efficient approach to risk and modern investing.

No matter the exact origin, robo-advising began to take a firm root in the industry in 2008, when a handful of fund managers decided to employ basic data-driven concepts into the management of the ever-growing target date funds that were already gaining popularity in retirement and college-based funds. Initially, robo-advisors took care of the straightforward, passive investing responsibilities of rebalancing funds to match investor risk tolerances.

By 2010, however, the significant potential seen in machine-learning technology was embraced for a more active asset management approach. With its ability to leverage the immense information in big data while learning and refining its knowledge-base and skill set with time and further use, machine-learning proved to match extremely well with investment platforms of all shapes and sizes.

In fact, robo-advising has grown exponentially since its nascent stages just a handful of years ago. Total assets under management (AUM) by the various robo-advisor platforms is estimated to reach $8 trillion by 2020, at which point nearly all forms of investing will be at least partially automated with machine-learning technology.

 

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Benefits of Robo-Advisors

Of course, all sorts of technologies come and go without so much as an afterthought from industry. Only the truly innovative advancements gain lasting traction and become a part of collective industry best practices. Robo-advisors and their machine-learning engines certainly qualify as a true innovation, appealing to management firms for a variety of different reasons.

Effortless Diversification

Although proper portfolio diversification across the various asset classes has long been seen as a salve against volatility, the explosion in the sheer number of different assets now available to nearly every investor class has made management of highly diversified portfolios unwieldy for even the most streamlined traditional management models. By utilizing a robo-advising platform, however, management companies can offer investment portfolios that are constantly rebalanced across as many asset classes as needed, never being susceptible to the inevitable inefficiencies of human error.

Furthermore, additional complex strategies can also be better employed through algorithmic-based robo-advisors, including everything from year-end tax harvesting to green investing. Such strategies put even more importance on rebalancing and targeted trading to meet specific needs and demands of investor classes, a notion that lends itself extraordinarily well to the computational abilities of a robo-advising platform.

Evolving Personalization

While investment strategies are extremely quantitative in nature, relying on metrics and measures to provide growth to a portfolio, investing itself will always be prone to the complexities of human behavior. Management firms have long used risk tolerance questionnaires to develop the most suitable portfolios for clients relative to risk, time, and personal financial data. However, those questionnaires lack the ability to integrate the smallest nuances of investor behavior in a meaningful capacity.

Since machine-learning is a subset of artificial intelligence, it has the remarkable ability to refine investment strategy over time, being able to integrate those nuances in real-time within portfolios. In other words, robo-advisors can evolve along with the needs and goals of a client base, even down to the individual investor. This is a skill that traditional management firms have historically struggled with, at least with a significant degree of immediacy.

Improving Client Satisfaction and Revenues

Management firms have always looked for the most ideal combination of client satisfaction and expanding AUM to generate revenue growth. A roster of even the most seasoned and efficient investment professionals can struggle balancing portfolio maintenance and client service with an active and thorough effort to increase AUM.

Robo-advising platforms provide management firms the best of both worlds, allowing the highly efficient machine-learning systems to handle the time-consuming task of portfolio maintenance while giving their human advisors the opportunity to spend time with the client base and actively pursue additional assets. Both client satisfaction and the bottom line benefit with the integration of robo-advising platform.

At Xinn, we are constantly monitoring and embracing technologies that can make operations more efficient and effective for partners. Innovations like machine-learning in robo-advisor platforms demonstrate the type of advancement that can redefine the financial services industry. Between robo-advising, encryption and security technologies, cloud-based computing and our own innovative platform, Xinn is proud to be a part of the technological revolution that is permanently changing the way business is conducted for the better, a notion that both Xinn and our partners enthusiastically embrace.

About the Author

Jeff Williams is a Customer Advocate and Director of Content and Design at Xinn. His main passion is to design next generation communication technologies that match the direct needs of today's complex business and financial world.

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