The robots are here. Not the humanoid versions that you see in Hollywood movies, but the invisible ones that are the brains behind what look like normal online front-ends. They can educate you, advise you, execute trades for you, manage your portfolio and even earn some extra dollars for you by doing tax-loss harvesting every day. These robo-advisers also are not just for do-it-yourself or self-directed consumers; they're also for financial advisers, who can offload some of their more mundane tasks on the robo-advisers. This can enable advisers to focus more on interacting with clients, understanding their needs and acting as a trusted partner in their investment decisions.
It's no wonder that venture capital money is flowing into robo-advising (also called digital wealth management, a less emotionally weighted term). Venture capitalists have invested nearly $500 million in robo-advice start-ups, including almost $290 million in 2014 alone. Many of these companies are currently valued at 25 times revenue, with leading companies commanding valuations of $500 million or more. This has motivated traditional asset managers to create their own digital wealth management solutions or establish strategic partnerships with start-ups. Digital wealth management client assets, from both start-ups and traditional players, are projected to grow from $16 billion in 2014 to roughly $60 billion by end of 2015, and $255 billion within the next five years. However, this is still a small sum considering U.S. retail asset management assets total $15 trillion and U.S. retirement assets total $24 trillion.
What has caused this recent "gold rush" in robo-advice? Is it just another fad that will pass quickly, or will it seriously change the financial advice and wealth management landscape? To arrive at an answer, let's look at some of the key demographic, economic and technological drivers that have been at play over the past decade.
Demographic Trends
The need for digital wealth management and the urgent need to combine low-cost digital advice with face-to-face human advice have arisen in three primary market segments, which many robo-advisers are targeting:
- Millennials and Gen Xers: More than 78 million Americans are Millennials (those born between 1982 and 2000), and 61 million are Gen Xers (those born between 1965 and 1981); accordingly, this segment's influence is significant. These groups demand transparency, simplicity and speed in their interactions with financial advisers and financial services providers. As a result, they are likely to use online, mobile and social channels for interactive education and advice. That said, a significant number of them are new to financial planning and financial products, which means they need at least some human interaction.
- Baby Boomers: Baby boomers, numbering 80 million, are still the largest consumer segment and have retail investments and retirement assets of $39 trillion. Considering that this segment is either at or near retirement age, the urgency to plan for their retirement as well as draw down a guaranteed income during it is critical. The complexity of planning and executing this plan typically goes beyond what today's automated technologies can provide.
- Mass-Affluent & Mass-Market: Financial planning and advice has largely been aimed at high-net-worth (top 5%) individuals. Targeting mass-affluent (the next 15%) and mass-market (the next 50%) customers at an affordable price point has proven difficult. Combining automated online advice with the pooled human advice that some of the digital wealth management players offer can provide some middle ground.
- Data Availability: The availability of data, including personally identifiable customer transactional level data and aggregated and personally non-identifiable data, has been increasing over the past five years. In addition, a number of federal, state and local government bodies have been making more socio-demographic, financial, health and other data more easily available through open government initiatives. A host of other established credit and market data companies, as well as new entrants offering proprietary personally non-identifiable data on a subscription basis, complement these data sources. If all this structured data is not sufficient, one can mine a wealth of social data on what customers are sharing on social media and learn about their needs, concerns and life events.
- Machine Learning & Predictive Modeling: Techniques for extracting insights from large volumes of data also have been improving significantly. Machine learning techniques can be used to build predictive models to determine financial needs, product preferences and customer interaction modes by analyzing large volumes of socio-demographic, behavioral and transactional data. Big data and cloud technologies facilitate effective use of this combination of large volumes of structured and unstructured data. In particular, big data technologies enable distributed analysis of large volumes of data that generates insights in batch-mode or in real-time. Availability of memory and computing power in the cloud allows start-up companies to scale on demand instead of spending precious venture capital dollars setting up an IT infrastructure.
- Agent-Based Modeling: Financial advice; investing for the short-, medium- and long-term; portfolio optimization; and risk management under different economic and market conditions are complex and interdependent activities that require years of experience and extensive knowledge of numerous products. Moreover, agents have to cope with the fact that individuals often make investment decisions for emotional and social reasons, not just rational ones.
- First Generation or Standalone Robo-Advisers: The first generation of robo-advisers targets self-directed end consumers. They are standalone tools that allow investors to a) aggregate their financial data from multiple financial service providers (e.g., banks, savings, retirement, brokerage), b) provide a unified view of their portfolio, c) obtain financial advice, d) determine portfolio optimization based on life stages and e) execute trades when appropriate. These robo-advisers are relatively simple from an analytical perspective and make use of classic segmentation and portfolio optimization techniques.
- Second Generation or Integrated Robo-Advisers: The second generation of robo-advisers is targeting both end consumers and advisers. The robo-advisers are also able to integrate with institutional systems as "white labeled" (i.e., unbranded) adviser tools that offer three-way interaction among investors, advisers and asset managers. These online platforms are variations of the "wrap" platforms that are quite common in Australia and the UK, and offer a cost-effective way for advisers and asset managers to target mass-market and even mass-affluent consumers. In 2014, some of the leading robo-advisers started "white labeling" their solutions for independent advisers and linking with large institutional managers. Some larger traditional asset managers also have started offering automated advice by either creating their own solutions or by partnering with start-ups.
- Third Generation or Cognitive Robo-Advisers: Advances in artificial intelligence (AI) based techniques (e.g., agent-based modeling and cognitive computing) will see second generation robo-advisers adding more sophisticated capability. They will move from offering personal financial management and investment management advice to offering holistic, cradle-to-grave financial planning advice. Combining external data and social data to create "someone like you" personas; inferring investment behaviors and risk preferences using machine learning; modeling individual decisions using agent-based modeling; and running future scenarios based on economic, market or individual shocks has the promise of adding significant value to existing adviser-client conversations.
- Asset Managers & Product Manufacturers: Large asset managers and product manufacturers who are keen on expanding shelf-space for their products should view robo-advisers as an additional channel to acquire specific type of customers - typically the self-directed and online-savvy segments, as well as the emerging high-net-worth segment. They also should view robo-advisers as a platform to offer their products to mass-market customers in a cost-effective manner.
- Broker Dealers and Investment Advisory Firms: Large firms with independent broker-dealers or financial advisers need to seriously consider enabling their distribution with some of the advanced tools that robo-advisers offer. If they do not, then these channels are likely to see a steady movement of assets - especially of certain segments (e.g., the emerging affluent and online-savvy) - from them to robo-advisers.
- Registered Independent Advisers and Independent Planners: This is the group that faces the greatest existential threat from robo-advisers. While it may be easy for them to resist and denounce robo-advisers in the short term, it is in their long-term interest to embrace new technologies and use them to their advantage. By outsourcing the mechanics of financial and investment management to robo-advisers, they can start devoting more time to interacting with the clients who want human interaction and thereby build deeper relationships with existing clients.
- Insurance Providers and Insurance Agents: Insurance products and the agents who sell them also will feel the effects of robo-advisers. The complexity of many products and related fees/commissions will become more transparent as the migration to robo-adviser platforms gathers pace. This will put greater pressure on insurers and agents to simplify and package their solutions and reduce their fees or commissions. If this group does not adopt more automated advice solutions, then it likely will lose its appeal to attractive customer segments (e.g., emerging affluent and online-savvy segments) for whom their products could be beneficial.
