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The Rise of the Robo-Advisers?

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.

 

Technological Advances

Technical advances have accompanied demographic developments. The availability of new sources and large volumes of data (i.e., big data) has meant that new techniques are now available (see “What comes after predictive analytics?”) to understand consumer behaviors, look for behavioral patterns and better match investment portfolios to customer needs.

 

  • 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.

 

Behavioral finance takes into account the many factors that influence how individuals really make decisions, and human advisers are naturally skeptical that robo-advisers will be able to match their skills interpreting and reacting to human behavior. While this will continue to be true for the foreseeable future, the gap is narrowing between an average adviser and a robo-adviser that models human behavior and can run scenarios based on a variety of economic, market or individual shocks. Agent-based models are being built and piloted today that can model individual consumer behavior, analyze the cradle-to-grave income/expenses and assets/liabilities of individuals and households, model economic and return conditions over the past century and simulate individual health shocks (e.g., need for assisted living care). These models are assisting both self-directed investors who interact with robo-advisers and also human advisers.

Evolution of Robo-advisers

We see the evolution of robo-advisers taking place in three overlapping phases. In each phase, the sophistication of advice and its adoption increases.

 

  • 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.

 

One could argue that, with the increasing sophistication of robo-advisers, human advisers will eventually disappear. However, we don’t believe this is likely to happen anytime in the next couple of decades. There will continue to be consumers (notably high-worth individuals with complex financial needs) who seek human advice and rely on others to affect their decisions, even if doing so is more expensive than using an automated system. Because of greater overall reliance on automated advice, human advisers will be able to focus much more of their attention on human interaction and building trust with these types of clients. 

Implications to Financial Service Providers

How should existing producers and intermediaries react to robo-advisers? Should they embrace these newer technologies or resist them?

 

  • 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.

 

Product manufacturers, distributors, and independent advisers who ignore the advent of robo-advisers do so at their own risk. While there may be some present-day hype and irrational exuberance about robo-advisers, the long-term trend toward greater automation and integration of automation with face-to-face advice is undeniable. This situation is not too dissimilar to automated tax-advice and e-filing. When the first automated tax packages came out in the ’90s, some industry observers predicted the end of tax consultants. While a significant number of taxpayers did shift to self-prepared tax filing, there is still a substantial number of consumers who rely on tax professionals to file their taxes. Nearly 118 million of the 137 million tax returns in 2014 were e-filings (i.e., electronically filed tax returns), but tax consultants filed many of them. A similar scenario for e-advice is likely: a substantial portion of assets will be e-advised and e-administered in the next five to 10n years, as both advisers and self-directed investors shift to using robo-advisers.

$1.2 Trillion Disruption in Personal Insurance

Most of us don't think much about insurance. That's by design, of course. Insurance is supposed to be a safety net that affords us the leisure of not thinking about it. Unless of course, we have to. That generally happens about once a year when we're reacquainted with our premium. Ouch. According to statisticians, most of us will also have to think about our insurance about once every seven to eight years when we'll encounter a loss of some sort. Another ouch.

My insurance is pretty confusing. I pay for coverage of my house – a fairly precise calculation based on its quality, size, age, materials, etc. I get a guarantee that, if I keep paying my premium, my home will be covered for its replacement costs. That's pretty reassuring. But then it gets a little weird. I get a “blanket” (insurance-speak is very comforting), which is really a formula that assumes that all the stuff I own is worth, um, somewhere around 50% to 70% of the value of my home. Huh? Maybe there's a bit of science to this, but surely there's a lot of guess…and, according to research, about 39% of the time the formula is just wrong. (As one insurance CEO recently confessed to me, most folks are probably 50% underinsured). The complications go on: If I own something really valuable, some bauble or collectible, well, that has to go on a list of things that are really valuable, and those things get their own coverage. Then, so my stuff continues to be well-protected, I have to re-estimate the value of those things from time to time, or employ an appraiser. What's more, if I buy something or donate something I own, or if any of my things goes down or up in value for whatever reason, my insurance doesn't change — because my provider doesn't know about these changes. And, if you've ever had a claim to file, the process starts with the assumption of fraud, with the burden of proof borne by the policyholder, because most people don't have an accurate accounting of their possessions and their value. Still another ouch.

So while I'm not supposed to be thinking about insurance, maybe I should be paying closer attention.  

Change is coming like a freight train, and its impact has the potential to shake one of the world's largest industries to its core. For a little perspective: The property and casualty insurance industry collected some $1.2 trillion (!) in premiums in 2012, (or about twice the annual GDP of Switzerland). 

At the core of the P/C insurance enterprise is (and I know I am simplifying here) the insurance-to-value ratio, which estimates whether there's enough capital reserved to insure the value of items insured —  if values go up, there'd better be enough money around in case of a loss. All good, right? Except that for as long as actuaries have been actuarying, the value side of that ratio has been a guess — especially for personal property (the stuff I own other than my home). So, if I forget to tell my insurer about something I bought, or if I no longer own that painting, watch, collectible, antique; or if the precious metal in my jewelry has increased…then what? Am I paying too much, or am I underinsured for the current value of the things I own? Of course, these massive companies make calculated allowances for the opacity…but these allowances also cost us policyholders indirectly in increased premiums, and the inefficiency costs the insurer in potential returns on capital. 

The coming changes can be summarized in terms of three trends. First is the expectation of the connected generations, now entering their most acquisitive years and set to inherit $30 trillion of personal wealth. Second is the connected availability of current data about the value of things. Third is the emergence of the personal digital locker for things.

Data, data! I want my data! — the expectation of the connected generations.

If they're anything, the connected generations are data-savvy and mobile. If you’ve shopped for just about anything with a Millennial recently, you’re familiar with their reliance on real-time data about products, local deals, on-line values and even local inventories. (I was with one of Google's brains, and he showed me how retailers are now sending Google local inventory data so now it can post availability and price of a searched-for item at a local store). Smartphone usage is nearly 90% for Gen Xers and Millennials, and data is mother's milk to the children of the connected generations who are being weaned on a diet rich with direct (disintermediated) access to comparisons, descriptions, opinions, crowd-sourced knowledge and even current values. The emerging generations rarely rely on the intermediation of experts (unless validated on a popular blog with a mass following) and are not likely to be satisfied with an indirect relationship with those affecting their financial health. Smartphones in hand, depending on data in the cloud, they will demand and receive visibility into the data shaping all their risk decisions.    

And here's where the insurance revolution will begin: A connected generation that is apt to disintermediate and has access to real-time info on just about any thing will demand that they insure only what they own (bye bye, blanket); that their insurance should track to real values, not formulaic guesses; and that they have the ability to reprice more frequently than once a year. 

The time is coming for variable-rate insurance that reflects changes in the values of items insured and is offered on a real-time basis for any item that the owner deems valuable. 

The price is wrong — the real-time valuation of everything.

Over the past few years, several data services have sprung up whose charters are similar: something like developing the world's largest collection of data about products — their descriptions, suggested retail price, current resale value, user manuals, photos and the like. No one has yet dominated, but it's early yet, and someone (or probably a few) will conquer the objective. Similarly, there are a few excellent companies that are collecting and indexing for speedy retrieval the information about every collectible that has been sold at auction for the past 15 years. I know something of these endeavors because our core product relies on the availability and accuracy of these data providers to collect the values (and other attributes) of the items people are putting into their Trovs (our moniker for the personal cloud for things). It is only a matter of time before we will be able to accurately assign a fair market value to most every thing — in real-time and without human intervention. This real-time value transparency will transform the way that insurance is priced, and how financial institutions view total wealth.

My stuff in the clouds — the automated collection and secure storage for the information about my things.

Within 12 to 24 months, connected consumers will embrace applications that will automatically (as much as possible) collect the information about all they own and store it in a secure, personal cloud-hosted locker. These “personal data lockers” will proliferate because of their convenience, because of real financial incentives from insurers and other service providers and because data-equipped consumers will have powerful new tools with which to drive bargains based on the data about everything they own. These new tools will pour fuel on the re-invention of insurance because all the information needed to provide new types of insurance products will be in the personal cloud-hosted data locker.

Progressively (pun noted, not intended) engineered insurance products that account for the connected generations' expectation of access to data, the abundance of data about products and collectibles and the active collection and accurate valuation of the things people own may turn the 300-year-old insurance industry on its head. Doubtless, the disruption will leave some carriers grappling for handholds and wondering how they could have insured against a different outcome.

This article first appeared in JetSet magazine.