Tag Archives: robot

Smart Tech Helps Older People, Too

New technologies offer insurers the opportunity to build more engaged relationships with their customers. Fitness-linked insurance programs, for example, are attractive to active people who have access to technology and a desire to use it. While wearables and apps are most closely associated with promoting physical fitness, technology is increasingly being put to use in lifestyle monitoring of the elderly and others in need of care.

Technology that is simple to understand and use works best. Some older people find the latest gadgets baffling. Even after a device has been set up and explained, they often have little confidence and remain skeptical of the benefits. Health problems make some devices hard to operate, while the cost and lack of access to technology is another barrier. Despite the challenges, the percentage of people using technology in later life is rising fast.

U.K. figures show that 75% of people 65-74 years old now have access to the internet and that more than one-third own a smartphone. Among the individuals over 75, one-quarter use tablets, and 41% have a social media account. Three-quarters of smartphone-owning older Americans use the internet several times a day or more. These numbers are all pretty close to those seen in much younger age groups.

It’s no surprise the baby-boomer generation is digitally engaged, but new technologies can also provide interventions for much older adults, and many of them are eager adopters. Aging populations create opportunities for products and services. The U.K. government has committed to invest in innovation to meet the needs that result from this demographic change.

See also: Insurance 2025: Smart Contracts  

Telecare and telehealth are technological interventions to deliver services at a distance from the provider. Smart homes, assistive robots, technology-based wellness and therapeutics can all promote an independent lifestyle for older people, not only providing for their physical and cognitive fitness but also entertainment, leisure and wellbeing.

There are reasons other than cost-saving for technological solutions to help older people remain independent, including assistance with everyday tasks compensating for lost physical or cognitive function. In Japan, where 25% of the population are senior, the predicted shortfall of caregivers by 2025 is likely to be met by nursing-care robots currently being developed with government backing. Caregivers also enjoy positive outcomes by experiencing less worry. For example, tracking how a person with dementia interacts with a virtual assistant device – the questions they ask it and how often, the tone and cadence of the voice – could help spot cognitive changes, as could analysis of onscreen scrolling and mouse movement.

Phones and tablets provide isolated people information and links to social networks for friendship, help and support. Technology sends reminders about medication. Sensors monitor sleep, kitchen activity and walking speed, and raise the alarm if a person has a fall. Behavioral data from self-learning intelligent software allows caregivers to analyze patterns of behavior, spot negative trends and intervene quickly.

Before insurers embark on building more digital engagement programs, it is important to know how they can appeal to the wide range of customers. It is important to maximize the potential for understanding how older adults perceive technology, and providing help with setup and support. In the Netherlands, several insurers now reimburse users employing home sensors, and others are experimenting with reimbursements on wearables. More will surely follow because technology might prevent hospitalization or worse.

See also: Solving Insurtech’s People Challenge  

Concerns remain over potential security and privacy risks that these technologies pose. Monitoring must be structured in an ethical way that is compliant with data laws, and there must be a person-centered approach ensuring tangible benefit for the person concerned. The pressure on health services is increasing as the numbers of elderly people continue to rise, and developed technologies that address these concerns can help reduce the overall costs of prevention and monitoring.

6 Technologies That Will Define 2016

Please join me for “Path to Transformation,” an event I am putting on May 10 and 11 at the Plug and Play accelerator in Silicon Valley in conjunction with Insurance Thought Leadership. The event will not only explore the sorts of technological breakthroughs I describe in this article but will explain how companies can test and absorb the technologies, in ways that then lead to startling (and highly profitable) innovation. My son and I have been teaching these events around the world, and I hope to see you in May. You can sign up here.

Over the past century, the price and performance of computing has been on an exponential curve. And, as futurist Ray Kurzweil observed, once any technology becomes an information technology, its development follows the same curve. So, we are seeing exponential advances in technologies such as sensors, networks, artificial intelligence and robotics. The convergence of these technologies is making amazing things possible.

Last year was the tipping point in the global adoption of the Internet, digital medical devices, blockchain, gene editing, drones and solar energy. This year will be the beginning of an even bigger revolution, one that will change the way we live, let us visit new worlds and lead us into a jobless future. However, with every good thing, there comes a bad; wonderful things will become possible, but with them we will create new problems for mankind.

Here are six of the technologies that will make the change happen.

1. Artificial intelligence

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There is merit to the criticism of AI—even though computers have beaten chess masters and Jeopardy players and have learned to talk to us and drive cars. AI such as Siri and Cortana is still imperfect and infuriating. Yes, those two systems crack jokes and tell us the weather, but they are nothing like the seductive digital assistant we saw in the movie “Her.” In the artificial-intelligence community, there is a common saying: “AI is whatever hasn’t been done yet.” People call this the “AI effect.” Skeptics discount the behavior of an artificial intelligence program by arguing that, rather than being real intelligence, it is just brute force computing and algorithms.

But this is about to change, to the point even the skeptics will say that AI has arrived. There have been major advances in “deep learning” neural networks, which learn by ingesting large amounts of data. IBM has taught its AI system, Watson, everything from cooking, to finance, to medicine and to Facebook. Google and Microsoft have made great strides in face recognition and human-like speech systems. AI-based face recognition, for example, has almost reached human capability. And IBM Watson can diagnose certain cancers better than any human doctor can.

With IBM Watson being made available to developers, Google open-sourcing its deep-learning AI software and Facebook releasing the designs of its specialized AI hardware, we can expect to see a broad variety of AI applications emerging because entrepreneurs all over the world are taking up the baton. AI will be wherever computers are, and it will seem human-like.

Fortunately, we don’t need to worry about superhuman AI yet; that is still a decade or two away.

2. Robots

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The 2015 DARPA Robotics Challenge required robots to navigate over an eight-task course that simulated a disaster zone. It was almost comical to see them moving at the speed of molasses, freezing up and falling over. Forget folding laundry and serving humans; these robots could hardly walk. While we heard some three years ago that Foxconn would replace a million workers with robots in its Chinese factories, it never did so.

Breakthroughs may, however, be at hand. To begin with, a new generation of robots is being introduced by companies—such as Switzerland’s ABB, Denmark’s Universal Robots, and Boston’s Rethink Robotics—robots dextrous enough to thread a needle and sensitive enough to work alongside humans. They can assemble circuits and pack boxes. We are at the cusp of the industrial-robot revolution.

Household robots are another matter. Household tasks may seem mundane, but they are incredibly difficult for machines to perform. Cleaning a room and folding laundry necessitate software algorithms that are more complex than those required to land a man on the moon. But there have been many breakthroughs of late, largely driven by AI, enabling robots to learn certain tasks by themselves and by teaching each other what they have learned. And with the open source robotic operating system (ROS), thousands of developers worldwide are getting close to perfecting the algorithms.

Don’t be surprised when robots start showing up in supermarkets and malls—and in our homes. Remember Rosie, the robotic housekeeper from the TV series “The Jetsons”?  I am expecting version No. 1 to begin shipping in the early 2020s.

3. Self-driving cars

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Once considered to be in the realm of science fiction, autonomous cars made big news in 2015. Google crossed the million-mile mark with its prototypes; Tesla began releasing functionality in its cars; and major car manufacturers announced their plans for robocars. These cars are coming, whether or not we are ready. And, just as the robots will, they will learn from each other—about the landscape of our roads and the bad habits of humans.

In the next year or two, we will see fully functional robocars being tested on our highways, and then they will take over our roads. Just as the horseless carriage threw horses off the roads, these cars will displace us humans. Because they won’t crash into each other as we humans do, the robocars won’t need the bumper bars or steel cages, so they will be more comfortable and lighter. Most will be electric. We also won’t have to worry about parking spots, because they will be able to drop us where we want to go to and pick us up when we are ready. We won’t even need to own our own cars, because transportation will be available on demand through our smartphones. Best of all, we won’t need speed limits, so distance will be less of a barrier—enabling us to leave the cities and suburbs.

4. Virtual reality and holodecks

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In March, Facebook announced the availability of its much-anticipated virtual reality headset, Oculus Rift. And Microsoft, Magic Leap and dozens of startups aren’t far behind with their new technologies. The early versions of these products will surely be expensive and clumsy and cause dizziness and other adverse reactions, but prices will fall, capabilities will increase and footprints will shrink as is the case with all exponential technologies. 2016 will mark the beginning of the virtual reality revolution.

Virtual reality will change how we learn and how we entertain ourselves. Our children’s education will become experiential, because they will be able to visit ancient Greece and journey within the human body. We will spend our lunchtimes touring far-off destinations and our evenings playing laser tag with friends who are thousands of miles away. And, rather than watching movies at IMAX theaters, we will be able to be part of the action, virtually in the back seat of every big-screen car chase.

5. Internet of Things

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Mark Zuckerberg recently announced plans to create his own artificially intelligent, voice-controlled butler to help run his life at home and at work. For this, he will need appliances that can talk to his digital butler: a connected home, office and car. These are all coming, as CES, the big consumer electronics tradeshow in Las Vegas, demonstrated. From showerheads that track how much water we’ve used, to toothbrushes that watch out for cavities, to refrigerators that order food that is running out, all these items are on their way.

Starting in 2016, everything will be be connected, including our homes and appliances, our cars, street lights and medical instruments. These will be sharing information with each other (perhaps even gossiping about us) and will introduce massive security risks as well as many efficiencies. We won’t have much choice because they will be standard features—just as are the cameras on our smart TVs that stare at us and the smartphones that listen to everything we say.

6. Space

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Rockets, satellites and spaceships were things that governments built. That is, until Elon Musk stepped into the ring in 2002 with his startup SpaceX. A decade later, he demonstrated the ability to dock a spacecraft with the International Space Station and return with cargo. A year later, he launched a commercial geostationary satellite. And then, in 2015, out of the blue, came another billionaire, Jeff Bezos, whose space company Blue Origin launched a rocket 100 kilometers into space and landed its booster within five feet of its launch pad. SpaceX achieved the feat a month later.

It took a space race in the 1960s between the U.S. and the USSR to even get man to the moon. For decades after this, little more happened, because there was no one for the U.S. to compete with. Now, thanks to technology costs falling so far that space exploration can be done for millions—rather than billions—of dollars and the raging egos of two billionaires, we will see the breakthroughs in space travel that we have been waiting for. Maybe there’ll be nothing beyond some rocket launches and a few competitive tweets between Musk and Bezos in 2016, but we will be closer to having colonies on Mars.

This surely is the most innovative period in human history, an era that will be remembered as the inflection point in exponential technologies that made the impossible possible.

Seriously? Artificial Intelligence?

I don’t know about you, but when I think of artificial intelligence, I think Steven Spielberg and Arnold. That was until I saw a solution offered by Conversica, a Salesforce partner.

AI is here, it’s happening now and it’s a lot more pervasive than you think. The rise of “robo advisers” in financial services, Ikea’s “Anna” customer service rep and Alaska Airline’s “Jenn” all point to the growing adoption of technology that personalizes customer experiences….at scale.

One of the 5 D’s of Disruption in insurance is “Dialogue.” And AI is driving it.

Today, in insurance, AI is used to create natural dialogue with customers, nurture those leads, prioritize them for agents and follow through as needed. Conversica, for example, gets smarter as it interacts more with customers. And, yes, it has passed the Turing test.

It is particularly well-suited for B2C because the volume of interactions with prospects can be overwhelming for insurance agents. As insurers embrace omni-channel, new prospects can be created from any source, whether it be a contact center, social media or a face-to-face meeting. Not only is lead volume increasing, but it takes as many as six before an agent can get a prospect on the phone. This becomes a time and energy suck for agents; he is unable to follow through on every lead, and the quality of interactions goes down.

So how are insurers and agents responding? In this webinar, Eric (Conversica) and Alex (Spring Venture Group) explain to me how AI is used to nurture and convert leads.

My takeaway: AI is not just a science project. It works. It’ll become more invisible to consumers. And it creates real value to both customers and employees.

As Marc Benioff, CEO of Salesforce, said recently in Fortune magazine, “We’re in an AI spring. I think for every company, the revolution in data science will fundamentally change how we run our business because we’re going to have computers aiding us in how we’re interacting with our customers.”

The Robocalypse for Knowledge Jobs

Long-time Costa Rican National Champion Bernal Gonzalez told a very young me in 1994 that the world’s best chess-playing computer wasn’t quite strong enough to be among the top 100 players in the world.

Technology can advance exponentially, and just three years later world champion Garry Kasparov was defeated by IBM’s chess playing supercomputer Deep Blue. But chess is a game of logic where all potential moves are sharply defined and a powerful enough computer can simulate many moves ahead.

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Things got much more interesting in 2011, when IBM’s Jeopardy-playing computer Watson defeated Ken Jennings, who held the record of winning 74 Jeopardy matches in a row, and Brad Rutter, who has won the most money on the show. Winning at Jeopardy required Watson to understand clues in natural spoken language, learn from its own mistakes, buzz in and answer in natural language faster than the best Jeopardy-playing humans. According to IBM, ”more than 100 different techniques are used to analyze natural language, identify sources, find and generate hypotheses, find and score evidence and merge and rank hypotheses.” Now that’s impressive — and much more worrisome for those employed as knowledge workers.

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What do game-playing computers have to do with white collar, knowledge jobs? Well, Big Blue didn’t spend $1 billion developing Watson just to win a million bucks playing Jeopardy. It was a proof of concept and a marketing move. A computer that can understand and respond in natural language can be adapted to do things we currently use white collar, educated workers to do, starting with automating call centers and, sooner rather than later, moving on up to more complex, higher-level roles, just like we have seen with automation of blue collar jobs.

In the four years since its Jeopardy success, Watson has continued advancing and is now being used for legal research and to help hospitals provide better care. And Watson is just getting started. Up until very recently, the cost of using this type of technology was in the millions of dollars, making it unlikely that any but the largest companies could make the business case to replace knowledge jobs with AIs (artificial intelligence). In late 2013, IBM put Watson “on the cloud,” meaning that you can now rent Watson time without having to buy the very expensive servers.

Watson is cool but requires up-front programming of apps for very specific activities and, while incredibly smart, lacks any sort of emotional intelligence, making it uncomfortable for regular people to deal with it. In other words, even if you spent the millions of dollars to automate your call center with Watson, it wouldn’t be able to connect with your customer, because it has no sense of emotions. It would be like having Data answering your phones.

Then came Amelia…

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Amelia is an AI platform that aims to automate business processes that up until now had required educated human labor. She’s different from Watson in many ways that make her much better-suited to actually replace you at the office. According to IPsoft, Amelia aims at working alongside humans to “shoulder the burden of tedious, often laborious tasks.”

She doesn’t require expensive up-front programming to learn how to do a task and is hosted on the cloud, so there is no need to buy million-dollar servers. To train her, you literally feed her your entire set of employee training manuals, and she reads and digests them in a matter of a few seconds. Literally, just upload the text files, and she can grasp the implications and apply logic to make connections between the concepts. Once she has that, she can start working customer emails and phone calls and even recognize what she doesn’t know and search the Internet and the company’s intranet to find an answer. If she can’t find an answer, then she’ll transfer the customer to a human employee for help. You can even let her listen to any conversations she doesn’t handle herself, and she literally learns how to do the job from the existing staff, like a new employee would, except exponentially faster and with perfect memory. She also is fluent in 20 languages.

Like Watson, Amelia learns from every interaction and builds a mind-map that eventually is able to handle just about anything your staff handled before. Her most significant advantage is that Amelia has an emotional component to go with her super brains. She draws on research in the field of affective computing, “the study of the interaction between humans and computing systems capable of detecting and responding to the user’s emotional state.” Amelia can read your facial expressions, gestures, speech and even the rhythm of your keystrokes to understand your emotional state, and she can respond accordingly in a way that will make you feel better. Her EQ is modeled in a three-dimensional space of pleasure, arousal and dominance through a modeling system called PAD. If you’re starting to think this is mind-blowing, you are correct!

The magic is in the context. Instead of deciphering words like insurance jargon when a policyholder calls in to add a vehicle or change an address, IPsoft explains that Amelia will engage with the actual question asked. For example, Amelia would understand the same requests that are phrased different but essentially mean the same thing: “My address changed” and “I need to change my address.” Or, “I want to increase my BI limits” and “I need to increase my bodily injury limits”.

Amelia was unveiled in late 2014, after a secretive 16-year-long development process, and is now being tested in the real world at companies like Shell Oil, Accenture, NNT Group and Baker Hughes on a variety of tasks from overseeing a help desk to advising remote workers in the field.

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Chetan Dube, long-time CEO of IPSoft, Amelia’s creator, was interviewed by Entrepreneur magazine:

“A large part of your brain is shackled by the boredom and drudgery of everyday existence. […] But imagine if technology could come along and take care of all the mundane chores for you, and allow you to indulge in the forms of creative expression that only the human brain can indulge in. What a beautiful world we would be able to create around us.”

His vision sounds noble, but the reality is that most of the employees whose jobs get automated away by Watson, Amelia and their successors, won’t be able to make the move to higher-level, less mundane and less routine tasks. If you think about it, a big percentage of white collar workers have largely repetitive service type jobs. And even those of us in higher-level roles will eventually get automated out of the system; it’s a matter of time, and less time than you think.

I’m not saying that the technology can or should be stopped; that’s simply not realistic. I am saying that, as a society, there are some important conversations we need to start having about what we want things to look like in 10 to 20 years. If we don’t have those discussions, we are going to end up in a world with very high unemployment, where the very few people who hold large capital and those with the STEM skills to design and run the AIs will do very well, while the other 80-90% of us could potentially be unemployable. This is truly scary stuff, McKinsey predicts that by 2025 technology will take over tasks currently performed by hundreds of millions of knowledge workers. This is no longer science fiction.

As humans, our brains evolved to work linearly, and we have a hard time understanding and predicting change that happens exponentially. For example, merely 30 years ago, it was unimaginable that most people would walk around with a device in their pockets that could perform more sophisticated computing than computers at MIT in the 1950s. The huge improvement in power is a result of exponential growth of the kind explained by Moore’s law, which is the prediction that the number of transistors that fit on a chip will double every two years while the chip’s cost stays constant. There is every reason to believe that AI will see similar exponential growth. Just five years ago, the world’s top AI experts at MIT were confident that cars could never drive themselves, and now Google has proven them wrong. Things can advance unimaginably fast when growth becomes exponential.

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Some of the most brilliant minds of our times are sounding the alarm bells. Elon Musk said, “I think we should be very careful about AI. If I had to guess, our biggest existential threat is probably that we are summoning the demon.” Stephen Hawking warned, “The development of full-artificial intelligence could spell the end of the human race.”

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.