Last year, venture capitalist Marc Andreessen famously wrote that software is eating the world. The impact of algorithms upon media, education, healthcare and government, among many other verticals, is just beginning to be felt, and with still unfolding consequences for the industries disrupted.
Whether it’s the prospect of IBM’s Watson offering a diagnosis to a patient or Google’s self-driving car taking over on the morning commute, there are going to be serious concerns raised about safety, power, control and influence.
Doctors and lawyers note, for good reason, that their public appearances on radio, television and the Internet should not be viewed as medical or legal advice. While financial advice may not pose the same threat to a citizen as an incorrect medical diagnosis or treatment, poor advice could have pretty significant downstream outcomes.
That risk isn’t stopping a new crop of startups from looking for a piece of the billions of dollars paid every year to financial advisors. Future Advisor launched in 2010 with the goal of providing better financial advice through the Internet using data and algorithms. They’re competing against startups like Wealthfront and Betterment, among others.
Not everyone is convinced of the validity of this algorithmically mediated approach to financial advice. Mike Alfred, the co-founder of BrightScope (which has liberated financial advisor data itself), wrote in Forbes this spring that online investment firms are wrong about financial advisors:
“While singularity proponents may disagree with me here, I believe that some professions have a fundamentally human component that will never be replaced by computers, machines, or algorithms. Josh Brown, an independent advisor at Fusion Analytics Investment Partners in NYC, recently wrote that ‘for 12,000 years, anywhere someone has had wealth through the history of civilization, there’s been a desire to pay others for advice in managing it.’ In some ways, it’s no different from the reason why many seek out the help of a psychiatrist. People want the comfort of a human presence when things aren’t going well. A computer arguably may know how to allocate funds in a normal market environment, but can it talk you off the cliff when things go to hell? I don’t think so. Ric Edelman, Chairman & CEO of Edelman Financial Services, brings up another important point. According to him, ‘most consumers are delegators and procrastinators, and need the advisor to get them to do what they know they need to do but won’t do if left on their own’.”
To get the other side of this story, I recently talked with Bo Lu (@bolu), one of the two co-founders of Future Advisor. Lu explained how the service works, where the data comes from and whether we should fear the dispassionate influence of our new robotic financial advisor overlords.
Where did the idea for Future Advisor come from?
Lu: The story behind Future Advisor is one of personal frustration. We started the company in 2010 when my co-founder and I were working at Microsoft. Our friends who had reached their mid-20s were really making money for the first time in their lives. They were now being asked to make decisions, such as “Where do I open an IRA? What do I do with my 401K?” As is often the case, they went to the friend who had the most experience, which in this case turned out to be me. So I said, “Well, let’s just find you guys a good financial advisor and then we’ll do this,” because somehow in my mind, I thought, “Financial advisors do this.”
It turned out that all of the financial advisors we found fell into two distinct classes. One were folks that were really nice but essentially in very kind words said, “Maybe you’d be more comfortable at the lower stakes table.” We didn’t meet any of their minimums. You needed a million dollars or at least a half million to get their services.
The other kinds of financial advisors who didn’t have minimums immediately started trying to sell my friends term life insurance and annuities. I’m like, “These guys are 25. There’s no reason for you to be doing this.” Then I realized there was a misalignment of incentives there. We noticed that our friends were making a small set of the same mistakes over and over again, such as not having the right diversification for their age and their portfolio, or paying too much in mutual fund fees. Most people didn’t understand that mutual funds charged fees and were not being tax efficient. We said, “Okay, this looks like a data problem that we can help solve for you guys.” That’s the genesis out of which Future Advisor was born.
What problem are you working on solving?
Bo Lu: Future Advisor is really trying to do one single thing: deliver on the vision that high-quality financial advice should be able to be produced cheaply and, thus, be broadly accessible to everyone.
If you look at the current U.S. market of financial advisors and you multiply the number of financial advisors in the U.S. — which is roughly a quarter-million people — by what is generally accepted to be a full book of clients, you’ll realize that even at full capacity, the U.S. advisor market can serve only about 11% of U.S. households.
In serving that 11% of U.S. households, the advisory market for retail investing makes about $20 billion. This is a classic market where a service is extremely expensive but in being so can only serve a small percentage of the addressable market. As we walked into this, we realized that we’re part of something bigger. If you look at 60 years ago, a big problem was that everyone wanted a color television and they just weren’t being manufactured quickly or cheaply enough. Manufacturing scale has caught up to us. Now, everything you want you generally can have because manufactured things are cheap. Creating services is still extremely expensive and non-scalable. Healthcare as a service, education as a service and, of course, financial services, financial advising service comes to mind. What we’re doing is taking information technology, like computer science, to scale a service in the way the electrical engineering of our forefathers scaled manufacturing.
How big is the team? How are you working together?
Bo Lu: The team has eight people in Seattle. It’s almost exactly half finance and half engineering. We unabashedly have a bunch of engineers from MIT, which is where my co-founder went to school, essentially sucking the brains out of the finance team and putting them in software. It’s really funny because a lot of the time when we design an algorithm, we actually just sit down and say, “Okay, let’s look at a bunch of examples and see what the intuitive decisions are of science people and then try to encode them.”
We rely heavily on the existing academic literature in both computational finance and economics because a lot of this work has been done. The interesting thing is that the knowledge is not the problem. The knowledge exists, and it’s unequivocal in the things that are good for investors. Paying less in fees is good for investors. Being more tax efficient is good for investors. How to do that is relatively easy. What’s hard for the industry for a long time has been to scalably apply those principles in a nuanced way to everybody’s unique situation. That’s something that software is uniquely good at doing.
How do you think about the responsibility of providing financial advice that traditionally has been offered by highly certified professionals who’ve taken exams, worked at banks, and are expensive to get to because of that professional experience?
Bo Lu: There’s a couple of answers to that question, one of which is the folks on our team have the certifications that people look for. We’ve got certified financial advisors*, CFAs, which is a private designation on the team. We have math PhDs from the University of Washington on the team. The people who create the software are the caliber of people that you would want to be sitting down with you and helping you with your finances in the first place.
The second part of that is that we ourselves are a registered investment advisor. You’ll see many websites that on the bottom say, “This is not intended to be financial advice.” We don’t say that. This is intended to be financial advice. We’re registered federally with the SEC as a registered investment advisor and have passed all of the exams necessary.
*In the interview, Lu said that FutureAdvisor has ‘certified financial advisors’. In this context, CFA stood for something else: The Future Advisor team includes Simon Moore, a chartered financial analyst, who advises the startup on investing algorithms design.
Where does the financial data behind the site come from?
Bo Lu: From the consumer side, the site has only four steps. These four steps are very familiar to anyone who’s used a financial advisor before. A client signs up for the products. It’s a free web service, designed to help everyone. In step one, they answer a couple of questions about their personal situation: age, how much they make, when they want to retire. Then they’re asked the kinds of questions that good financial advisors ask, such as your risk tolerance. Here, you start to see that we rely on academic work as much as possible.
There is a great set of work out of the University of Kentucky on risk tolerance questionnaires. Whereas most companies just use some questionnaire they came up with internally, we went and scoured literature to find exact questions that were specifically worded — and have been tested under those wordings to yield statistically significant deviations in determining risk tolerance. So we use those questions. With that information, the algorithm can then come up with a target portfolio allocation for the customer.
In step two, the customer can synchronize or import data from their existing financial institutions into the software. We use Yodlee, which you’ve written about before. It’s the same technology that Mint used to import detailed data about what you already hold in your 401K, in your IRA, and in all of your other investment accounts.
Step three is the dashboard. The dashboard shows your investments at a level that makes sense, rather than current brokerages where when you log in, they tell you how much money you have, with a list of funds you have, and how much they’ve changed in the last 24 hours of trading. We answer four questions on the dashboard.
- Am I on track?
- Am I well-diversified for this goal?
- Am I overpaying in hidden fees in my mutual funds?
- Am I as tax efficient as I could be?
We answer those four questions and then in the final step of the process, we give algorithmically-generated, step-by-step instructions about how to improve your portfolio. This includes specific advice like “this many shares of Fund X to buy this many shares of Fund Y” in your IRA. When the consumer sees this, he or she can go and, with this help, clean up their portfolios. It’s kind of like diagnosis and prescription for your portfolio.
There are three separate streams of data underlying the product. One is the Yodlee stream, which is detailed holdings data from hundreds of financial institutions. Two is data about what’s in a fund. That comes from Morningstar. Morningstar, of course, gets it from the SEC because mutual funds are required to disclose this. So we can tell, for example, if a fund is an international fund or a domestic fund, what the fees are, and what it holds. The third dataset is from the datasets that we have to tier in ourselves, which is 401K data from the Department of Labor.
On top of this triad of datasets sits our algorithm, which has undergone six to eight months of beta testing with customers. (We launched the product in March 2012.) That algorithm asks, “Okay, given these three datasets, what is the current state of your portfolio? What is the minimum number of moves to reduce both transaction costs and any capital gains that you might incur to get you from where you are to roughly where you need to be?” That’s how the product works under the covers.
What’s the business model?
Bo Lu: You can think of it as similar to Redfin. Redfin allows individual realtors to do more work by using algorithms to help them do all of the repetitive parts. Our product and the web service is free and will always be free. Information wants to be free. That’s how we work in software. It doesn’t cost us anything for an additional person to come and use the website.
The way that Future Advisor makes money is that we charge for advisor time. A small percentage of customers will have individual questions about their specific situation or want to talk to a human being and have them answer some questions. This is actually good in two ways.
One, it helps the transition from a purely human service to what we think will eventually be an almost purely digital service. People who are somewhere along that continuum of wanting someone to talk to but don’t need someone full-time to talk to can still do that.
Two, those conversations are a great way for us to find out, in aggregate, what the things are that the software doesn’t yet do or doesn’t do well. Overall, if we take a ton of calls that are all the same, then it means there’s an opportunity for the software to step in, scale that process, and help people who don’t want to call us or who can’t afford to call us to get that information.
What’s the next step?
Bo Lu: This is a problem that has a dramatic possible impact attached to it. Personal investing, what the industry calls “retail investing,” is a closed-loop system. Money goes in, and it’s your money, and it stays there for a while. Then it comes out, and it’s still your money. There’s very little additional value creation by the financial advisory industry.
It may sound like I’m going out on a limb to say this, but it’s generally accepted that the value creation of you and I putting our hard-earned money into the market is actually done by companies. Companies deploy that capital, they grow, and they return that capital in the form of higher stock prices or dividends, fueling the engine of our economic growth.
There are companies across the country and across the world adding value to people’s lives. There’s little to no value to be added by financial advisors trying to pick stocks. It’s actually academically proven that there’s negative value to be added there because it turns out the only people who make money are financial advisors.
This is a $20 billion market. But really what that means is that it’s a $20 billion tax on individual American investors. If we’re successful, we’re going to reduce that $20 billion tax to a much smaller number by orders of magnitude. The money that’s saved is kept by individual investors, and they keep more of what’s theirs.
Because of the size of this market and the size of the possible impact, we are venture-backed because we can really change the world for the better if we’re successful. There are a bunch of the great folks in the Valley who have done a lot of work in money and the democratization of software and money tools.
What’s the vision for the future of your startup?
Bo Lu: I was just reading your story about smart disclosure a little while ago. There’s a great analogy in there that I think applies aptly to us. It’s maps. The first maps were paper. Today if you look at the way a retail investor absorbs information, it’s mostly paper. They get a prospectus in the mail. They have a bunch of disclosures they have to sign — and the paper is extremely hard to read. I don’t know if you’ve ever tried to read a prospectus; it’s something that very few of us enjoy. (I happen to be one of them, but I understand if not everyone’s me.) They’re extremely hard to parse.
Then we moved on to the digital age of folks taking the data embedded in those prospectuses and making them available. That was Morningstar, right? Now we’re moving into the age of folks taking that data and mating it with other data, such as 401K data and your own personal financial holdings data, to make individual personalized recommendations. That’s Future Advisor the way it is today.
But just as maps moved from paper maps to Google Maps, it didn’t stop there. It moves and has moved to self-autonomous cars. There will be a day when you and I don’t ever have to look at a map because, rather than the map being a tool to help me make the decision to get somewhere, the map will be a part of a service I use that just gets the job done. It gets me from point A to point B.
In finance, the job is to invest my money properly. Steward it so that it grows, so that it’s there for me when I retire. That’s our vision as well. We’re going to move from being an information service to actually doing it for you. It’s just a default way so that if you do nothing, your financial assets are well taken care of. That’s what we think is the ultimate vision of this: Everything works beautifully and you no longer have to think about it.
We’re now asked to make ridiculous decisions about spreading money between a checking account, an IRA, a savings account and a 401K, which really make no sense to most of us. The vision is to have one pot of money that invests itself correctly, that you put money into when you earn money. You take money out when you spend it. You don’t have to make any decisions that you were never trained nor educated to make about your own personal finances because it just does the right thing. The self-driving car is our vision.
Connecting the future of personal finance with an autonomous car is an interesting perspective. Just as with outsourcing driving, however, there’s the potential for negative outcomes. Do you have any concerns about the algorithm going awry?
Bo Lu: We are extremely cognizant of the weighty matters that we are working with here. We have a ton of testing that happens internally. You could even criticize us, as a software development firm, in that we’re moving slower than other software development firms. We’re not going to move as quickly as Twitter or Foursquare because, to be honest, if they mess up, it’s not that big a deal. We’re extremely careful about it.
At the same time, I think the Google self-driving car analogy is apt because people immediately say, “Well, what if the car gets into an accident?” Those kinds of fears exist in all fields that matter.
Analysis: Why this matters
“The analogy that comes to mind for me isn’t the self-driving car,” commented Mike Loukides, via email. “It’s personalized medicine.”
One of the big problems in health care is that to qualify treatments, we do testing over a very wide sample, and reject it if it doesn’t work better than a placebo. But what about drugs that are 100% effective on 10% of the population, but 0% effective on 90%? They’re almost certainly rejected. It strikes me that what Future Advisor is doing isn’t so much helping you to go on autopilot, but getting beyond generic prescriptions and generating customized advice, just as a future MD might be able to do a DNA sequence in his office and generate a custom treatment.
The secret sauce for Future Advisor is the combination of personal data, open government data and proprietary algorithms. The key to realizing value, in this context, is combining multiple data streams with a user interface that’s easy for a consumer to navigate. That combination has long been known by another name: It’s a mashup. But the mashups of 2012 have something that those of 2002 didn’t have, at least in volume or quality: data.
Future Advisor, Redfin (real estate) or Castlight (healthcare) are all interesting examples of entrepreneurs creating data products from democratized government data. Future Advisor uses data from consumers and the U.S. Department of Labor, Redfin synthesizes data from economists and government agencies, and Castlight uses health data from the U.S. Department of Health and Human Services. In each case, they provide a valuable service and/or product by making sense of that data deluge.