Nyca Partners is a FinTech-focused venture firm based in New York, targeting firms that will transform payments, credit models, digital advice, and financial infrastructure. It is one of the most active FinTech venture firms globally and has made ten or so investments year-to-date. Notable portfolio companies include Ladder, Acorns, SigFig, and Affirm.
Hans Morris launched Nyca in 2014, raising $30 million for its initial fund. Fund II closed in early 2017 with $140 million, and according to filings made earlier this summer the firm has raised roughly $130 million of a targeted $200 million for Nyca Investment Fund III.
In addition to being the founder of Nyca, Hans is chairman of the board of Lending Club (which is publicly traded), and a director at Payoneer, AvidXchange, SigFig, and Boomtown. He is a former Partner at General Atlantic, the former president of Visa, as well as a former CFO of Citi’s institutional businesses and a Salomon Smith Barney banker.
Q. Hans, one of your original premises in founding Nyca was that Wall Street (NY) and Silicon Valley (CA) just didn’t “get” one another. Do you think that’s still true?
A. I think it’s improved dramatically in the five years or so since we started Nyca. Nyca stands for New York and California and was intended as a bridge between those two worlds. But since then, Wall Street and the banking system have organized themselves to be much more responsive to technology. Boards have become focused on it — and senior management is very focused on it — particularly at the larger institutions.
In many cases they’ve adopted new vendors, they’ve met with VCs, and they’ve all done trips out to Silicon Valley. In some cases they’ve set up their own internal venture funds or they’re at least open to making direct investments. They almost all have technology or innovation centers of excellence. This is all a material change from five years ago.
Insurance companies have done the same. They’re behind the banks, but they’ve all geared up. Now, even asset managers are doing it. I’d say it’s a quite significant evolution.
Q. What are your investment criteria at Nyca? What are you looking for when you evaluate companies for investment?
A. We do three types of investments.
We do several seed-stage investments each year. We think of it as helping a FinTech entrepreneur figure something out, such as how to apply their idea most effectively in the complex financial system, how they can shape the product, or think through distribution. It could include understanding the capital required to implement their idea, or what regulatory structures they could consider. Most of the time they have thought about these things already, but we can help them better understand the implications quickly and efficiently, by giving them access to real experts or tools that will help them design the product and get into the market with a higher likelihood of success.
In this stage, the most important variable is the team. How capable are they? How strong is their ability to recruit top-tier people? If it’s a group, have they worked together in the past? What sort of success have they had? Will they have the resiliency to manage through as many ups and downs as any startup has?
Sometimes it’s people who have come out of a big institution, but many people who come out of big institutions aren’t ready for the startup environment. Some have been successful with previous start-ups, but many haven’t.
One thing I care a lot about is their degree of fluency in the problem. Do they fundamentally understand it, and are they able to describe what will make success with real clarity? They may not know everything, but the fact they’ve done the work to think it through is extremely important. The nature of distribution — you can distribute directly to consumers, or to enterprises, or through enterprises to small businesses or consumers — their experience with whatever form of distribution they’ve decided on matters to us because each of them is harder than it looks if you’ve never done it before.
We would want to know how their model creates a sustainable competitive advantage of information in the underwriting decision-making. What competitive advantage do they have in origination?
A key thing for us is the amount of capital it’s going to take to realize that plan and be a winner. That changes considerably based on business model. If they are actually warehousing risk — insuring something or lending money — that can take a lot of capital. If you continue to warehouse risk, it also doesn’t scale to the degree that other functions (e.g., CAC and operating expense) will scale. The more you originate, the more risk you’re taking and so the capital generation rate becomes a very important variable. If the business model doesn’t generate sufficient excess capital to fund the growth rate, then you’re going to have to keep diluting your shareholders to meet your business plans.
Those are a few of the variables we focus on at the seed stage — the management, the specifics of the business model, the financial design, and the ability to create a consistent competitive advantage of information.
Q. What is the definition of an enduring competitive advantage of information?
A. It depends on the business model, but to pick an obvious example, if a consumer lender principally uses credit bureau scores as the core decisioning data, then that company is not creating an enduring competitive advantage of information in its underwriting process; every lender has access to the same data. If a different company is lending to consumers at the POS who don’t have credit scores, and applies insight from SKU purchase data, employment data, or behavioral insight from previous small borrowings which haven’t been reported to the bureaus, this might create a considerable and enduring competitive advantage of information, since other lenders will not have access to it.
Q. And the other two types of investments?
A. I’ve just described seed, but most of our capital is in A- or B-round companies. To us, there’s no scientific distinction between them. What we like to see is that the core management team is there — you’ve got your head of risk, the head of engineering, the head of head of sales (or distribution or whatever the model requires), and of course the CEO. You don’t always have a CFO at that stage — it’s not essential. The product is in the market and you can demonstrate that there’s demand — product/market fit is clear. The market opportunity is large and the gap in the market is also large. Is it possible that this company could become a large, public company if it’s successful? And if not, who would want to buy it and at what value? It’s also critical at this stage that we can assess the unit economics and what it’s going to take to scale the business, and how much capital that will require. Those are all important characteristics that we’ll want to evaluate.
The third type of investment we make is what we call opportunistic growth. We’ve done four of these. The company is either cash flowing or close to it, and we’re investing with others — so it’s a collaborative investment approach where we bring a specific insight or expertise to the investment team. The risk profile on that type of company is very different. We’re not actively seeking growth-stage investments, but we make about one of those a year.
Q. For the A/B round companies, what’s the average size check that you write?
A. It’s evolved since our first fund. In our first fund, in almost all circumstances we were collaborating with others and we were writing checks of anywhere from $100,000 to I think our largest was $1.7 million. In the second fund, we got much more rigorous about this. We do two types of investments within the Series A / Series B. We collaborate with other investors and that’s where a leading VC will lead the transaction and we will invest alongside them. They’ll own 10 to 20% or something like that and we’ll target a 5% ownership (sometimes less, sometimes more). And we occasionally lead, although these are not the typical, straightforward transactions with many big firms interested in leading. We’ve lead transactions where the company aligns closely to our expertise, and where we have a lot of conviction. Since 2014, we have led five or six Series A/B deals.
Q. So you’ve made 50 or so investments but in just five or six instances have you led. Is there a skill set that makes you the partner of choice for so many other VC firms? Does it simply come down to being able to play well with others?
A. I think that in the Series A / Series B world, particularly now, that entrepreneur has many, many choices, especially if they have a good underlying business model, traction, and a great team. Lots of choices. It makes sense to us that the entrepreneur would choose one of the top firms as their lead because those firms offer abundant resources, they bring an excellent track record across hundreds of companies, and decades of experience. They can make large capital commitments, and also have in-house support organizations and services they can bring. And they have a brand, which matters in many ways, but particularly in recruiting and fundraising. Having a top firm as the lead investor is usually a smart choice for the entrepreneur.
What we bring is expertise in the financial services industry. We have this group of people we call our limited partner advisors, or LPAs, and we have 40 of them now, with a wide range of experiences. I am pretty certain that there is no question in the financial services industry that cannot be answered by at least one of the LPAs — or maybe they’re a phone call away from the answer. Our institutional LPs also provide considerable help to our companies, and that’s something we have worked hard on to make work effectively. And we have a record of being a good collaborator both with the entrepreneurs and the other investors.
The lead investor, of course, does a lot work, and if someone else is leading, that’s work that we don’t have to do. Therefore, we can make more investments at a smaller size. We think we can get the same return outcomes because we’re spending our time efficiently. Instead of doing ten investments in a fund, we might do 30 investments, and get the same distribution outcome as the best performing firms. At least that’s our hope.
We think we clearly bring value. If you don’t bring value, you are not going to get any allocation. But we aren’t trying to replicate the value that the larger investors bring.
Q. How are your investment decisions made inside the firm?
A. I’m a big believer in having one pool of capital. I’m also a big believer in an investment committee process. We do it every two weeks on Tuesdays with our four investment partners, who are not full-time members of Nyca, but they’ve been part of our investment committee from the beginning: Brian Finn, Osama Bedier, Charlie Songhurst, and Tom Miglis. This is very different from other firms and it was done intentionally to avoid a problem inherent to many investment processes, which is politics in the decision-making. We wanted a mix of people who were expert in different areas of financial technology. Tom was the CIO of Salomon Brothers and the CIO of Citadel and in my view he’s the single best person on Wall Street technologies. Brian was the president of Credit Suisse and has been in the M&A, asset management and private equity business for 35 years. Charlie was the head of strategy at Microsoft and subsequently became an angel investor and a hedge fund manager. He’s extremely well connected all over the world. Osama headed wallets and payments at Google and was a key architect of PayPal’s developer platform. He’s now the CEO of a company called Poynt. That group was intended to represent a mix of Californian and New York perspectives. It really has worked extremely well. The IC has such a good dynamic that we haven’t wanted to change it. It provides very objective decisioning.
Q. Do you have regional investment preferences? Or will geographic allocation be just a residual of the attractiveness of the deals you see?
A. That’s an issue we talk about quite a bit. We have considerable geographic diversity within the experience of our LPA group and in the personal backgrounds of our partners, many of whom have decades of global experience.
So we’re aware that the problems you need to solve in a particular country might be very different from the problems in the United States. With all the similarities, for example, even Canada, has very different underlying infrastructure.
A key part of our success, though, is our ability to work closely with the companies we invest in, and that’s a lot harder if you’re not there. It’s also harder to make sure you know all the facts underlying your investment decision. You can get a point of view from the entrepreneur and you can diligence that, but if you’re not there and if you’re not plugged in, it’s hard to be a better investor than someone who isthere — impossible, almost.
So we look at non-U.S. companies and we have invested in eight non-U.S. companies. Three are in Israel. Many Israeli FinTech companies, of course, have the United States as a focus.
Q. Israel is a small market, so they have to think globally from the beginning.
A. Correct. They may not focus just on the U.S., but it’s rare that the U.S. isn’t part of their game plan. We’ve also invested in three UK-based companies and we’ve looked at many companies in other parts of the world. We’ve come close on a few but haven’t invested. Is it likely that we’ll make additional investments outside the U.S.? Yes, but the circumstances have to line up, which means we have to know a lot about the specific subject matter the company is focusing on. If we have a partner who’s there — the lead is in Israel or Mexico or Singapore and we have a good working relationship with them and a similar philosophy — that would be important for us.
Q. Have the firm’s four investment themes — payments, credit models, digital advice, and financial infrastructure — changed since you started?
A. We still use those four categories. Again, I would not describe those categories as having scientific precision. The types of companies that we see have evolved, and many investment themes seem to come in waves.
We have strong points of view on each one of those four categories. Why those are big trends, what makes a company successful, what ideas we are looking forward to seeing, and which problems still haven’t been tackled. It doesn’t mean we wouldn’t look at something outside those areas, but we think it provides a good organizing principle that helps us stay focused. We haven’t had any shortage of companies to meet so we don’t feel the need to expand beyond them.
Q. Which ideas are you most excited about right now?
A. Almost every week I’ll meet a company that’s interesting. Sometimes it will be a company that is in a business or reflects certain characteristics we’ve been looking for or is aligned with us on a particular point of view.
I can tell you the first time this happened. I’ve worked around the mortgage industry going back to 1980. In 2014, we wanted to invest in a top-tier Silicon Valley team focus on an enterprise solution in mortgage technology. We met with dozens of companies but didn’t see any that reflected exactly what we were looking for. And then I met the team at Blend. I remember how excited we were when we met them.
Here’s a different example. In 2014, we spent a lot of time with blockchain companies. We created a thesis around crypto, and one of our key beliefs was that ledgers describe most of the financial system. Whether you’re a bank or an insurance company, an asset management firm, or a brokerage firm, all of the accounts and subaccounts are just ledgers and ledger entries. Much financial friction — particularly in capital markets — is due to reconciliation of the batch processing of these entries, which creates risk and expense. So is a synchronous system in which everyone had the exact same data with unbreakable controls a powerful idea? Yes. The problem we’ve encountered is that the cost of getting to that state is very high. The ledger entries don’t really cost you anything, it’s all the systems around it that are expensive to maintain and to change. And to achieve systemic utility, it requires many organizations to adopt the changes at the same time, and if only a few firms adopt it, there might be much less benefit.
So we knew that enterprise blockchain could be a big deal for financial services. We went out and met with all the companies and there are some really good entrepreneurs and good companies, but we felt this was going to take a long time and the burn was going to be high and managing big consortia is impossible. We made a couple of small investments, but we didn’t make any big bets.
And one team we met in 2014 was the Schvey brothers, Greg and Jeff, who are CEO and CTO of Axoni; we thought they were terrific but we also thought the project they were just getting started with at DTCC on CDS was going to be a bear to manage. We just led their recent investment round after observing them over the past three years and seeing what an effective team they have. That’s an example of where we feel the last few years have given us stronger conviction about what’s going to work and what’s not. Enterprise blockchain is not a new idea, but we think Axoni is clearly the market leader, and there is more clarity about what projects will go into production.
We just invested in a company called Ethic, which is focused on the ESG space, which I am pretty confident almost every wealth manger wants to talk about. Many families as well as endowments and individual investors care about the impact of their investments. We felt that Epic had built an excellent white-label application, enabling wealth managers and others to make idiosyncratic decisions and construct a portfolio around that criteria in a very impressive way. The application create a personal dashboard of issues the investor cares about, and then creates a portfolio to achieve your social or environmental goals within a determined tracking error on a standard index, selected by the investor. We found that just fascinating. Great team, fun to work with, and they seem to get a sale at every meeting they go to!
I’m also personally very interested in how real-time payments will change certain categories of payments and open up new ways of transacting. We are seeing other new ideas in payments, though many people would have said that was unlikely given the maturity of the sector. Amino is a good example – an impressive innovation to provide payments authentication and value assessment in the AdTech ecosystem. These are some of the things we like.
Q. Which areas are overhyped?
A. It’s not so much overhyped, but maybe areas where you’re making big leaps of faith. They may be right, they may be wrong. In insurance, the nitty-gritty details of how much traction a company really has and how much capital it’s going to take to take to get meaningful market share is where the rubber is meeting the road.
That’s the lesson in marketplace lending, too. If you are warehousing risk, you need a lot of capital. Even if you’re not warehousing risk, you have lots of operational risk, and liquidity risk. If you want to protect yourself against that, you need capital.
Q. As you’ve pointed out elsewhere, you need to know how you’re going to survive without access to capital during the next liquidity crunch.
A. Correct. Another example is these new banks. Everyone knows the problems banks have. Most of them have terrible net promoter scores – although they’ve improved quite a bit recently — but the technology often stinks, and customers are hit by all these fees and charges. Couldn’t you imagine a much better, cleaner, more intuitive way of interacting with your bank? Of course you can, and many banks are working hard to create better experiences, often in partnerships with fintech companies.
So when I look at the valuations on some of the new banking models, there’s an awful lot you have to believe in order to accept them. One of the problems is the capital required if you’re really going to be the bank. You need a lotof capital.
And it’s important to keep in mind that the revenues in banking come from four sources: fees, which no one likes, debit interchange, which is regulated and pretty skinny, and float, which is not much (but could be going up). The much more important revenue source is lending income, but that typically doesn’t come from cross-selling the deposit customer. The problem is that the person who is your depositor is not often the person borrowing money where the margin is made.
In effect, you have people who deposit money at very low risk and you can use that money to fund, in a levered way, people who need to borrow money. If you do everything very well, you can generate a 12% rate of return on your equity, assuming an 8% leverage ratio. Maybe the best in the world can get 25%, which is a 2% return on assets. That’s assuming you do everything perfectly and you attract both types of customers to the platform. But it’s doesn’t generate a 50% return on capital if you assume normal levels of leverage.
You can certainly manage costs mush better, by designing it from scratch, and I think they’ve proven that. You can offer great experiences. But a licensed, balance sheet driven business shouldn’t receive software-type valuations. Capital is necessary to support the customer growth.
Q. While you’ve made investments in enterprise blockchain efforts, and they fit nicely within your infrastructure theme, you’ve said you don’t want to invest in anything that’s dependent on the price of cryptocurrencies. Is this still true?
A. I don’t have an opinion on the price of cryptocurrencies. I’ve certainly not seen any analytical framework that I found credible that supports the valuations. I’ve found arguments for appreciation in Bitcoin (but you can apply this to any cryptocurrency) that say usage would drive demand, which in turn would drive appreciation because of the defined number of units. Two problems with that. One, the use cases that people thought would drive demand have not turned out to be good use cases. It’s not effective as a transaction mechanism in most cases and it’s also not an effective store of value. It’s a speculative investment. Second, the static number of units isn’t necessarily true because you can fork so easily.
So, we wouldn’t invest in a crypto token based on a speculative appreciation case, we would only invest in it because it does something else that is relevant to financial services. And let me emphasize that we think the adoption of the underlying technology is way past the tipping point in terms of number of engineers and teams and traders engaged in crypto. Many large companies are thinking about applications of the technology. It’s clear to me that this will be a very important aspect of future technology applications, and I think we’ll find it works really well in certain circumstances and doesn’t work well in others. But there’s lots of shaking out to do.
We’ve definitely passed on some really great crypto companies. I mean companies I’ve loved. I met the Coinbase people when they were doing their B round, and they were really smart and talented. It was an impressive company. I just thought it was too expensive and I was obviously wrong about that. We met the Circle team and thought they were impressive, as was the Ripple team. We met a lot of companies that have increased their value dramatically. It’s just that our investment decision at the time was that, with so much still left to be determined, valuations should have been much lower. Obviously, that was incorrect.
Q. Where do you see the biggest opportunities to apply AI in financial services? In a recent issue of the Economist, they reported that bankers are primarily interested in using the technology to eliminate headcount, which is disappointing.
A. I feel that with AI, and I actually feel this is true with distributed ledgers as well, that “being an AI company” will fade to the background. It’s just a technology which will be incorporated into many things. But I believe there are several applications where if you don’t employ it you will be at an incredible competitive disadvantage.
Fraud is a great application. There are some areas which are very promising, but they have associated regulatory issues, for example AML. You file a SAR because you have suspicious activity and determine it was a false positive. You want to teach the machine to shrink the haystack. But regulators have to endorse this, and it’s not fully sorted out in any jurisdiction, let alone throughout the financial system.
Fraudsters, whether money launderers or organized crime, morph regularly, so the nature of the problem will regularly change, and therefore what you are monitoring and tracking will also change. So those are both good applications of AI.
Similarly, there is massive expense in financial reconciliation, so machine learning can be very effective applied. As can robotics, actually. In credit, it’s less clear. In short-term credit it can be quite effective. But if you are offering credit over a long period of time it’s a very hard environment to control for because you have systemic response. How do other agents respond and how does behavior change over a measurement period? It’s not clear that AI will solve all those problems effectively, particularly since in almost all cases lending is regulated. How your model incorporates new variables will have to be demonstrated to regulators. It’s not completely straightforward how it’s going to work.
In investment management, it’s already proven that there are some good applications for AI. There are many, many other promising ideas if you survey the full range of financial services, but it’s not a panacea. We have a joke about people coming in with old ideas but saying, “now with AI.” We don’t, therefore, view it as an investment thesis on its own.
Q. What is your reaction to the recent Treasury Department FinTech report?
A. I thought it was quite good and well written. I continue to think payday lending is problematic and I might say that the report’s determinations were influenced politically. I agree that eliminating the confusion around marketplace lending is a good thing to do. I think they are appropriately focused on potential abuses or problems that could be caused by technology, particularly in data usage. But they’re also focused on how technology can help and urging entrepreneurs and regulators to get on with it.
It was impressive that the OCC released the FinTech charter on the same day.
Q. Are we going to see VC-backed, full-stack, de novo banking startups in the U.S., or does it just require too much capital?
A. Varo is one. Warbug Pincus led that which is unusual in that they typically focus on growth stage investing, but they do startups occasionally. Colin Walsh is an example of an entrepreneur with a clear strategy and vision who knew what he was doing, and he convinced a great investor to back it. But I think the amount of capital required, as I said earlier, is significant.
I don’t know how many of these we’ll see. There’s still a lot to be determined in terms of how the OCC interprets their own regulation and so I don’t think anyone is ready to predict how many of those charters are going to be granted.
Regulatory clarity is something that was called for by the Treasury, and we have a system in the United States which reflects literally the earliest economic debates in the U.S. The debate around the First Bank of the United States concerned whether you want a centralized financial system as Hamilton and the Federalists did, or you want a decentralized system or democratized banks, which Jefferson, Madison and Jackson wanted.
I think you could read Andrew Jackson’s veto message of the renewing the charter of the Second Bank of the United States on the floor of Congress today. The First Bank of the United States was not rechartered and was allowed to close in 1812. When the War of 1812 broke out soon after, it proved very difficult to collect and move money for the federal government. The expiration of the charter for Second Bank of the United States in 1836 was followed very shortly by the massive Panic of 1837.
In many cases, the people against the Bank of the United States were people who had an emotional response that was against their economic interests. I’ve viewed it as somewhat analogous to health care and other issues where it’s not clear why a subset of Americans have a visceral – and often misinformed — objection to something which is actually in their economic interest, but that characteristic goes back to the beginning of the republic. It’s not new.
Q. I did not expect that we’d have a history lesson in this interview, but that’s really good stuff!
You started your career in public finance. Are you seeing any innovative ideas targeting this area?
A. I have not. Neighborly is one I‘m aware of but I have not met them. I have not seen many innovative companies addressing public finance. I do think technology can improve the delivery of public services. We have not, ourselves, invested in any companies that are doing that, but we’ve met a few that we like. Use cases for some of the companies we have invested in could involve government payments. I’m thinking of PayRange, for parking and other small ticket payments, for example.
Q. You are Chairman of the Board of Lending Club. What advice do you have about managing in a crisis?
A. Most crises have several characteristics in common. They’re crises because people didn’t anticipate them, so one piece of advice is to build an enterprise risk framework. You can’t anticipate everything, but you can have a point of view around it and you can have enough capital to get you through it. That’s important.
I do believe that tabletop exercises, as they’re called, are very, very useful. I remember [at Salomon Smith Barney] after 9/11, our planning around business continuity became much, much better and we hired a terrific person who really helped us rethink things. Some of the tabletop exercises we did were incredibly prescient. Suddenly, you feel like you’re in the middle of a crisis. Someone is saying, “Now this has happened, followed by some other terrible, unanticipated development, how do you react to this?”
One we did was around a hurricane hitting New York. They displayed a graphic with flooding spreading everywhere very rapidly. One thing we did as a result of the exercise is we moved the generators out of the basement of 390 Greenwich Street, and that proved to be an extremely good decision when the basement actually flooded during Hurricane Sandy.
You can do that around liquidity. I actually think continuity of business is not something FinTech companies have prepared themselves for and yet some of these developments become more likely as you become more successful — obviously not an earthquake — but a targeted cyberattack is very much a function of how successful you are and of whether you’re on the radar screen of state actors or organized crime. Similarly, as you hire more and more people, the fact that some could be bad actors among the last 1,000 people you added is inevitable. So doing an exercise around that is, I feel, very, very useful because you’ll learn and you’ll be a little bit more ready for the experience when it happens to you.
Q. Many startups find that accessing banking and insurance data for product development purposes is difficult and expensive, if not impossible. What advice do you have for startups that need data to prove their models?
A. That’s a big topic. Even high-quality capital markets data that is organized to enable vigorous back-testing is very difficult to get. If you just want historical data on U.S. Treasuries, that’s one thing, but if you’re looking for relationships between all asset classes — and all the derivatives of all asset classes — that’s really hard to do.
There’s a lot left to learn about what data turns out to be highly relevant in making underwriting decisions, whether it’s insurance or credit. If I were going to make a sweeping — but true — statement, it would be that every time I’ve looked back in my career over the last 38 years we’ve said, well, five years ago the models were wrong. Your models will also turn out to be right many times, but each time we make our models more sophisticated to incorporate data we either hadn’t thought about or that were difficult to capture, we found that either the modeling or the risk management turned out not to work well when things turned south.
There’s a really good technique that I did not know much about until four or five years ago called agent-based modeling. You can use it to model transmission of infectious diseases, for example. Really what you’re doing is modeling how all these different agents inside a system optimize for their outcomes and what is the impact of this on others. Therefore, your ecosystem is not stable. Your ecosystem changes and typical modeling doesn’t assume that. You’ll assume some sort of distribution analysis and say that within these observation periods we are confident that this relationship will hold. Be you haven’t captured the fact that the system may change. The environment when you last established these relationships is likely to be different from what you have now.
And as models get more complex, they break down. I don’t see any solution to that. If anything, really smart people sometimes have an overreliance on their models just due to the range of their experience. That’s something that’s very useful to remember. My old boss, Lew Glucksman, had been the chairman of Lehman Brothers. He was hired by Sandy Weill to run capital markets at Smith Barney where I ended up reporting to him. He used to have this line which is so true: The secret to Wall Street was that the same lessons were learned over and over again and he was the only one who remembered what happened last time. Still wise words.
Q. Digital advice is one of your themes. Is technology going to eliminate the role commissioned agents, brokers, and other producers play in the distribution of financial products and advice?
A. There are obviously misaligned incentives, at times.
You could be a very hardworking and conscientious agent or broker, and yet, if you go on vacation you may not rebalance the portfolio on a timely basis, and there are other things you may not do if someone is not your most important client, no matter how hard you’re trying to.
The more important issue is, I think, the nature of the issues that person is being asked to address is getting more complex than even the most able person can manage. If you really want to do a great job, then you need a level of expertise and tools to support you that you are unlikely to find in these very large agent forces. As a result, firms are trying to train people and monitor them in case they do something stupid or noncompliant.
Really, I think machines are much better at this. They don’t do things that are noncompliant and they’re much cheaper. You can create great experiences that are in many cases very intuitive. And they can have alignment with the customer. But, the machine doesn’t know things about the customer that might be really helpful to him or her.
Do I think SigFig does a better job of allocating a $100,000 investment portfolio than anyone at Merrill Lynch or Morgan Stanley? I would say, yeah, certainly better than 99% of them. And fewer than 10% of Americans have more than $100,000 to invest. So, therefore, 90% of people, at least, should be using it. But what SigFig doesn’t know — what the algorithm doesn’t know — is the nature of your relationships and what worries you — that your sister might be crazy, for example. There’s other stuff relevant to the relationship. And so I can see people being involved for a long time and maybe forever. But saying that this person is an investment expert is not nearly as important as saying this person has good judgement and is going to help you, with the support of the firm.
Q. How would you like entrepreneurs to contact you?
A. The best thing is to use email@example.com. That’s an efficient way to do it.
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