Lending, Crowdfunding, and Modern Investing


Module 1: Robo-Advising

Lecture slides

  • Analyze the building blocks of Robo-Advising
  • Assess the benefits and drawbacks of traditional investment advising
  • Discuss how robo-advisers deliver high impact investment advice at high volume and low costs


  • you don't have to read the news very far to see that benefit came at a big cost. So it's not just magic that the money is going to be there when it's time to retire. In fact, the employer has to stick to a schedule of making payments into a pension fund and then has to see to it that that money grows to the level that's needed so that the assets of the pension fund are sufficient for the liabilities, the liabilities being the pension payments that they have to make to the people who've retired.
  • take a look at what's happened around the country with pension plans particularly municipal, state, and other government pension plans where the money paid into the plans really wasn't sufficient to meet this goal. They were relying on rates of return that they thought they were going to make but then they didn't make them and when they didn't make them, well the money just wasn't there.
  • we were transitioning out of those defined benefit plans into today's world of what we call defined contribution plans.
  • So getting back to robo-advisors. If you think about where is the place that they're really creating the value, that's really the oxygen for this whole field, it's in helping people with these big financial decisions they now have to make, they didn't use to have to make them, but now you do. Now, do it yourself retirement savings were just about everybody and these are people who don't know any more than they used to about this field that that's not their field. So it's crucial to help these people save intelligently for retirement to help them come up with an investment plan, help them stick to it, and also to show them the range of outcomes that they can expect.

Portfolio Theory

  • what it is that Dr. Markowitz did and how it is essentially what you will find now under the hood of a robo-advising app and his insights from the 50s are exactly the insights we're using today and you see they're all about how if you want to invest for an expected return you're going to have to bear some risk.
  • what Dr. Markowitz showed was how to take the least risk for the expected return that you are targeting. That is the sort of special sauce of what robo advising is bringing to the client.
  • you're really not going to want to take that bet to risk losing the first thousand for the chance of getting the second thousand.

Mean Variance Optimisation

  • so our goal here is fixed expected return, minimize risk, or fix risk and maximize expected return
  • How to measure risk
    • Variance: variance is the expected squared deviation of the outcome from its expectation
    • Standard deviation
  • for a given expected return, we're going to want to minimize variance and for a given variance we're going to maximize expected return. That's why people refer to this whole sort of body of work on this topic as mean variance optimization
  • Efficient portfolio:
    • of all portfolios with the same expected return it has least variants
    • or all portfolios with it's variants it has the highest expected return
  • Co-variances - how much the assets move with each other.
  • that can be the tough part, right, you think about well, what might go into this portfolio? Well, maybe shares of Facebook, maybe shares of Apple, maybe shares of Walmart, or Ford, or maybe an IPO that just happened yesterday. Maybe stocks from other countries, other sorts of assets, gold futures, oil futures all sorts of things could go in to this optimization, right?
  • And that can be tough to say what was the expected return of Facebook versus the expected return of Apple, versus the expected return of Ford? All right, that's not so easy, okay, generally speaking it's going to be a lot easier for you to say something intelligent about, not those expected returns, but those co-movements, right. That's something that you actually can say, you can think about with a lot more accuracy. I can kind of understand how Facebook moves with Apple or with Google. I can understand how Facebook might not move so tightly with Walmart or with Ford. And I can go to the data, I can measure those co-movements with a lot of accuracy
  • let's just say for argument's sake, expected returns are all the same, but the co-movements. Those are the things that I'm going to estimate from the data and put in the optimization and that is going to give me my optimal portfolio.
  • sometimes for some practitioners, the easiest judgment calls is to say look, I'm not going to make sort of wild guesses about expect returns of different stocks. But I do have lots of information to use about their co-movements, and that's going to be the workhorse of the investment advice I'm giving my clients, okay? So that's one of the hard way
  • The easy way to accomplish mean variance optimization is to just take the value-weighted market index to be an efficient portfolio, okay? So take the value-weighted market index such as the S&P 500, that's pretty much a value-weighted market index here in the US, to be an efficient portfolio. Okay, now this might sound like just throwing up your hands like, okay, forget it. I'm not going to try to optimize. But actually there are some good economic reasons to believe that a value-weighted market index, the broader the index, the better is going to be at least reasonably efficient
  • if everybody in the world holds an efficient portfolio, then everybody's portfolio put together is an efficient portfolio, okay? Well, everybody's portfolio put together is essentially the value-weighted market index, right? That's sort of what it is. It's just all of the assets put together according to their sizes
  • If everybody can borrow, or lend, at the risk-free rate, all right?¬†Everybody can borrow or lend at the risk-free rate, more or less.
  • Then you can show that, ultimately, It's optimal for¬†everybody to hold risky assets in the same proportional weights and¬†then just lever up or down depending on how risk¬†averse they are using the risk-free rate, right?¬†So if you're very risk averse then you'd put a little bit in the risky assets and¬†a lot in the risk-free rate.¬†If you really want risk,¬†then you're going to borrow a lot at the risk-free rate, you're going to lever up.¬†Borrow money at the risk-free rate and then lever way up and¬†put a huge amount of money in the risky assets.
  • Both of the arguments I gave obviously are approximate, because we know that they're not exactly true statements about people and how they invest. But they're approximate, and economists learn to live with close enough
  • Another reason, which is very different reason, is that an index is generally going to be much cheaper to trade, cheaper to trade then individual assets.
    • now when I see this I'm thinking of the work of another famous economist, his name is George Ackerlof, and he is professor at Berkeley. And he wrote a very famous paper, the 1970, for which he also won the Nobel Prize. And this is a paper he called The Market for Lemons. when you and I want to trade and you know that I have private information about the thing that we're trading, that is going to make you nervous
    • he shows how the market could completely evaporate all due to the fact that you worry about whatever price you might offer the car.
    • The person on the other side has to defend against the possibility that you are trading due to some private information. And therefore is going to charge you a transactions cost. He's going to charge you what we call in the profession, a bid ask spread, right? If you're buying, he's going to charge you a price higher than you would get if you sold
    • so that's what it's like to try to put on a position in individual assets. But what if you're trading the whole index? What if you're trading the whole index at once, okay? I'm not buying each of the 500 stocks in the S&P, I'm just buying the S&P index from somebody else. Well, now that worry is going to be far smaller. Okay, when I'm buying the S&P 500 index from you, you're probably worrying a lot less.
    • So what George Ackerlof is telling us about trading is try to trade in a way that the other side can see that you're probably not trading on private information. And you're going to get better transactions costs, okay? So trade of the index versus the individual assets is going to be cheaper in the long run.
  • So you can have an optimizer that optimizes over individual assets. Or you could just take the value-weighted index to be your optimal portfolio.¬†You can also take a middle road here, kind of a hybrid approach,¬†where your optimizer is optimizing over individual assets.¬†But the assets themselves that it's optimizing¬†over are index portfolios, okay?¬†So the S&P could be one of those indices that goes into the optimizer.¬†There could be the indices for other countries

ETFs and Mutual Funds

  • what are this assets that we can optimize over, that allow us to diversify across indices and basically, there are two types of assets that I want to talk about here. Number one is going to be your basic open-end mutual fund, let's call it mutual funds.
  • The other is going to be an exchange traded fund with this assets that are now called ETFs. ETF, exchange traded fund. So those are the two things I want to talk about. Those really are the main workhorses of the robo advisor. It's going to be where they're going to be putting your money.
  • Mutual funds: around since 1920s. if I have a mutual fund, then I'm managing money and if you want to invest with me, well, you can give me 10,000 bucks of your money to manage and then I would take your 10,000 bucks, I would put it in the fund. So now my fund is gotten bigger by 10,000 bucks and I got to go put your money to work by buying more shares or whatever it is I'm trading. What you get, you're going to get shares of the fund and at what price you're going get shares? Well, I'm going to calculate the per share value of my mutual fund. So I value the whole portfolio that I'm holding of all the different things, value it at the end of the day and come up with a dollar figure divided by the number of shares outstanding of the fund, and that ratio is the value per share of the fund, and that's the price that you're going to pay.
    • this industry has changed tremendously over the past, let's say, over the past 40 years but especially, let's say, just in the past 10 years. I would say 10 or 15 years. This industry has changed the most and the change I am talking about is a transition from a world of active management to a world of passive management. So when I say active management, that's what it sounds like. You give me your money and I am going to actively manage it. I'm going to figure out which stocks are undervalued. I'm going to buy those. If they're overvalued, I'm going to sell them.
    • That was sort of the whole world of mutual funds if you go back 40 years but especially recently, people have been moving instead into index investment and for two reasons really. Number one, the track record of active managers has not been good on average. So in a given year, the fraction of active managers that beat the market is below half. Most of them don't beat the market. Part of this of course is they charge pretty big fees. You might pay one percent per year to your active manager to make his trades. Whereas, if you invested with an index fund they would charge you far less, right? You put your money just an S&P 500 index fund, you're not paying one, hoping not paying one percent, right? These index funds they're not doing much labor here. They're not doing a lot of research. They're not doing all this work. They would otherwise charge you for. They also not trading very much, right? Because they're just buying the index. So it's all going to be a lot cheaper and people have been convinced by that logic and money is moving from active management into indexing
    • Notice that, when you trade with an open-end mutual fund, you're putting money in the fund so your investment makes the fund trade, okay? Now that can be some amount of drain on the fund that people go in and out and going in and out, makes the fund trade because it has transactions costs, right? So there's that. There's also another thing which is that, if you want to invest in an open-end fund, there's only one time of day that they trade, which is the end of the day. So if in the middle of the day say, "Hey, I think now's the time to get in the market." Well, you can make an order for shares, but you're not actually buying shares until the end of the day. So whatever happens in the market for the rest of the day you're not getting
  • Exchange Traded Funds: they trade on the exchange. That means, when you buy shares of an ETF, you're not putting money in the fund, you're just buying shares from somebody else who's selling them, okay? Your money goes to the seller and you get the shares, but the fund itself is sitting there untouched. Okay. Your trade is of no consequence, direct consequence to the fund itself.
    • another difference from the open-end fund is, because these shares are trading on the exchange, you can go in and out whenever the market is open
    • because it's trading on the exchange, you can access it from any brokerage account
  • if you take a look at what these robo-advisors are going to do, they're going to focus on the ETFs. They're going to focus on the ETFs because you have a whole range of ETFs to choose from. You can go in and out whenever. ETF fees are even lower than the index funds.
    • If you invested the robo-advisor, you are paying fees on fees, right? You're paying fee to the robo-advisor and you're paying a fee for the investment that they put you in. But the fees on the investments they put you in a really low, okay? So even you add those two things together, you're not talking about a lot much per year.
    • there are about 200 new ETFs rolled out every year, with just every possible combination different sorts of indices that you might want to aim for. So the robo-advisor can sort of think at its level about how to allocate across these indices and there's going to be an ETF there to make that work

Target Date Funds

  • another thing that's probably going to be the most key customizations they do, is going to be targeting, so where you are on the life cycle. Where are you now relative to when you picture yourself retiring?
  • this has been how people thought about life cycle investing forever as long as people have been saving for retirement, which is that more risk is more appropriate when you're younger. This is still pretty much the conventional wisdom that people follow, that over time, if you're investing for the long horizon, then you think "Well, I can put money in something risky. Yes. It's risky. It's going to go up and down, but over a long horizon, I should expect the ups and downs to wash out."
  • this leads me to a product these days that it's not robo-advising, but it's very popular and it gives you a sense of what robo-advisors are doing. That is target date funds. Target date funds are by far the most popular choice these days for retirement savings. They're doing essentially what I just said.
  • The overwhelming choice these days is the employee will say, "Okay, about when am I going to retire?" If I'm right now starting a job, right now, I might think, "I'm probably going to retire something like 2065." All right. Something like that. So I would look and I say, "There is a 2065 target date fund." Okay. I'll just go with that, 2065 target date fund. Just send all my money to the 2065 target date fund, and that's it.
  • Well, that fund is going to execute that life cycle tilting that I just described. When you're way off from retirement as you are now, it's going to put you heavily into equities and a little bit into bonds. Okay. Then over time, it's just going to follow a rule by which equity investment goes down, bond investment goes up.
  • They're targeting that date, they're following a rebalancing rule that targets that date of 2065. But how much are you're going to have then? That's really going to depend on what happens with the market. This confusion was a big deal back during the financial crisis. If you think about in 2008 or 2009, market went way down and whatever your target date fund had in equities at that time, well, they were exposed to that return. If you were sitting and thinking that there's a definite amount of money that you're going to get at retirement and now you see your bounce go way down, well, know there's not a definite amount of money.
  • that is the essence of robo-advising. They are a customized portfolio that is taken to account, just one key factor, which is how close are you to retirement and coming up with a customized retirement plan on that basis.


  • Robo-Advisors obviously can do better than that. They can find out much more than just your age and they can customize more accurately than that. We talked about how they could gauge your risk aversion by asking targeted questions and and customize your portfolio to that. Of course, there's plenty other financial goals that you might have besides retirement.
  • There's all these other things that they don't have to ask, you they just have to watch you. If you have my app I can see when you use it, I can see what kind of events occur in the macro economy in the world that seem to trigger you using it. I can see where you click. I can see where other people are clicking right now. So, I'm learning a lot, you think you're just looking at your app. I'm learning about you constantly. I can use that in ways that could help you,

Module 2: Crowdfunding

Lecture slides

  • It's too early to say what crowdfunding has done for us, but we can do is look at what crowdfunding is and see how it might fit in to the whole sort of ecosystem of financing innovation. Okay, so the focus of this module is not so much on the realized experience of crowdfunding, it's about the promises and also we'll think a lot about the conceptual challenges of crowdfunding
  • Jobs Act of 2012 gave way for crowdfunding

Raising capital

  • companies going directly to the retail market with advertisements and with in cautious language, and celebrity endorsements, and then just taking whatever money they could raise that way, well, this was pretty much standard operating procedure if you go back more than a 100 years, until it's an accumulation of bad experiences and a fraud here and there started the regulatory process at the state, and then the federal level that has given us the protocol that we follow now for tapping the capital markets.
  • if we want to understand what crowdfunding is, how to think about this new regulatory environment, these new loosening of regulations on tapping this market, it's worth first seeing what it is, what it is we're doing now in the existing regulatory environment to facilitate raising money from the retail public
  • Consider an IPO:
  • On average, IPO goes up 18% from the offer price to where it trades the next day
  • that's just a sketch there of how we go public, right. There's a lot of regulatory scrutiny with all the documents that you are presenting to the public. You're engaging an underwriter who has tremendous experience and reputation with the capital markets.
  • from the retail investors point of view IPOs are fun to watch, but it's not really something that you're going to participate in and you can buy like anyone else, you can buy it once it starts trading. So bear that in mind.
  • Here's one other interesting fact, which is that the number of IPOs is way down. the mid 90s number of publicly listed companies was over seven thousand, that's like 7200 publicly listed companies in the US. That had been going up and up and up and it seemed as natural.
  • Well, of course it's just going to keep going up as the US population grows. Naturally, you think the number of publicly listed companies would grow. So the number of publicly listed companies is today half of what it was about 25 years ago, so you think from the retail investors' point of view just the scope of choice.
  • the status quo of¬†raising money from the capital markets when you¬†go to the retail, public,¬†I do that through the IPO process in which¬†the retail public really barely plays any role at all.¬†I do this through tight scrutiny by¬†the regulators through engaging¬†an underwriter with a big reputation.
  • This has worked recently well over the years but it's¬†also come way down as a part of the capital markets,¬†many fewer IPOs than there used to be,¬†many fewer publicly listed companies that used to be.¬†That is the context in which¬†Congress rolled out the Jobs Act of 2012.

Jobs Act 2012

  • The darkest moments I suppose were 2009 into 2010 and the rate of IPOs was very low, it was almost nonexistent for awhile there and you may remember, you remember those days, remember Ben Bernanke talking about looking for "green shoots" right, like new plants growing up after there's been a forest fire or something like that, signs of new life, the next generation of entrepreneurship rising out of the ashes. So that was the context in which Congress was thinking, what can we do? What can we do to bring capital into startups? Get entrepreneurship going to jumpstart
  • Act has four titles focusing on different stages of a startup's growth curve
    1. the idea was emerging growth companies, so the idea that a company that is making less than a billion dollars in revenue, not profit, revenue, a billion dollars. That such a company would be called an emerging growth company and if they wanted to go public, they could do it in a less onerous way, a way that might be a little less costly or intimidating to them than and that's maybe that's why they're not doing it, okay. So one of the big pieces of it and this has really caught on, is confidential review process. I can decide well, okay, do I want to do this or not? Do I want to go public or not? I might think well, given the feedback I'm getting from the SEC or maybe just given what's happened elsewhere in the markets for several months. This has been extended by Trump administration to all companies.
    2. image
  1. Allowing companies to stay private. regulation D is one of the go-to regulation for companies that want to stay private and want to raise money privately without going public. But what they can't do, is bring in money by advertising, or what the SCC calls general solicitation. You couldn't do that. You couldn't just put an ad in the paper say call this number if you're interested in investing in our company. You couldn't advertise. that is the big developed with title II is that I can bring in money from outside investors that I bring in simply by advertising. Now these days you do that on the Internet. So you can do that, but there's a catch. So basically, this is crowdfunding. Titled II is a flavor of crowdfunding. I'm putting myself out there on the Internet, I'm describing a company, I'm telling you how you can invest and you can invest, sounds like crowdfunding.
  1. Crowdfunding for anybody. But limit on how much people can invest and company is limited on how much it can rise.
  1. Mini IPO: it's a way to allow a company to do an IPO maybe in a slightly earlier moment than they otherwise would and at a smaller size. So this really hasn't taken off so much the way that the regulators hoped it would as well. There's only been a few of these and the performance hasn't really been that great. As I say it's not really, I wouldn't call it crowdfunding, it really got more of the flavor of a typical IPO. Sell directly to retail investors. Done on a best effort basis

Cost of Crowdfunding

  • what does it cost to go to this crowdfunding market? What it cost from the entrepreneur's point of view, and particularly, what does it cost from the investor's point of view
  • Due diligence maybe too expensive. Alternative is to not spend the time on it - invest in things you already know about.
  • I don't have that fall back that you have in the public markets, that even if I don't know what I'm doing, if I don't buy Tesla, I don't really know about all those product lines that Tesla produces. I could really not know anything about the business, and yet I'm paying a price that because the market price is a sense in which I'm not getting ripped off. This is a price that in some sense, the market has agreed, is the right price for Tesla.
  • there's a sense that what you see in the crowdfunding site in many cases, is picked over. These are some extent the companies that couldn't be funded some other way, and if that's the case, then that's potential concern point for you as an investor.
  • So that's where you would be asking yourself this, "Why might crowdfunding not be a last resort?" Well, one story that you'll hear and you might just ask yourself does this apply in this case or not, is that I'm going to the crowdfunding market as a proof of concept. I'm going to the crowdfunding market before going to the venture capitalists, so I can show them look at the potential demand is going to be for my product. there you can see there's a very plausible story in which this is¬†not a last resort financing

Impact Investing

  • B Corp vs Benefit Corporation
  • As opposed to just a B Corp that has got a stamp of approval from a private outfit, a benefit corp has actually legally exposed themselves to damages if they don't follow through on their impact goal.

Module 3: Marketplace Lending

Lecture slides



The Consumer Credit Landscape

  • Students and car loans really taken off
  • we're going to focus here, because we're thinking about marketplace lending, peer-to-peer lending. This is really about the unsecured, uncollateralized part of the market, so not borrowing to buy a house, borrowing to buy a car, it's loans that are not secured by anything in particular. The main elements of consumer credit that that's going to mean is the credit card debt and the student loans.
  • Hone equity line - lower interest rate because you've posted your house as collateral
  • Why is student loan balance is going up? Income-driven repayment

Evolution of Peer to Peer Lending

  • how as an economist would look at this problem of trying to find the rate at which we could transact. There's a fundamental problem that lending institutions everywhere encounter every day, and to an economist, the term of art for this problem is what they call credit rationing.
  • we both know that, I know things about my risk that you don't know. All right. I know things about my job, my health, my plans, all sorts of other things, my investments. I put a high probability on the outcome where I go bust. All right. That I just I can't pay it back, I privately know that that's a high probability.
  • you offer me, offer to loan to me at a high interest rate. Well, if I look at that loan at a high interest rate, I might look at that and say, well, you know what, it's on paper it's a high interest rate, but in fact if I put a high probability on not repaying the loan, then it doesn't really seem like such a high interest rate to me because I put a big probability on paying you back zero, right. Or something small right. So a high interest rate to me to someone who privately knows his risk is high, might not seem so big
  • If you offer to loan to me at a low interest rate, then I would accept that whether I'm low risk or high risk. Okay. But the higher the interest rate that you offer, the more likely it is that the guy who privately knows he's low-risk is going to drop out and say, forget it. But the guy who privately knows that he's high risk is just still going to accept it. As you increase the interest rate that you offer, the average risk of the person who would accept that offer, is going up.
  • The problem is that, you might only be willing to go so far with the interest rate that you offer because you know that if you go above that rate, then the effect of going above that rate on the expected risk of the person who would accept that rate, is just too high. So you're willing to go so far but not above that in the rate that you'll offer me, and that's why people talk about credit rationing because normally in any market you think well, if there is excess demand to buy something at some price, then the price is going to go up to make supply equal demand. But in the lending market, if there's excess demand to lend at this rate, that doesn't mean that the price is going to change. There's only so much. People are only going to raise the rates so much. They're not going to go higher than that because of this negative effect of the rate that you offer on the risk of the kind of person who would accept it.
  • hat's how it started, and there was some of this in a small-scale. Of course, it didn't help very much that this started in 2006, and then very soon, well, we were in the recession which naturally didn't help the repayment capability of the people who borrowed. So what happened over the next few years is that the lending platform got more and more involved in the decisions.
  • FICO score <680 = subprime. What the people at the Lending Club and Prosper and so on will say, "Well, okay. We can look at that population of people below 680, and we can find good credits. We can find people who really are prime even though their credit score says otherwise, and we're willing to loan to them at rates that reflect our belief that they really are prime."
  • Lending Club categorises them from A to G (A lowest risk, G highest)
  • So it's basically showing you that as you¬†sort people by our credit risks,¬†we are capturing the lion's share of¬†the actual variation in how risky¬†people really are in the FICO score.¬†On top of that, this isn't telling you very much.¬†So they've developed an algorithm.¬†Of course, they're looking at things¬†your FICO score can't look at,¬†your FICO score is calculated off your credit report.¬†They are calculating this off of all sorts of stuff that¬†the FICO score cannot be calculated off of.¬†Your bank account statement isn't on your credit report.¬†All sorts of things that they can¬†see are not on your credit report. So this big data coming through for them allowing them to make credit decisions which are far more informed.
  • one thing people often say about this market and it's true is that, this market in this current business model hasn't been tested by a big downturn. So we'll see what happens when the economy turns out to the performance of these loans. It's going to be a test, and maybe they pass that test, maybe they don't, that's yet to be seen.

Student Loan Debt

  • let's think about the opportunity that this can present to a lender entering this space. Okay, so, think about the graduates who are doing better. We've already seen the graduates who have hit a rough patch access income-driven payment but those that are doing better, you've got the job you hoped for and you expect rightly or wrongly that this is going to continue, you're going to have this high paying job going forward from here and you'd like, so now you're a low risk
  • Main business model of biggest player in the space called SoFi
  • a bit of a risk, a speculation on your part when you do refinance into a SoFi loan because you are giving up your right to enter income-driven repayment, right? That's a federal program, it's for federal student loans, right? If you're refinancing the SoFi, then you got to find out, well, what are they offering me? If I hit a rock, anyone can hit a rough patch for whatever reason, what are they offering me in that situation? So that's the trade-off.
  • Many people felt that, well, it's worth it, I like that low interest rate, I'm going to do that, and that's been driving the ramping up of these businesses.
  • let me add, by the way, that this whole business of refinancing people out of their student loans has kind of slowed down recently. Why would it slow down? Well, it slowed down because rates have gone up, rates have gone up, just the treasury rates have gone up.
  • because 10 year treasury rate has come up, the business model of these Fintech student lenders has gotten tougher, right? Because if you took out a student loan, let's say in 2016 the rates were really low, then that rate you got was keyed off of the very low ten-year rate of 2016.
  • So it's gotten harder for them to make money this way, the offer to refinance people out of their student loans. So if you look at these issuers, SoFi and so on, what you'll notice is, they're trying to transition away a little bit from that business model of focusing on refinance, to being the lender that loans to you in the first place. And of course now they're making it a little harder on themselves because they're loaning to you before you get a job or don't get a job and sort of taking a bigger risk.
  • it remains to be seen how well that goes for them and how well that works out for the borrowers who are now sort of depending on SoFi and so on to help them through the rough patches versus the federal plan.

Lending to small businesses

  • furthermore, and this is sort of the beauty part from their point of view, they've got a huge advantage over a bank. Right, so let's say a bank loaned you 10,000 bucks, they give you 10,000 bucks and now you have to pay them back 11,000 bucks. Well, of course the bank has to hope that you see it as worth your while to repay the loan. Then once they give you the 10,000 bucks, and now you've got money coming in and you have the bank loan, but you also have your payroll, you can buy more inventory, you can pay the rent. Whatever it is, you have all these expenses, the bank is just one of them. Maybe you pay the bank, maybe you don't, right? So you have some discretion over when you pay the bank and how much, okay? So the bank has to worry about that. So the big advantage of the payment aggregator, such as Square, is that all the money that people are spending at your store goes through them before it gets back to you.
  • this is not by itself a completely new idea, the idea of a merchant cash advance, where the institution processing your payments is taking the repayment directly out of your cash flow. This has been around for years. The sort of special sauce here is this kind of big data approach, that Square can see not just you but people like you elsewhere. People in your neighborhood transacting with sort of the same customer base, making their other sorts of transactions, the dry cleaners, the Bodega, whatever it is. They see that too, and that helps them think about your ability to repay the loan, and the use to which you could put the money from the loan.


  • when Square was started, it was trying to solve the problem that small businesses couldn't take credit cards. What that actually meant is that they couldn't make the sale. So, let me step back and explain what that means. Ten years ago, there was no mobile payment system for credit cards.
  • Jack and Jim, the two founders of Square created a little white dongle that's now somewhat iconic as being perceived as such a core design of Square. That dongle plugged into the headphone jack of a mobile device. What that enabled a small business to do was two things. First, they could take the credit card, very simple. Within a few minutes, they were able to get up and running and have access to the payment system very simply in a mobile device that they might already own. The second thing it enabled them to do was actually get into the financial system so that they could make the sale. Prior to Square, the way credit card companies worked is that it took weeks of getting access to a seller's personal data. In order to be given a credit card account, they had to be underwritten for risk. With Square, we really changed the paradigm of what risk looks like so that a seller was able to be on-boarded onto the system immediately and only removed from the system if anything looked off.
  • there are four interesting dynamics happening in FinTech today. First, there's a disaggregation between the data and the data source. So you're seeing interesting businesses built around APIs that have really separated the customer experience and the backend sources of data. I think that'll be really interesting as different combinations of data and end-user tools are pulled together to create a great experience. Second, user-interface so much cleaner. We're very used to financial apps that are complicated, lots of words, hard to use. I think that is completely changing with very simple apps that consumers can easily use. Next and related is really design. You're seeing a lot of design first products. It's making a huge difference in the ability to use them. I think they're getting a lot of consumer residents across the country. Then, last, slightly different topic, but the ideas around blockchain are having a significant impact on the invention of infrastructure related to the financial system. As we see transactions changing around the world and evolving around the world, you'll start to see more blockchain technology deployed across lots of different financial companies around the world.
  • I think business models and banks will have a significant change over time. If you look at two of the big core businesses today, wealth management and lending, both of them are seeing a significant amount of interest from fintechs who are really disrupting the user experience and the product flow of both of those significantly impactful industries within finance. Over the course of 20 years, I would expect to see models changing, the consumers having more power in how their product is used
  • I think the one fundamental issue that will remain within banking is that there's always going to be a cost to capital. I think banks do have an edge around regulatory compliance, the way they operate in terms of their scale and the cost of capital. They're going to have to work with that advantage to figure out how they evolve over time.
  • One of the biggest challenges we're facing right now is related to privacy. There's a lot of interest in looking at privacy law around the world. We've seen a lot of changes with the introduction of GDPR in Europe. The challenge that we're facing in the United States is that there is a very significant interest in pursuing some type of legislation and regulatory regime around privacy, yet it's being adopted and evaluated on a state-by-state basis. It's hard to have national level products when you could have such variation from state to state. I think the challenge is going to be how to deal with building legislation and momentum on a national level so that we can create one regime that makes sense for all of the United States