WEBVTT 00:00:00.500 --> 00:00:01.473 [RUSTLING] 00:00:01.473 --> 00:00:02.455 [CLICKING] 00:00:02.455 --> 00:00:06.383 [SQUEAKING] 00:00:11.012 --> 00:00:12.460 GARY GENSLER: Welcome to FinTech-- 00:00:12.460 --> 00:00:15.400 Shaping the World of Finance. 00:00:15.400 --> 00:00:18.160 We're going to talk about challenger banks today. 00:00:18.160 --> 00:00:22.360 And we've chatted in our last session about credit, payment 00:00:22.360 --> 00:00:23.470 before that. 00:00:23.470 --> 00:00:26.590 But challenger banks is an interesting topic 00:00:26.590 --> 00:00:28.790 that I thought we should take a moment 00:00:28.790 --> 00:00:31.670 and just talk about challenger banks. 00:00:31.670 --> 00:00:37.290 This is this concept built upon building a bank that's 00:00:37.290 --> 00:00:44.070 internet-only, building a bank that has no bricks and mortar 00:00:44.070 --> 00:00:45.630 office. 00:00:45.630 --> 00:00:47.763 Now, it's not actually a new concept. 00:00:47.763 --> 00:00:50.430 And in this lecture, we're going to talk a little bit about what 00:00:50.430 --> 00:00:54.600 was happening even then in the 1990s and the 2000s 00:00:54.600 --> 00:00:58.050 when online banking started. 00:00:58.050 --> 00:01:01.260 But this term, challenger bank, and another related term, 00:01:01.260 --> 00:01:07.140 neobanks, relates to something a little bit later. 00:01:07.140 --> 00:01:14.220 Ally Bank, which used to be the old auto lending 00:01:14.220 --> 00:01:16.350 part of General Motors, the largest-- 00:01:16.350 --> 00:01:19.350 at one point, at one point the largest auto manufacturer 00:01:19.350 --> 00:01:21.110 in the US-- 00:01:21.110 --> 00:01:25.400 Ally Bank in 2009 decided it was not just going 00:01:25.400 --> 00:01:26.275 to be an auto lender. 00:01:26.275 --> 00:01:27.567 They're going to become a bank. 00:01:27.567 --> 00:01:29.210 And in 2009, they said they were going 00:01:29.210 --> 00:01:31.640 to be an internet-only bank. 00:01:31.640 --> 00:01:35.030 Over in Europe, and particularly the United Kingdom, 00:01:35.030 --> 00:01:37.890 many people say challenger banks started in Europe, 00:01:37.890 --> 00:01:40.070 in the United Kingdom, particularly 00:01:40.070 --> 00:01:42.710 when the official sector got involved 00:01:42.710 --> 00:01:44.960 and through something called the Payment Systems 00:01:44.960 --> 00:01:47.540 Directive and the Payment System Directive 2 00:01:47.540 --> 00:01:50.750 and then later in the United Kingdom around open banking-- 00:01:50.750 --> 00:01:54.600 we've talked about this, about open APIs-- 00:01:54.600 --> 00:01:57.600 that it opened up the system a little bit. 00:01:57.600 --> 00:02:04.290 And by 2013 to 2015, you saw a slew of these challenger banks 00:02:04.290 --> 00:02:08.610 and neobanks getting into this space, 00:02:08.610 --> 00:02:12.720 first particularly in Europe, then a lot in the US, 00:02:12.720 --> 00:02:13.920 now around the globe. 00:02:13.920 --> 00:02:18.030 You can find challenger banks and neobanks in Brazil, 00:02:18.030 --> 00:02:21.690 in India, and Southeast Asia, in China. 00:02:21.690 --> 00:02:25.020 Some of them have been started by big tech companies. 00:02:25.020 --> 00:02:27.000 And in the last few years, you have 00:02:27.000 --> 00:02:31.110 big finance reacting as well, like Goldman Sachs starting 00:02:31.110 --> 00:02:33.040 Marcus. 00:02:33.040 --> 00:02:36.240 And so now the term gets a little loose. 00:02:36.240 --> 00:02:39.280 Is a challenger bank and neobank if Goldman Sachs 00:02:39.280 --> 00:02:40.210 is starting Marcus? 00:02:40.210 --> 00:02:42.010 That doesn't feel like a challenger bank. 00:02:42.010 --> 00:02:47.020 Goldman Sachs is established 140, 150-year-old investment 00:02:47.020 --> 00:02:48.290 bank. 00:02:48.290 --> 00:02:50.230 So it gets a little blurry. 00:02:50.230 --> 00:02:52.720 And I admit that in this space, there's sometimes 00:02:52.720 --> 00:02:55.850 terms don't fit well together. 00:02:55.850 --> 00:02:57.550 I'm going to upload the slides here 00:02:57.550 --> 00:02:58.930 for a second and share slides. 00:02:58.930 --> 00:03:02.268 But Romain is there any questions as we're starting? 00:03:02.268 --> 00:03:03.310 TA: Nothing so far, Gary. 00:03:13.415 --> 00:03:15.578 GARY GENSLER: So we're going to go back to a topic 00:03:15.578 --> 00:03:17.120 that we didn't have quite enough time 00:03:17.120 --> 00:03:20.990 to finish in class seven, credit scoring and alternative data. 00:03:20.990 --> 00:03:23.270 And it's an important topic that underlies 00:03:23.270 --> 00:03:25.820 some of what's going on with challenger backs, not 00:03:25.820 --> 00:03:27.420 everything. 00:03:27.420 --> 00:03:30.290 And then we're going to talk about some traditional banking, 00:03:30.290 --> 00:03:33.740 online banking as it sort of began in the late '90S 00:03:33.740 --> 00:03:34.760 and in the noughts. 00:03:34.760 --> 00:03:39.380 And what is this new wave, neo or challenging banks, 00:03:39.380 --> 00:03:44.420 some of how they're funded and their own valuations, and then 00:03:44.420 --> 00:03:48.410 the big finance reaction. 00:03:48.410 --> 00:03:50.740 So back to data-- 00:03:50.740 --> 00:03:52.410 there is a company. 00:03:52.410 --> 00:03:54.150 Many of us know it as FICO. 00:03:54.150 --> 00:03:59.370 It's actually called the Fair Isaac Company, started 60 00:03:59.370 --> 00:04:04.050 some years ago, started by two Stanford graduates, 00:04:04.050 --> 00:04:08.460 not MIT, but a very good institution. 00:04:08.460 --> 00:04:11.070 And they were more into data analytics. 00:04:11.070 --> 00:04:14.430 And remember, credit cards had started in the 1950s. 00:04:14.430 --> 00:04:17.550 And here it was, the late '50s, early 60s, 00:04:17.550 --> 00:04:24.882 a fintech company of its time trying to do analysis on data. 00:04:24.882 --> 00:04:28.370 A gentleman named Fair and a gentleman named Isaac-- 00:04:28.370 --> 00:04:29.840 I don't recall their first names, 00:04:29.840 --> 00:04:31.460 I think one was Bill Isaac-- 00:04:31.460 --> 00:04:32.570 start this company. 00:04:32.570 --> 00:04:34.850 It's a data analytics company. 00:04:34.850 --> 00:04:39.290 And by the late 1970s, it had taken hold. 00:04:39.290 --> 00:04:42.530 By the 1980s, it was the dominant player 00:04:42.530 --> 00:04:47.400 for consumer credit ratings. 00:04:47.400 --> 00:04:52.120 And their system is now used in 32 countries. 00:04:52.120 --> 00:04:56.660 And many other companies are using the FICO score system. 00:04:56.660 --> 00:04:58.513 But it's only one system. 00:04:58.513 --> 00:05:00.430 And this gives you a little bit of background. 00:05:00.430 --> 00:05:04.300 It's about what your amounts that you owe, the payment 00:05:04.300 --> 00:05:10.720 history on those credit cards and other bank relations. 00:05:10.720 --> 00:05:12.460 Are you taking out new credit? 00:05:12.460 --> 00:05:16.000 We could do a whole class just on FICO scores. 00:05:16.000 --> 00:05:18.840 They have updated it at least eight times. 00:05:18.840 --> 00:05:20.800 And in the summer of 2020, I think 00:05:20.800 --> 00:05:22.990 they're rolling out FICO 9.0. 00:05:22.990 --> 00:05:24.880 It's possibly FICO 10.0. 00:05:24.880 --> 00:05:29.590 So it's not static, but it's built in another era. 00:05:29.590 --> 00:05:33.790 It's built in an era about your payment history 00:05:33.790 --> 00:05:36.850 around your bank credit. 00:05:36.850 --> 00:05:38.470 It doesn't include many of the things 00:05:38.470 --> 00:05:41.280 that we would normally think to include. 00:05:41.280 --> 00:05:44.050 And so what is it that we're going through now? 00:05:44.050 --> 00:05:45.550 We're going through a period of time 00:05:45.550 --> 00:05:47.383 where folks are saying, well, wait a minute, 00:05:47.383 --> 00:05:50.740 maybe there are other ways, alternative ways 00:05:50.740 --> 00:05:54.730 to figure out and assess your credit. 00:05:54.730 --> 00:05:56.440 And it's called alternative data. 00:05:56.440 --> 00:06:00.130 Alternative Data in a sense is just alternative to whatever 00:06:00.130 --> 00:06:02.320 FICO might be using. 00:06:02.320 --> 00:06:05.080 This could be your bank, checking, employment, income, 00:06:05.080 --> 00:06:07.150 insurance, tenant, utilities. 00:06:07.150 --> 00:06:09.610 All of this currently is not in FICO scoring. 00:06:09.610 --> 00:06:13.450 Now, in some countries, employment data and income data 00:06:13.450 --> 00:06:18.130 is already and has been for decades used for consumer 00:06:18.130 --> 00:06:19.870 credit underwriting. 00:06:19.870 --> 00:06:22.750 But in the US and up to 30 other countries 00:06:22.750 --> 00:06:25.870 using the FICO scores, a lot of this, 00:06:25.870 --> 00:06:28.720 even this classic information like did you 00:06:28.720 --> 00:06:33.040 pay your rent on time, did you pay your utilities on time, 00:06:33.040 --> 00:06:36.160 is not necessarily included in FICO. 00:06:36.160 --> 00:06:40.300 It's partly because they didn't have the data in the 1970s 00:06:40.300 --> 00:06:43.210 when Fair and Isaac were doing this. 00:06:43.210 --> 00:06:45.280 And eight versions later, they still 00:06:45.280 --> 00:06:49.340 haven't included a lot of utility and tenancy. 00:06:49.340 --> 00:06:53.170 OK, secondly, there's something called cash flow underwriting. 00:06:53.170 --> 00:06:56.920 And if we go to China and we go to payment wallets 00:06:56.920 --> 00:07:01.470 around the globe, but let's stay with China, Alipay and WeChat 00:07:01.470 --> 00:07:05.390 Pay now can really see a lot of your cash flow, 00:07:05.390 --> 00:07:08.530 not just your expenditures, but your revenues, if you're 00:07:08.530 --> 00:07:15.100 moving your income in on some regular basis into a wallet. 00:07:15.100 --> 00:07:18.280 And cash flow underwriting is even more relevant-- again, 00:07:18.280 --> 00:07:21.580 let's stay with Alipay for a moment-- 00:07:21.580 --> 00:07:25.150 about small and medium-sized enterprises. 00:07:25.150 --> 00:07:28.510 So whether it's Alipay in China or Toast, 00:07:28.510 --> 00:07:31.390 a payment start up in the restaurant business here 00:07:31.390 --> 00:07:35.860 in the US, Toast can start to see all the revenues 00:07:35.860 --> 00:07:39.790 and expenditures of a restaurant and sort of make a credit 00:07:39.790 --> 00:07:41.940 decision based upon that. 00:07:41.940 --> 00:07:44.440 So cash flow underwriting is a very important thing. 00:07:44.440 --> 00:07:47.380 You don't necessarily need machine learning 00:07:47.380 --> 00:07:51.700 or AI to incorporate this alternative data. 00:07:51.700 --> 00:07:56.270 But the alternative data is something that FICO doesn't do. 00:07:56.270 --> 00:08:01.090 And classic banking, doing small and medium-sized lending, 00:08:01.090 --> 00:08:07.070 doesn't necessarily do good cash flow underwriting. 00:08:07.070 --> 00:08:10.235 Romain any questions? 00:08:10.235 --> 00:08:11.540 TA: None so far on this topic. 00:08:11.540 --> 00:08:13.457 We got one question from Luke that perhaps you 00:08:13.457 --> 00:08:16.070 can touch upon later on why online banking started 00:08:16.070 --> 00:08:19.440 in Europe versus other regions. 00:08:19.440 --> 00:08:20.440 GARY GENSLER: OK, OK. 00:08:20.440 --> 00:08:26.070 Let me hold the Luke question when we get into that as well. 00:08:26.070 --> 00:08:27.880 Your consumption, your purchase data-- 00:08:27.880 --> 00:08:30.250 Visa and Mastercard already are looking at this 00:08:30.250 --> 00:08:32.260 to cross-sell to us. 00:08:32.260 --> 00:08:35.169 And others are looking at it to cross-sell. 00:08:35.169 --> 00:08:39.460 If you're buying sports equipment or hiking equipment, 00:08:39.460 --> 00:08:42.100 we, all of a sudden, get advertising for it. 00:08:42.100 --> 00:08:45.910 But can alternative data, like what you buy, 00:08:45.910 --> 00:08:47.110 relate to your credit? 00:08:47.110 --> 00:08:49.000 Well, let's talk about something that's been 00:08:49.000 --> 00:08:50.990 known for some time. 00:08:50.990 --> 00:08:52.960 I don't know how many of you would 00:08:52.960 --> 00:08:57.820 have ever, when you went to replace an automobile tire, 00:08:57.820 --> 00:08:59.740 decide to get it retreaded. 00:08:59.740 --> 00:09:05.130 Literally, a retread is putting rubber, new rubber 00:09:05.130 --> 00:09:07.980 on the old tire. 00:09:07.980 --> 00:09:10.830 Now, retreads or something that we 00:09:10.830 --> 00:09:13.050 don't usually talk about in a finance course. 00:09:13.050 --> 00:09:15.420 But it has been shown that those who 00:09:15.420 --> 00:09:20.370 buy retreads are a little bit more susceptible to credit 00:09:20.370 --> 00:09:25.320 risk, meaning default, meaning they're a higher risk credit. 00:09:25.320 --> 00:09:28.260 What if you could track people's purchases, 00:09:28.260 --> 00:09:33.480 whether it's automobile retreads versus new tires-- 00:09:33.480 --> 00:09:36.000 you see the difference-- or anything else 00:09:36.000 --> 00:09:38.580 that in the purchase history gives you a sense, 00:09:38.580 --> 00:09:41.490 is this a higher risk credit or a lower risk credit? 00:09:41.490 --> 00:09:44.550 You can take purchase and consumption data 00:09:44.550 --> 00:09:48.870 and tie it to are they higher insurance risk? 00:09:48.870 --> 00:09:52.046 Are they a higher risk in their credit? 00:09:52.046 --> 00:09:53.830 Now, you have to be careful. 00:09:53.830 --> 00:09:59.370 You have to be careful to comply with local law and societal 00:09:59.370 --> 00:10:04.500 norms about not using alternative data that might be 00:10:04.500 --> 00:10:06.360 suggestive of something else. 00:10:06.360 --> 00:10:09.320 It might be suggestive of cultural differences, 00:10:09.320 --> 00:10:12.630 of race or gender or ethnic background 00:10:12.630 --> 00:10:14.590 or sexual orientation. 00:10:14.590 --> 00:10:16.740 And if your consumption of purchase data 00:10:16.740 --> 00:10:20.520 has to do with that and you end up with something in the US, 00:10:20.520 --> 00:10:22.440 you could easily be on the wrong side 00:10:22.440 --> 00:10:24.720 of what's called the Equal Credit Opportunity 00:10:24.720 --> 00:10:28.950 Act, that you have to basically be fair and unbiased 00:10:28.950 --> 00:10:31.720 in your extension of credit. 00:10:31.720 --> 00:10:33.980 And this is sort of at the cutting ground 00:10:33.980 --> 00:10:37.280 of alternative data, is how does alternative data 00:10:37.280 --> 00:10:43.850 get used, but not inadvertently, or worse, purposely, 00:10:43.850 --> 00:10:46.200 trip up fairness and bias issues. 00:10:46.200 --> 00:10:50.030 And we saw that recently with Apple Credit Card. 00:10:50.030 --> 00:10:55.490 Apple Credit Card rolled out in the spring of 2019-- 00:10:55.490 --> 00:10:59.030 a partnership between Apple, Goldman Sachs' Marcus 00:10:59.030 --> 00:11:02.780 and Mastercard, a new, innovative thing 00:11:02.780 --> 00:11:04.880 that big tech was rolling out with big 00:11:04.880 --> 00:11:07.160 find in the spring of 2019. 00:11:07.160 --> 00:11:10.550 And all of a sudden, a venture capitalist 00:11:10.550 --> 00:11:13.490 runs into the Apple Credit Card and finds 00:11:13.490 --> 00:11:17.240 that he and his spouse were going to get 00:11:17.240 --> 00:11:19.690 10 times different credit. 00:11:19.690 --> 00:11:21.550 They file tax returns together. 00:11:21.550 --> 00:11:25.700 They were together, apparently worth many millions of dollars, 00:11:25.700 --> 00:11:27.370 maybe centimillionaire. 00:11:27.370 --> 00:11:31.180 And he starts to advertise on Twitter 00:11:31.180 --> 00:11:34.120 and promote that the Apple Credit Card had a problem. 00:11:34.120 --> 00:11:36.820 And then Steve Wozniak, the co-founder of Apple, 00:11:36.820 --> 00:11:40.120 publicly announces that he ran into the same problem 00:11:40.120 --> 00:11:42.070 with his wife and himself. 00:11:42.070 --> 00:11:44.110 Again, they filed their taxes together. 00:11:44.110 --> 00:11:46.750 They're both extraordinarily wealthy. 00:11:46.750 --> 00:11:49.180 Why would one have 10 times or 20 times more 00:11:49.180 --> 00:11:50.800 the credit than the other? 00:11:50.800 --> 00:11:53.300 There seemed to be, for whatever reason, 00:11:53.300 --> 00:11:57.970 some gender bias in the Apple Credit allocation alternative 00:11:57.970 --> 00:11:59.650 data system. 00:11:59.650 --> 00:12:02.870 Now, we don't know yet to this day what it was. 00:12:02.870 --> 00:12:06.130 But what we do know is that Apple Credit Cards stubbed 00:12:06.130 --> 00:12:08.710 their toe on their rollout. 00:12:08.710 --> 00:12:10.390 Sometimes it's about the law. 00:12:10.390 --> 00:12:13.180 Sometimes, though, it's about business. 00:12:13.180 --> 00:12:16.270 Sometimes it's about breaking societal norms. 00:12:16.270 --> 00:12:19.600 But in that case, Apple had a tough rollout. 00:12:19.600 --> 00:12:21.340 They roll out Apple Credit Card. 00:12:21.340 --> 00:12:23.230 And yet they have the co-founder, 00:12:23.230 --> 00:12:26.380 former founder of Apple saying not so fast, 00:12:26.380 --> 00:12:29.240 you've got a problem here. 00:12:29.240 --> 00:12:33.070 The other sets of data is our social footprint, 00:12:33.070 --> 00:12:35.710 our social and media footprint-- app usage, 00:12:35.710 --> 00:12:38.950 browsing history, the like, geolocation, et cetera. 00:12:38.950 --> 00:12:42.070 And in this world of coronavirus and the crisis, 00:12:42.070 --> 00:12:44.770 we know that geolocation is going to be used 00:12:44.770 --> 00:12:48.790 and is already likely being used for a lot of contact 00:12:48.790 --> 00:12:51.880 tracing in many countries, certainly 00:12:51.880 --> 00:12:54.040 in Korea from the news reports. 00:12:54.040 --> 00:12:58.070 And I'm guessing Luke could tell us more about that. 00:12:58.070 --> 00:13:02.990 But we know that contact tracing and all 00:13:02.990 --> 00:13:07.630 of the attributes of figuring out who we are as an individual 00:13:07.630 --> 00:13:09.610 are being traced more and more. 00:13:09.610 --> 00:13:13.510 In China, there's a social credit scoring system 00:13:13.510 --> 00:13:18.490 that is an official part of the government's 00:13:18.490 --> 00:13:20.650 approach to extending credit. 00:13:20.650 --> 00:13:23.030 It might be whether you can buy an airline ticket. 00:13:23.030 --> 00:13:26.080 It may be whether you can even get on the commuter rail 00:13:26.080 --> 00:13:29.320 system, depending upon your app usage. 00:13:29.320 --> 00:13:32.920 And that app usage in China even includes, as I 00:13:32.920 --> 00:13:35.260 understand it, not just your-- 00:13:35.260 --> 00:13:37.180 what you're doing on Alipay or WeChat 00:13:37.180 --> 00:13:40.780 Pay, but also what you're doing on dating websites. 00:13:40.780 --> 00:13:43.510 Literally, I repeat on dating websites, 00:13:43.510 --> 00:13:48.520 and whether and how you're on any website or any app 00:13:48.520 --> 00:13:53.420 could be a little bit like the tire retreading, 00:13:53.420 --> 00:13:54.510 that you might be-- 00:13:54.510 --> 00:13:56.810 you might find, through the alternative data, 00:13:56.810 --> 00:13:59.100 that it's a little different. 00:13:59.100 --> 00:14:03.300 Here in the US, many fintech companies 00:14:03.300 --> 00:14:05.190 are approaching the regulators and saying 00:14:05.190 --> 00:14:08.100 we don't want to trip up and be on the wrong side of the Equal 00:14:08.100 --> 00:14:09.540 Credit Opportunity Act. 00:14:09.540 --> 00:14:11.820 We don't want to be on the wrong side of other laws, 00:14:11.820 --> 00:14:14.730 like Fair Credit Reporting, Truth in Lending Act, 00:14:14.730 --> 00:14:17.160 and a series of other laws. 00:14:17.160 --> 00:14:19.590 Please help us, regulators, interpret 00:14:19.590 --> 00:14:23.220 how we can use alternative data, how we can actually 00:14:23.220 --> 00:14:28.870 be more inclusive, provide more and better finance 00:14:28.870 --> 00:14:32.440 to more people at a lower cost, but not 00:14:32.440 --> 00:14:36.250 be on the wrong side of these important societal 00:14:36.250 --> 00:14:38.060 and legal norms. 00:14:38.060 --> 00:14:40.780 So it's a very interesting place. 00:14:40.780 --> 00:14:46.240 I'll use one example from a conversation with Lending Club. 00:14:46.240 --> 00:14:50.440 I had the honor to be on a stage-- 00:14:50.440 --> 00:14:52.960 before the crisis, one of the last conferences 00:14:52.960 --> 00:14:55.000 was an MIT fintech conference. 00:14:55.000 --> 00:14:58.510 And I was on the stage with the CEO of Lending Club 00:14:58.510 --> 00:15:02.710 and having a good interview, a fireside chat. 00:15:02.710 --> 00:15:06.340 And I asked about alternative data and how they use it. 00:15:06.340 --> 00:15:07.760 They use it. 00:15:07.760 --> 00:15:10.650 But what they find is in using alternative data 00:15:10.650 --> 00:15:13.570 in that marketplace lending company, 00:15:13.570 --> 00:15:18.100 they then use machine learning on the alternative data. 00:15:18.100 --> 00:15:22.030 They find some what they believe to be correlations. 00:15:22.030 --> 00:15:26.530 And then they just use straight up regression analysis. 00:15:26.530 --> 00:15:30.850 Once they identify an attribute, they 00:15:30.850 --> 00:15:32.920 put the machine learning to the side. 00:15:32.920 --> 00:15:33.970 They've used it. 00:15:33.970 --> 00:15:38.320 But then they sort of then go to the down-scaled old way, 00:15:38.320 --> 00:15:39.760 just straight up. 00:15:39.760 --> 00:15:42.560 That's an attribute we've identified. 00:15:42.560 --> 00:15:44.780 Tire retreads matter. 00:15:44.780 --> 00:15:49.060 Let's just use it that way. 00:15:49.060 --> 00:15:52.360 So they do it by trying to find some attributes 00:15:52.360 --> 00:15:56.420 and then move on, as I understood the conversation. 00:15:56.420 --> 00:15:59.830 So there's a tremendous ecosystem in alternative data, 00:15:59.830 --> 00:16:00.370 too. 00:16:00.370 --> 00:16:02.110 This is just a slide that pulled. 00:16:02.110 --> 00:16:04.680 You can readily get these types of slides on the internet. 00:16:04.680 --> 00:16:09.440 But this is from CB Insights as to alternative data sources. 00:16:09.440 --> 00:16:10.720 This is a bunch of companies-- 00:16:10.720 --> 00:16:13.390 I wouldn't necessarily call them fintech startups, 00:16:13.390 --> 00:16:15.640 but they're companies trying to service, 00:16:15.640 --> 00:16:18.550 here's how we can help you either with machine 00:16:18.550 --> 00:16:21.160 learning, AI, or actually sources 00:16:21.160 --> 00:16:24.160 of data in this whole space. 00:16:24.160 --> 00:16:28.270 So I'm going to move on to the challenger bank discussion, 00:16:28.270 --> 00:16:30.512 but pause for a moment to see if there are questions. 00:16:30.512 --> 00:16:32.470 I know Luke had a question, but that was really 00:16:32.470 --> 00:16:33.620 about challenger banks. 00:16:33.620 --> 00:16:35.025 So [Romain? 00:16:35.025 --> 00:16:38.350 TA: Hassan. 00:16:38.350 --> 00:16:40.480 AUDIENCE: Hi, yeah. 00:16:40.480 --> 00:16:42.250 I mean, if you could just elaborate 00:16:42.250 --> 00:16:44.825 on cash flow underwriting. 00:16:44.825 --> 00:16:47.040 I do not quite get it. 00:16:47.040 --> 00:16:49.270 GARY GENSLER: So let's do it in a small business. 00:16:49.270 --> 00:16:55.330 And let's use either the example of toast in the US 00:16:55.330 --> 00:16:57.670 with restaurants or almost any business 00:16:57.670 --> 00:17:00.580 that Alipay has the wallet. 00:17:00.580 --> 00:17:04.599 What Alipay or Toast and see on that small business 00:17:04.599 --> 00:17:07.420 or restaurant is they can see their revenues 00:17:07.420 --> 00:17:10.304 and they can see their expenditures to the extent 00:17:10.304 --> 00:17:14.260 that it's kept inside that payment architecture. 00:17:14.260 --> 00:17:18.760 So in Alipay, they've incentivized the millions 00:17:18.760 --> 00:17:23.290 of merchants using Alipay not only to receive revenues 00:17:23.290 --> 00:17:27.280 into their Alipay Wallet, but also to pay their suppliers 00:17:27.280 --> 00:17:29.470 with the Alipay Wallet. 00:17:29.470 --> 00:17:32.620 So what Alipay can do and does do 00:17:32.620 --> 00:17:37.335 is they can start to see the entire picture or 80% or 90% 00:17:37.335 --> 00:17:37.960 of the picture. 00:17:37.960 --> 00:17:40.960 They might not see the entire picture, 00:17:40.960 --> 00:17:47.560 but they can literally build a whole 360 degrees around them. 00:17:47.560 --> 00:17:50.500 Or take something like Intuit here in the US, 00:17:50.500 --> 00:17:52.390 which is the company that was founded 00:17:52.390 --> 00:17:57.070 20 plus years ago that has the most dominant sort of tax 00:17:57.070 --> 00:17:59.800 software, TurboTax. 00:17:59.800 --> 00:18:02.290 Intuit is also in the fintech space. 00:18:02.290 --> 00:18:05.500 They recently announced buying Credit Karma 00:18:05.500 --> 00:18:07.330 for billions of dollars. 00:18:07.330 --> 00:18:10.900 And they also have other fintech offerings, 00:18:10.900 --> 00:18:14.920 where they can maybe build a greater picture. 00:18:14.920 --> 00:18:17.050 I call it cash flow underwriting. 00:18:17.050 --> 00:18:19.660 It means underwriting, assessing the risk, 00:18:19.660 --> 00:18:23.830 because you can see the revenues and expenditures and build, 00:18:23.830 --> 00:18:28.750 in essence, an income statement on the merchant. 00:18:28.750 --> 00:18:33.670 Amazon can do some of that, not completely as well as Alipay. 00:18:33.670 --> 00:18:36.790 But Amazon can do some of that through Amazon Prime 00:18:36.790 --> 00:18:39.700 and what they can see on the merchant. 00:18:39.700 --> 00:18:41.190 Did that help? 00:18:41.190 --> 00:18:44.270 AUDIENCE: Thank you. 00:18:44.270 --> 00:18:46.620 GARY GENSLER: And it gives them a competitive advantage. 00:18:46.620 --> 00:18:50.220 And it's part of why getting into the payment space 00:18:50.220 --> 00:18:52.800 before getting into the credit space 00:18:52.800 --> 00:18:56.250 is a business model, particularly in big tech. 00:18:56.250 --> 00:18:58.290 Now, I'm not saying that's what was 00:18:58.290 --> 00:19:01.770 the initial reason that Alipay got into the payment space. 00:19:01.770 --> 00:19:05.100 Alipay was trying to promote their online platform. 00:19:05.100 --> 00:19:07.470 Amazon got into the payment space 00:19:07.470 --> 00:19:09.580 to promote their online platform. 00:19:09.580 --> 00:19:12.720 And even Toast, the restaurant software business, 00:19:12.720 --> 00:19:17.760 got into the restaurant business to sell tablets. 00:19:17.760 --> 00:19:21.210 They were thinking we'll sell the better hardware 00:19:21.210 --> 00:19:25.590 to the restaurateurs, because this tablet was 00:19:25.590 --> 00:19:27.870 important for the servers, what we 00:19:27.870 --> 00:19:29.520 might call waiters and waitresses, 00:19:29.520 --> 00:19:31.530 walking around a restaurant. 00:19:31.530 --> 00:19:35.130 But what they quickly found is that the Toast-- 00:19:35.130 --> 00:19:36.420 this is the example-- 00:19:36.420 --> 00:19:39.180 they pivoted from being a hardware tablet business 00:19:39.180 --> 00:19:40.560 to being a payment business. 00:19:40.560 --> 00:19:43.800 And then they start to be a credit business as well, 00:19:43.800 --> 00:19:47.910 because the data they're receiving in a partial cash 00:19:47.910 --> 00:19:51.630 flow underwriting-- they don't have a full 360-- 00:19:51.630 --> 00:19:54.450 allows them to start to underwrite in that business. 00:19:54.450 --> 00:19:57.570 And frankly, they're likely to be able to do it better 00:19:57.570 --> 00:19:59.310 than a traditional bank. 00:19:59.310 --> 00:20:01.410 A traditional bank that might get 00:20:01.410 --> 00:20:04.770 annual or quarterly financials will not 00:20:04.770 --> 00:20:08.880 have quite the up-to-date sensitivity as to how 00:20:08.880 --> 00:20:09.880 that restaurant's doing. 00:20:13.295 --> 00:20:17.450 TA: And we have a question from Danielle about data aggregators 00:20:17.450 --> 00:20:17.992 on the slide. 00:20:17.992 --> 00:20:19.200 GARY GENSLER: Please, please. 00:20:19.200 --> 00:20:21.080 TA: Are the different data aggregator players 00:20:21.080 --> 00:20:23.860 for the purposes of selling to fintech customers 00:20:23.860 --> 00:20:26.120 or for selling to companies who want 00:20:26.120 --> 00:20:29.250 to use the data for personalized advertising? 00:20:29.250 --> 00:20:30.530 And if yes, why? 00:20:30.530 --> 00:20:32.983 Do you anticipate any consolidation? 00:20:32.983 --> 00:20:34.400 GARY GENSLER: No, I think Danielle 00:20:34.400 --> 00:20:35.810 raised a good question. 00:20:35.810 --> 00:20:37.220 They're data aggregators. 00:20:37.220 --> 00:20:40.970 And the terms, again, are not terribly well-defined. 00:20:40.970 --> 00:20:44.450 They're data aggregators that could be there, 00:20:44.450 --> 00:20:48.560 as Danielle points out, to cross-market, to sell. 00:20:48.560 --> 00:20:52.040 And even-- let's use Credit Karma for a minute. 00:20:52.040 --> 00:20:57.020 Credit Karma initially was about providing free access 00:20:57.020 --> 00:20:58.970 to your credit report. 00:20:58.970 --> 00:21:01.790 That was their initial insight. 00:21:01.790 --> 00:21:06.290 What they found over time is they could cross-sell 00:21:06.290 --> 00:21:09.660 and become a friendly website for free 00:21:09.660 --> 00:21:11.480 to give you access to credit. 00:21:11.480 --> 00:21:13.040 They were cross-selling. 00:21:13.040 --> 00:21:16.720 But then they were also collecting a fee. 00:21:16.720 --> 00:21:18.340 And they were collecting more data. 00:21:18.340 --> 00:21:23.140 And inevitably, they had 100 plus million files, 00:21:23.140 --> 00:21:26.590 even though they had much fewer customers. 00:21:26.590 --> 00:21:30.100 But data aggregation could also come from another space. 00:21:30.100 --> 00:21:34.240 Plaid, Galileo, and others, we call them also 00:21:34.240 --> 00:21:35.350 data aggregators. 00:21:35.350 --> 00:21:38.920 And they started as software providers 00:21:38.920 --> 00:21:42.190 to provide an open API, as we discussed. 00:21:42.190 --> 00:21:45.430 But then they create a data warehouse. 00:21:45.430 --> 00:21:47.080 And they become data aggregators. 00:21:47.080 --> 00:21:51.170 So the term data aggregator can be used in many ways. 00:21:51.170 --> 00:21:54.490 One small way it can be used is the second way Danielle 00:21:54.490 --> 00:21:57.080 mentioned, and it's kind of mentioned on this slide 00:21:57.080 --> 00:21:58.480 of alternative data. 00:21:58.480 --> 00:22:03.440 There's some companies that just said we will collect data. 00:22:03.440 --> 00:22:06.050 FICO score doesn't have utility data. 00:22:06.050 --> 00:22:09.530 We'll collect the utility data and rental payment data. 00:22:09.530 --> 00:22:14.120 We believe it will become an alternative data aggregator. 00:22:14.120 --> 00:22:16.040 So yes, they're are data aggregator. 00:22:16.040 --> 00:22:20.000 But they're collecting data with the clear mind 00:22:20.000 --> 00:22:23.630 that they're going to enhance credit scoring. 00:22:23.630 --> 00:22:25.670 And as we turn now to challenger banks, 00:22:25.670 --> 00:22:27.420 challenger banks might say, look, 00:22:27.420 --> 00:22:31.010 we don't have the ability in our first year 00:22:31.010 --> 00:22:33.530 or even maybe 10 years of business 00:22:33.530 --> 00:22:36.680 to be the one collecting all that utility and rent payment 00:22:36.680 --> 00:22:38.750 or social media data. 00:22:38.750 --> 00:22:40.760 We want to turn to somebody else. 00:22:40.760 --> 00:22:42.770 You might create a business simply 00:22:42.770 --> 00:22:45.680 to say we are going to be a data aggregator 00:22:45.680 --> 00:22:50.120 to enhance the underwriting of these banks. 00:22:50.120 --> 00:22:52.190 There's hundreds of challenger banks. 00:22:52.190 --> 00:22:55.340 I want to be a service provider to those challenger banks. 00:22:55.340 --> 00:22:56.690 So Danielle, good question. 00:22:56.690 --> 00:22:58.195 It could be on marketing. 00:22:58.195 --> 00:22:59.570 It could be that you've got there 00:22:59.570 --> 00:23:03.020 because you were in the business of something else, payments 00:23:03.020 --> 00:23:05.780 or credit scoring, like Credit Karma. 00:23:05.780 --> 00:23:08.000 Or you could specifically say I just 00:23:08.000 --> 00:23:11.630 want to have a business model simply 00:23:11.630 --> 00:23:15.690 about an alternative data field that I think 00:23:15.690 --> 00:23:17.600 and my machine learning might tell me 00:23:17.600 --> 00:23:19.490 that alternative data field is going 00:23:19.490 --> 00:23:21.050 to give us better underwriting. 00:23:21.050 --> 00:23:23.570 And then I will sell to all the other fintechs. 00:23:23.570 --> 00:23:25.760 I will service fintechs by collecting data. 00:23:28.750 --> 00:23:31.105 Romain anything else, or-- 00:23:31.105 --> 00:23:33.366 TA: We have Nikhil and Brian with their hands up. 00:23:33.366 --> 00:23:34.116 Go ahead, you two. 00:23:34.116 --> 00:23:36.491 GARY GENSLER: OK, Nikhil or Brian and then we'll move on. 00:23:36.491 --> 00:23:38.740 AUDIENCE: I had a question around underwriting 00:23:38.740 --> 00:23:40.220 based on cash flows. 00:23:40.220 --> 00:23:42.490 So I wrote my paper on alternative data. 00:23:42.490 --> 00:23:44.470 And something that came out was most 00:23:44.470 --> 00:23:47.230 of the alternative data lending firms are 00:23:47.230 --> 00:23:48.620 focused on small businesses. 00:23:48.620 --> 00:23:53.710 So the Amazon example that you gave is for their suppliers. 00:23:53.710 --> 00:23:55.510 Is there regulation in the US that's 00:23:55.510 --> 00:24:00.160 preventing a cash-flow-based underwriting for customers? 00:24:00.160 --> 00:24:04.470 Because countries like Kenya have it with M-Shwari. 00:24:04.470 --> 00:24:07.480 Baidu piloted something with Zest AI. 00:24:07.480 --> 00:24:10.690 So just curious, what's limiting cash-flow-based underwriting 00:24:10.690 --> 00:24:12.700 in the US specifically? 00:24:12.700 --> 00:24:14.750 GARY GENSLER: I think it's a great question. 00:24:14.750 --> 00:24:18.220 I think it's not just regulatory. 00:24:18.220 --> 00:24:25.600 It's also that as of now, as of now, 00:24:25.600 --> 00:24:29.020 the folks they could probably do the best cash flow 00:24:29.020 --> 00:24:34.060 underwriting on us are the banks themselves, 00:24:34.060 --> 00:24:39.790 that Bank of America and Citi sees your rent payment going 00:24:39.790 --> 00:24:42.130 out, sees your Visa payments going out, 00:24:42.130 --> 00:24:45.070 and sees your wages coming in. 00:24:45.070 --> 00:24:49.370 I mean, literally the banks themselves. 00:24:49.370 --> 00:24:52.870 And yet, they have not, as I understand it, 00:24:52.870 --> 00:24:58.360 yet done a full 360 cash-flow underwriting. 00:24:58.360 --> 00:25:01.570 A number of them are enhancing their credit card products 00:25:01.570 --> 00:25:04.810 and enhancing those products by taking the FICO scores and then 00:25:04.810 --> 00:25:07.390 sort of like FICO plus a little. 00:25:07.390 --> 00:25:10.300 But interestingly, the big banks have collected together 00:25:10.300 --> 00:25:12.700 and formed something called Vantage, 00:25:12.700 --> 00:25:15.620 which is a competitor to FICO. 00:25:15.620 --> 00:25:19.000 And it's a sort of consortium amongst some big banks. 00:25:19.000 --> 00:25:22.690 But it doesn't fully include the income side. 00:25:22.690 --> 00:25:26.410 And I think it does relate to some of the regulatory issues 00:25:26.410 --> 00:25:28.120 here in the US. 00:25:28.120 --> 00:25:32.080 In the earlier wave, in the earlier wave of data analytics 00:25:32.080 --> 00:25:33.580 in the 1960s-- 00:25:33.580 --> 00:25:36.910 remember, credit cards came along in the '50s and '60s-- 00:25:36.910 --> 00:25:39.610 we passed two really important laws-- 00:25:39.610 --> 00:25:43.120 one about bias, called Equal Credit Opportunity Act, 00:25:43.120 --> 00:25:47.080 and one about credit reporting agencies. 00:25:47.080 --> 00:25:50.260 These companies like TransUnion and Equifax, 00:25:50.260 --> 00:25:53.680 they're regulated under the Fair Credit Reporting Act. 00:25:53.680 --> 00:25:57.340 And a lot of banks, they're just sort of 00:25:57.340 --> 00:25:59.890 trying to work through those issues. 00:25:59.890 --> 00:26:02.620 And again, this sets up the challenger bank discussion. 00:26:02.620 --> 00:26:06.020 It's part of why challenger banks around the globe-- 00:26:06.020 --> 00:26:07.960 they started more in Europe-- 00:26:07.960 --> 00:26:11.920 but the challenger banks around the globe had an alternative. 00:26:11.920 --> 00:26:13.900 This is one of the reasons I think that they 00:26:13.900 --> 00:26:17.240 have a bit of an alternative. 00:26:17.240 --> 00:26:18.920 I think this is where we're headed. 00:26:18.920 --> 00:26:24.980 We will, as individuals, be more looked at this way. 00:26:24.980 --> 00:26:27.320 One little story-- there was one other question, 00:26:27.320 --> 00:26:29.165 Romain I'm sorry, who was it? 00:26:29.165 --> 00:26:30.205 TA: Brian. 00:26:30.205 --> 00:26:32.170 GARY GENSLER: Brian and then one little story 00:26:32.170 --> 00:26:34.722 close out alternative data. 00:26:34.722 --> 00:26:36.430 AUDIENCE: Could you please talk about how 00:26:36.430 --> 00:26:40.450 legislation, such as GDPR and other similar legislation, 00:26:40.450 --> 00:26:42.397 has affected this space? 00:26:42.397 --> 00:26:43.730 GARY GENSLER: So great question. 00:26:43.730 --> 00:26:48.670 There's also privacy concerns about sharing my data. 00:26:48.670 --> 00:26:50.050 Now, we all share our data. 00:26:50.050 --> 00:26:53.230 That sort of horse is out of that barn. 00:26:53.230 --> 00:26:55.660 But then society sort of says wait, 00:26:55.660 --> 00:26:57.540 what's your protection here? 00:26:57.540 --> 00:27:05.170 You're at the forefront of this, passes the GDPR, the General 00:27:05.170 --> 00:27:08.320 Directive, and the P is not privacy. 00:27:08.320 --> 00:27:09.940 I'm sorry, I can't remember the-- 00:27:09.940 --> 00:27:11.335 AUDIENCE: Protection something. 00:27:11.335 --> 00:27:13.840 GARY GENSLER: Protection Regulation, data-- data 00:27:13.840 --> 00:27:16.990 protection, that's right, General Data Protection 00:27:16.990 --> 00:27:18.280 Regulation. 00:27:18.280 --> 00:27:22.730 And they passed GDPR, basically the right to be forgotten, 00:27:22.730 --> 00:27:24.820 the rights about your information, the right 00:27:24.820 --> 00:27:28.210 to basically review it and so forth. 00:27:28.210 --> 00:27:31.750 It has some similarities to those laws that 00:27:31.750 --> 00:27:37.000 were way back in the 1970s called Fair Credit Reporting 00:27:37.000 --> 00:27:40.090 Act, even, but a lot further. 00:27:40.090 --> 00:27:43.420 And what these privacy laws, and more recently in the US, 00:27:43.420 --> 00:27:48.010 the California Consumer Protection Act or CCPA-- 00:27:48.010 --> 00:27:52.030 we all got notices in 2019 and regarding from Google 00:27:52.030 --> 00:27:54.550 and others about the CCPA. 00:27:54.550 --> 00:27:58.510 What they've done is they've leaned into an ability for you 00:27:58.510 --> 00:28:01.960 to review your data and potentially edit your data 00:28:01.960 --> 00:28:04.780 and delete some of what's being tracked. 00:28:04.780 --> 00:28:10.330 It does raise some of the R data protection-- 00:28:10.330 --> 00:28:14.770 General Data Protection Regulation. 00:28:14.770 --> 00:28:20.080 But it doesn't go to the heart of whether using the data 00:28:20.080 --> 00:28:23.980 is going to lead to biased outcomes, which 00:28:23.980 --> 00:28:27.550 is about fairness and equal credit, 00:28:27.550 --> 00:28:31.570 or whether you're going to be regulated like a credit agency. 00:28:31.570 --> 00:28:37.670 So I think the GDPR and CCPA better protect the public. 00:28:37.670 --> 00:28:41.380 They also raise some of the costs and barriers 00:28:41.380 --> 00:28:42.920 to entry in this field. 00:28:42.920 --> 00:28:45.370 So there's some economics to it. 00:28:45.370 --> 00:28:49.630 I think they might also tend a little bit towards then 00:28:49.630 --> 00:28:53.470 homogeneic outcomes, meaning that all the banks will 00:28:53.470 --> 00:28:56.380 be doing similar things to protect themselves 00:28:56.380 --> 00:28:59.450 in the regulatory environment. 00:28:59.450 --> 00:29:01.190 So I think it's a consideration. 00:29:01.190 --> 00:29:03.490 It doesn't necessarily slow this down. 00:29:03.490 --> 00:29:06.190 It's also an opportunity for a fintech startup. 00:29:06.190 --> 00:29:10.780 If you think you can be GDPR-CCPA compliant 00:29:10.780 --> 00:29:15.160 and still be a really talented alternative data aggregator, 00:29:15.160 --> 00:29:17.290 that's an opportunity for you. 00:29:17.290 --> 00:29:20.650 If you're ahead of the regulatory curve and lower 00:29:20.650 --> 00:29:24.680 regulatory risk for the other startups, 00:29:24.680 --> 00:29:26.840 so that's an opportunity. 00:29:26.840 --> 00:29:28.820 One little comment on alternative data 00:29:28.820 --> 00:29:30.420 that I like to mention-- 00:29:30.420 --> 00:29:33.890 and I don't remember where I read this, so I apologize. 00:29:33.890 --> 00:29:37.850 I can't quote this-- the study accurately. 00:29:37.850 --> 00:29:42.740 But there has been a study about credit 00:29:42.740 --> 00:29:46.190 as it relates to charging your phones. 00:29:46.190 --> 00:29:48.460 And so I just pass this along to you, 00:29:48.460 --> 00:29:53.510 that if some alternative data provider and alternative data 00:29:53.510 --> 00:29:58.310 underwriter wants to assess whether you or I are 00:29:58.310 --> 00:30:04.170 good credit, apparently, if one charges your phone every night, 00:30:04.170 --> 00:30:08.952 we're more likely to repay our credit card and personal loans. 00:30:08.952 --> 00:30:10.910 And if you don't charge your phone every night, 00:30:10.910 --> 00:30:13.100 that's fine with me. 00:30:13.100 --> 00:30:15.120 That's totally fine. 00:30:15.120 --> 00:30:19.990 But apparently, you're a little bit higher risk for credit. 00:30:19.990 --> 00:30:23.110 That's what one survey has shown. 00:30:23.110 --> 00:30:24.013 And I apologize. 00:30:24.013 --> 00:30:25.930 I just don't even remember all the things I've 00:30:25.930 --> 00:30:27.790 read, where I've breaded this. 00:30:27.790 --> 00:30:34.290 And so what if some challenger bank just tracks 00:30:34.290 --> 00:30:36.300 whether you charge your phone? 00:30:36.300 --> 00:30:38.550 By the way, that's not hard to do. 00:30:38.550 --> 00:30:40.210 It's easy. 00:30:40.210 --> 00:30:43.820 These phones are really quite sophisticated-- 00:30:43.820 --> 00:30:47.620 geolocation, tracking, contact tracing, everything. 00:30:47.620 --> 00:30:50.220 So let's talk about the readings and everything. 00:30:50.220 --> 00:30:51.570 I'm not going to dive into it. 00:30:51.570 --> 00:30:53.835 But there were three readings about challenger banks, 00:30:53.835 --> 00:30:59.130 neobanks, and some of the challenges going forward 00:30:59.130 --> 00:30:59.910 in this field. 00:31:02.980 --> 00:31:05.860 What is a challenger bank or neobank? 00:31:05.860 --> 00:31:06.807 And since we're-- 00:31:06.807 --> 00:31:08.140 I don't want to run out of time. 00:31:08.140 --> 00:31:12.630 Let me just sort of say challenger and neobank are 00:31:12.630 --> 00:31:17.320 often used interchangeably, but they're actually-- 00:31:17.320 --> 00:31:21.106 to the more sort of specific, they're a little bit different. 00:31:21.106 --> 00:31:24.650 A neobank is something that is acting like a bank 00:31:24.650 --> 00:31:28.490 but is not yet registered or licensed 00:31:28.490 --> 00:31:30.620 as a bank in its jurisdiction. 00:31:30.620 --> 00:31:33.890 And a challenger bank more technically 00:31:33.890 --> 00:31:35.990 would be something that has a license. 00:31:35.990 --> 00:31:39.860 Now, most people use these terms interchangeably. 00:31:39.860 --> 00:31:42.380 But there's slight differences. 00:31:42.380 --> 00:31:43.910 Do they have yet registered? 00:31:43.910 --> 00:31:45.890 And challenger banks and neobanks 00:31:45.890 --> 00:31:48.560 are used to talk about this thing that 00:31:48.560 --> 00:31:52.550 happened in these last seven to ten years. 00:31:52.550 --> 00:31:55.130 I'm going in a little bit talk about how 00:31:55.130 --> 00:32:00.050 this relates to the online banking that came before it. 00:32:00.050 --> 00:32:02.130 The pain points in traditional banking-- 00:32:02.130 --> 00:32:04.950 we're going to dive into this in a minute in a few slides. 00:32:04.950 --> 00:32:06.360 So I'll hold off. 00:32:06.360 --> 00:32:10.850 But those pain points about the costs that the big banks have, 00:32:10.850 --> 00:32:13.760 the bricks and mortar legacy business that they have, 00:32:13.760 --> 00:32:17.030 the legacy technology are real and they've 00:32:17.030 --> 00:32:20.630 provided some opportunity to challenger banks. 00:32:20.630 --> 00:32:23.930 And the question is how will challenger banks and neobanks 00:32:23.930 --> 00:32:25.470 affect the landscape? 00:32:25.470 --> 00:32:28.740 So one is what is this phenomena? 00:32:28.740 --> 00:32:31.850 Two, why have they been able to tap into this? 00:32:31.850 --> 00:32:34.190 What's the traditional bank's problems? 00:32:34.190 --> 00:32:36.020 And three, what's it mean for the future? 00:32:36.020 --> 00:32:39.750 That's what we're going to try to cover going forward. 00:32:39.750 --> 00:32:41.390 So the challenges-- 00:32:41.390 --> 00:32:45.080 I mentioned a couple of them, but look, we all know it. 00:32:45.080 --> 00:32:49.790 In the US, there's about 78,000 branches still in the US. 00:32:49.790 --> 00:32:52.520 That's down from about 82,000 or 83,000. 00:32:52.520 --> 00:32:55.520 What will that number be in 10 years? 00:32:55.520 --> 00:32:57.620 It's not declining leaps and bounds. 00:32:57.620 --> 00:33:00.380 But particularly after the coronavirus crisis, 00:33:00.380 --> 00:33:03.120 whenever we get to the other side of this, 00:33:03.120 --> 00:33:09.530 whether it optimistically is in months or more, realistically, 00:33:09.530 --> 00:33:13.100 in a couple of years and we-- all this social distancing 00:33:13.100 --> 00:33:15.860 starts to move around and we start 00:33:15.860 --> 00:33:21.440 to get out there, this 78,000 branches, do we need them all? 00:33:21.440 --> 00:33:24.380 And yet surveys of the public, still people 00:33:24.380 --> 00:33:28.250 like to have a physical branch for that occasional time you 00:33:28.250 --> 00:33:29.920 go into a physical branch. 00:33:29.920 --> 00:33:32.400 They have legacy technology. 00:33:32.400 --> 00:33:35.480 The products themselves are somewhat commoditized. 00:33:35.480 --> 00:33:38.750 A mortgages a mortgage, by and large, an auto loan, 00:33:38.750 --> 00:33:40.250 and even a small business loan. 00:33:40.250 --> 00:33:42.260 There's a certain commoditization 00:33:42.260 --> 00:33:45.200 of payments and credit. 00:33:45.200 --> 00:33:49.880 And how, when you still have 4,000 to 5,000 banks in the US 00:33:49.880 --> 00:33:52.070 and in many countries still hundreds of banks, 00:33:52.070 --> 00:33:54.770 how do they literally compete with each other 00:33:54.770 --> 00:33:58.970 on a product that is somewhat commoditized? 00:33:58.970 --> 00:34:01.220 Now, you might say that's hard for the new competitor. 00:34:01.220 --> 00:34:03.137 But the new competitor then comes in and says, 00:34:03.137 --> 00:34:06.410 well, maybe I can only provide something to the restaurant 00:34:06.410 --> 00:34:09.170 business, or I can provide something that is really 00:34:09.170 --> 00:34:11.360 a great user interface. 00:34:11.360 --> 00:34:15.290 And the cost and fees are real, too. 00:34:15.290 --> 00:34:17.600 The traditional banking, not just 00:34:17.600 --> 00:34:20.929 because of their branch network of nearly 80,000 branches 00:34:20.929 --> 00:34:24.800 in the US, but also because of their employment, 00:34:24.800 --> 00:34:27.080 also because of their fees-- 00:34:27.080 --> 00:34:30.120 a lot of the challenger banks broke in by saying, listen, 00:34:30.120 --> 00:34:32.780 we're not going to charge you the same overdraft fees 00:34:32.780 --> 00:34:34.250 or late fees. 00:34:34.250 --> 00:34:38.989 And if you do a deep dive and a deep study on one consumer 00:34:38.989 --> 00:34:42.830 banking, a lot of the revenue is on fees, 00:34:42.830 --> 00:34:46.770 whether it's overdraft, late fees and the like. 00:34:46.770 --> 00:34:50.659 And so challenger banks also said we can go in there 00:34:50.659 --> 00:34:54.540 and try to challenge online in a different way and, of course, 00:34:54.540 --> 00:34:56.909 provide greater user interface. 00:34:56.909 --> 00:34:58.910 I think, to Luke's earlier question, 00:34:58.910 --> 00:35:02.540 partly why challenger banks started in Europe 00:35:02.540 --> 00:35:05.470 was partly because of the Payment System directive. 00:35:05.470 --> 00:35:09.530 There was first PDS, Payment System Directive, 00:35:09.530 --> 00:35:12.890 what's now called 1 and PSD 2. 00:35:12.890 --> 00:35:14.900 I think it's partly because of the Payment 00:35:14.900 --> 00:35:19.370 System directives, but partly the 2008 crisis. 00:35:19.370 --> 00:35:23.600 Now, Ally Bank here in the US technically 00:35:23.600 --> 00:35:26.030 was the first one in 2009 here. 00:35:26.030 --> 00:35:29.300 But a lot of people don't say that was the first one. 00:35:29.300 --> 00:35:31.290 But that came after the crisis. 00:35:31.290 --> 00:35:33.080 And then you saw this spur of them 00:35:33.080 --> 00:35:36.770 in the early teens that came along. 00:35:36.770 --> 00:35:39.290 And I think a lot of it had to do with trust. 00:35:39.290 --> 00:35:41.480 And the European situation it was going through, 00:35:41.480 --> 00:35:46.670 the European debt crisis, all the way through 2012, 2013. 00:35:46.670 --> 00:35:49.850 And you saw an enormous number of these starting 00:35:49.850 --> 00:35:53.720 between 2011 and 2015. 00:35:53.720 --> 00:35:57.730 And so this is a little bit the history of online banking. 00:35:57.730 --> 00:36:01.640 And I take this from FT Partners, which 00:36:01.640 --> 00:36:03.500 is Financial Technology Partners, which 00:36:03.500 --> 00:36:09.140 is a small investment bank started by a former Wall Street 00:36:09.140 --> 00:36:11.240 banker, I think a handful from Goldman 00:36:11.240 --> 00:36:13.610 and maybe Morgan Stanley and the others. 00:36:13.610 --> 00:36:15.920 But we're not going to go through each of these. 00:36:15.920 --> 00:36:17.930 But the Bank of Scotland actually 00:36:17.930 --> 00:36:21.710 started to do online banking back in 1983. 00:36:21.710 --> 00:36:27.350 In essence, anything you think is new has something way back. 00:36:27.350 --> 00:36:30.630 Bank of America was already online by 2001. 00:36:30.630 --> 00:36:34.010 Yodlee, which is-- we talked about Yodlee when we talked 00:36:34.010 --> 00:36:35.750 about APIs-- 00:36:35.750 --> 00:36:38.540 had already been in this business. 00:36:38.540 --> 00:36:41.570 And ING, which was part of a big insurance company, 00:36:41.570 --> 00:36:43.700 started retail banking in the late '90s. 00:36:43.700 --> 00:36:45.110 The internet came along. 00:36:45.110 --> 00:36:47.450 An earlier wave of fintech developed 00:36:47.450 --> 00:36:50.060 and online banking came along. 00:36:50.060 --> 00:36:54.620 Most of it, most of it related to traditional banks. 00:36:54.620 --> 00:36:59.840 And the distinction that I draw between online banking 00:36:59.840 --> 00:37:01.390 and challenger banking-- 00:37:01.390 --> 00:37:05.660 it's a light distinction, but an important one-- online banking 00:37:05.660 --> 00:37:08.720 generally was related to companies 00:37:08.720 --> 00:37:11.570 that had bricks and mortars, traditional business models, 00:37:11.570 --> 00:37:15.200 generally-- not always, not always. 00:37:15.200 --> 00:37:20.540 And Mint was launched in 2006, later acquired by Intuit. 00:37:20.540 --> 00:37:23.510 That tax software company acquired Mint. 00:37:23.510 --> 00:37:27.320 ING, an insurance company, has something that Capital One 00:37:27.320 --> 00:37:30.320 buys for $9 billion in 2011. 00:37:30.320 --> 00:37:36.490 So the earlier wave of non-banking online banking, 00:37:36.490 --> 00:37:40.310 non-traditional banking, like ING Direct and Mint, 00:37:40.310 --> 00:37:42.290 were acquired. 00:37:42.290 --> 00:37:44.570 They were eaten up, taken up. 00:37:44.570 --> 00:37:48.170 And Ally-- Ally was an auto lending company 00:37:48.170 --> 00:37:50.930 formed 100 years ago by General Motors. 00:37:50.930 --> 00:37:55.370 It was the first captive auto lending company. 00:37:55.370 --> 00:37:58.430 But this set this stage to what if I just 00:37:58.430 --> 00:38:01.640 started something that never had a banking license, 00:38:01.640 --> 00:38:03.770 never had a bricks and mortar? 00:38:03.770 --> 00:38:05.750 It's just a start up. 00:38:05.750 --> 00:38:09.200 I'm just going to go into some of these cracks, 00:38:09.200 --> 00:38:11.810 some of these opportunities the traditional banks 00:38:11.810 --> 00:38:14.900 are giving me-- costs, fees, trust, user interface-- 00:38:14.900 --> 00:38:16.770 and I'm going to start something. 00:38:16.770 --> 00:38:18.800 And so what are the reasons? 00:38:18.800 --> 00:38:20.870 And from a survey, from a different group, 00:38:20.870 --> 00:38:23.670 The Financial Brand, these are just 00:38:23.670 --> 00:38:25.740 some of the reasons people are saying. 00:38:25.740 --> 00:38:28.080 It's easy to set up an account. 00:38:28.080 --> 00:38:30.600 I like the fees and the rates. 00:38:30.600 --> 00:38:33.960 Better experience, quality service, in one 00:38:33.960 --> 00:38:37.270 survey a couple of months ago. 00:38:37.270 --> 00:38:40.000 That's an opportunity as well. 00:38:40.000 --> 00:38:41.520 Here's a list. 00:38:41.520 --> 00:38:43.270 You can make the list so much longer. 00:38:43.270 --> 00:38:45.720 There's hundreds of other companies. 00:38:45.720 --> 00:38:47.370 But what I tried to do in this is 00:38:47.370 --> 00:38:51.360 sort of stick to around the world different banks. 00:38:51.360 --> 00:38:54.540 Ally Bank from 11 years ago, but look 00:38:54.540 --> 00:39:00.420 at the spate of startups in 2013 to 2016 or '16. 00:39:00.420 --> 00:39:04.990 Now, some of these are new, like BigPay in India. 00:39:04.990 --> 00:39:09.300 Some of them are multi-billion valuation companies already, 00:39:09.300 --> 00:39:13.440 like Nubank in Brazil, started about seven years ago. 00:39:13.440 --> 00:39:17.460 There's a bulk of them from the UK, like OakNorth. 00:39:17.460 --> 00:39:21.000 Now, Revolut is in 30 countries now, 00:39:21.000 --> 00:39:26.820 one of the earliest and largest out of Europe. 00:39:26.820 --> 00:39:29.380 Some are coming from other fintech space. 00:39:29.380 --> 00:39:32.800 Like SoFi didn't start as what people would traditionally 00:39:32.800 --> 00:39:34.140 call a challenger bank. 00:39:34.140 --> 00:39:38.110 SoFi kind of started as an online lending platform, 00:39:38.110 --> 00:39:40.120 not quite peer to peer. 00:39:40.120 --> 00:39:42.400 But then SoFi is moving over. 00:39:42.400 --> 00:39:45.130 You can put on this list Betterment 00:39:45.130 --> 00:39:47.350 that we will talk about in the next lecture that 00:39:47.350 --> 00:39:49.480 does wealth management. 00:39:49.480 --> 00:39:52.870 Betterment is starting to move into this space. 00:39:52.870 --> 00:39:57.400 Plaid has started to say they will move into this space. 00:39:57.400 --> 00:39:59.440 And then WeBank is an interesting one 00:39:59.440 --> 00:40:02.080 that's really formed by a bunch of commercial banks 00:40:02.080 --> 00:40:06.580 in China that's saying we're going to just have 00:40:06.580 --> 00:40:08.890 an online platform. 00:40:08.890 --> 00:40:11.440 Is it really part of those commercial banks or is it 00:40:11.440 --> 00:40:14.680 separately a challenger bank. 00:40:14.680 --> 00:40:18.740 So I've got a lot going on, and KakaoBank, 00:40:18.740 --> 00:40:21.320 which is started by one of the big tech firms. 00:40:21.320 --> 00:40:24.080 I chose not to put Ant Financial on here. 00:40:24.080 --> 00:40:27.720 But you could arguably say Ant Financial, part of Alipay. 00:40:27.720 --> 00:40:30.740 I was trying to stick to things that are really classically 00:40:30.740 --> 00:40:32.960 called challenger neobanks. 00:40:32.960 --> 00:40:36.280 So the vocabulary is a little loose. 00:40:36.280 --> 00:40:38.950 Most people, when they think of challenger neobanks, 00:40:38.950 --> 00:40:40.930 they think of Revolut in Europe. 00:40:40.930 --> 00:40:43.090 If you want to study a case closely, 00:40:43.090 --> 00:40:47.390 look at Revolut, now in 30 countries. 00:40:47.390 --> 00:40:51.200 But a lot of other players who try to get in-- 00:40:51.200 --> 00:40:53.320 TransferWise from the payment space. 00:40:53.320 --> 00:40:56.360 So some from the payment space some, from the big tech, 00:40:56.360 --> 00:41:00.290 like KakaoBank, some from commercial banks, 00:41:00.290 --> 00:41:03.200 some from investment banks, like Goldman Sachs starts Marcus. 00:41:06.380 --> 00:41:08.240 A lot going on. 00:41:08.240 --> 00:41:09.620 Nearly every country-- 00:41:09.620 --> 00:41:13.490 Australia is represented here, China, Southeast Asia 00:41:13.490 --> 00:41:16.160 and the like. 00:41:16.160 --> 00:41:18.320 But this sort of gives you a map of it, 00:41:18.320 --> 00:41:20.870 back to that FT Partners thing. 00:41:20.870 --> 00:41:24.830 This blows it up even more, just with their logos. 00:41:24.830 --> 00:41:28.610 But you can see it's really around the globe right now. 00:41:28.610 --> 00:41:32.943 Kind of was emanated out of Europe, populated out of the US 00:41:32.943 --> 00:41:34.610 then, and then all of a sudden, you just 00:41:34.610 --> 00:41:37.370 saw it kind of all around the globe. 00:41:37.370 --> 00:41:41.120 Brazil, enormous contributions out of Brazil in the business 00:41:41.120 --> 00:41:45.560 models as well and the like. 00:41:45.560 --> 00:41:53.440 Paytm out of India as well, in a sense, I should have mentioned. 00:41:53.440 --> 00:41:55.240 Quite a sort of interesting-- and this 00:41:55.240 --> 00:41:57.130 will be up in Canvas, of course. 00:41:57.130 --> 00:41:58.935 So what are their product offerings? 00:41:58.935 --> 00:42:00.310 What are their product offerings? 00:42:00.310 --> 00:42:03.670 Well, any bank has the same sort of list of product offerings, 00:42:03.670 --> 00:42:04.570 of course. 00:42:04.570 --> 00:42:08.200 And this is just one example, again, from this FT Partners. 00:42:08.200 --> 00:42:10.660 Checking and savings-- to be a challenger bank, 00:42:10.660 --> 00:42:13.210 it's usually right there, somewhere in debit 00:42:13.210 --> 00:42:16.070 cards, credit cards, checking, savings. 00:42:16.070 --> 00:42:19.990 And basically, they would say it was easy to an account. 00:42:19.990 --> 00:42:21.970 And we will actually pay you something. 00:42:21.970 --> 00:42:25.270 Revolut, interestingly, started in the cross-border payment 00:42:25.270 --> 00:42:28.725 area and basically said you could have an account-- 00:42:28.725 --> 00:42:30.100 you can be in one country and you 00:42:30.100 --> 00:42:33.660 could have an account in multiple currencies. 00:42:33.660 --> 00:42:38.250 Traditional banking is a single-currency product, partly 00:42:38.250 --> 00:42:41.080 regulation, partly history. 00:42:41.080 --> 00:42:42.630 But Revolut says we're online. 00:42:42.630 --> 00:42:44.670 You can have multiple currencies. 00:42:44.670 --> 00:42:49.270 And we won't charge you all of that cross-border cost. 00:42:49.270 --> 00:42:51.250 One might say it started as a payment company. 00:42:51.250 --> 00:42:55.790 But it was really an offering you accounts as well. 00:42:55.790 --> 00:42:57.580 And this idea of offering an account-- 00:42:57.580 --> 00:43:00.160 remember, the term neobank and challenger bank, 00:43:00.160 --> 00:43:02.770 often used interchangeably, but neobank is something 00:43:02.770 --> 00:43:05.050 that's not yet licensed. 00:43:05.050 --> 00:43:08.290 You've seen a spate of companies in the United Kingdom getting 00:43:08.290 --> 00:43:10.660 licensed from 2016 to 2019. 00:43:10.660 --> 00:43:13.900 Many that were not licensed as traditional banks 00:43:13.900 --> 00:43:16.330 then filed for their banking licenses. 00:43:16.330 --> 00:43:19.870 And you've seen that happening along the way. 00:43:19.870 --> 00:43:23.350 When will SoFi, for instance, get their banking license 00:43:23.350 --> 00:43:24.880 in the US, so to speak. 00:43:24.880 --> 00:43:29.100 You'll see that sort of going along the way as well. 00:43:32.450 --> 00:43:35.230 Romain I'm just going to pause for questions just to see-- 00:43:35.230 --> 00:43:37.160 get a little engaged here. 00:43:37.160 --> 00:43:40.250 And then I'm going to go to their account base. 00:43:40.250 --> 00:43:42.920 And we're going to talk about their account base. 00:43:42.920 --> 00:43:46.220 And then we'll turn to funding, valuations, and a little bit 00:43:46.220 --> 00:43:48.945 how traditional banks are reacting. 00:43:48.945 --> 00:43:49.810 TA: Excellent. 00:43:49.810 --> 00:43:51.227 Alessandro, do you want to go? 00:43:51.227 --> 00:43:52.310 AUDIENCE: Yeah, thank you. 00:43:52.310 --> 00:43:53.360 Hi, Gary. 00:43:53.360 --> 00:43:56.000 So my question is many of these companies 00:43:56.000 --> 00:44:00.410 that offer credit cards, they often partner up with banks. 00:44:00.410 --> 00:44:03.740 Is this because of regulatory issues or a balance 00:44:03.740 --> 00:44:05.110 sheet issues? 00:44:05.110 --> 00:44:06.610 GARY GENSLER: It's a great question. 00:44:06.610 --> 00:44:07.670 It's really both. 00:44:07.670 --> 00:44:10.700 And it's not just-- 00:44:10.700 --> 00:44:12.580 it's not just in the credit card business. 00:44:12.580 --> 00:44:16.340 You found that marketplace lenders also 00:44:16.340 --> 00:44:19.040 partnered up with banks. 00:44:19.040 --> 00:44:24.650 The idea of entering part of finance-- 00:44:24.650 --> 00:44:27.650 you always have a decision in any business of buy 00:44:27.650 --> 00:44:31.190 versus build or rent versus bill. 00:44:31.190 --> 00:44:32.960 You can do this with your cloud storage. 00:44:32.960 --> 00:44:35.990 Do I want to build my data warehouse or rent it? 00:44:35.990 --> 00:44:38.210 Well, by and large, if you're a fintech startup, 00:44:38.210 --> 00:44:40.460 you're going to rent your cloud storage now, right? 00:44:40.460 --> 00:44:42.650 You're not going to build your data warehouse. 00:44:42.650 --> 00:44:47.360 You might similarly rent somebody else's balance sheet. 00:44:47.360 --> 00:44:51.200 And so part of what you've seen is about balance sheet. 00:44:51.200 --> 00:44:55.110 You start a business and you try to find some funding, 00:44:55.110 --> 00:44:56.900 but you don't have the funding yourself. 00:44:56.900 --> 00:44:58.620 You don't have a balance sheet. 00:44:58.620 --> 00:45:01.460 And you can get a warehouse line of credit. 00:45:01.460 --> 00:45:04.160 And you can use the asset securitization market 00:45:04.160 --> 00:45:08.540 as well to offload those credit card receivables, those auto 00:45:08.540 --> 00:45:10.490 receivables, student loan, and, of course, 00:45:10.490 --> 00:45:12.380 mortgage receivables. 00:45:12.380 --> 00:45:14.522 And-- excuse me-- 00:45:14.522 --> 00:45:19.390 [SNEEZES] and so you see [SNEEZES] in some lines 00:45:19.390 --> 00:45:21.550 of business, particularly mortgages, 00:45:21.550 --> 00:45:24.640 that what one might call alternative finance-- 00:45:24.640 --> 00:45:27.250 Quicken Loans, PennyMac, and others 00:45:27.250 --> 00:45:29.800 have built very significant-- well 00:45:29.800 --> 00:45:34.660 over 50% of mortgages in the US now, 50% to 60%, 00:45:34.660 --> 00:45:39.970 originated by the non-banks because they can readily 00:45:39.970 --> 00:45:42.490 offload the balance sheet issues. 00:45:42.490 --> 00:45:45.760 But to your question, a lot of it is also regulatory. 00:45:45.760 --> 00:45:50.080 And in the credit card space and in the peer-to-peer lending 00:45:50.080 --> 00:45:53.350 space, without a bank license, it's 00:45:53.350 --> 00:45:56.920 hard to do all the pieces you need to do. 00:45:56.920 --> 00:46:00.340 And without kind of diving into each of the regulatory reasons 00:46:00.340 --> 00:46:01.990 why, you tend to-- 00:46:01.990 --> 00:46:05.223 so Amazon partnered with Synchrony, for instance. 00:46:05.223 --> 00:46:06.640 And subsequent, it looks like they 00:46:06.640 --> 00:46:11.890 may have also partnered with JPMorgan Chase on credit card 00:46:11.890 --> 00:46:12.670 offerings. 00:46:12.670 --> 00:46:14.830 And there are banks, like Synchrony and others, 00:46:14.830 --> 00:46:18.430 they will say we'll basically white label. 00:46:18.430 --> 00:46:20.460 Some of them, like Synchrony, that's a co-brand, 00:46:20.460 --> 00:46:21.940 on Amazon-Synchrony. 00:46:21.940 --> 00:46:24.960 Some will say we'll just be in the background. 00:46:24.960 --> 00:46:27.640 And there are actually some licensed banks 00:46:27.640 --> 00:46:31.300 that make a business out of servicing fintech companies. 00:46:31.300 --> 00:46:35.200 And there's a whole group of licensed banks 00:46:35.200 --> 00:46:38.860 that in both Europe and the US feel 00:46:38.860 --> 00:46:42.800 that's a line of business in and of itself. 00:46:42.800 --> 00:46:43.815 AUDIENCE: Thank you. 00:46:43.815 --> 00:46:45.405 TA: Martin. 00:46:45.405 --> 00:46:47.580 AUDIENCE: Yes, with so many banks 00:46:47.580 --> 00:46:50.600 and with so many similar credit offerings, 00:46:50.600 --> 00:46:54.130 how do you see the differentiation will happen? 00:46:54.130 --> 00:46:54.680 And there-- 00:46:54.680 --> 00:46:55.888 GARY GENSLER: Great question. 00:46:55.888 --> 00:46:58.470 Look, this is a very exciting space, 00:46:58.470 --> 00:47:00.150 but it's going to shake out. 00:47:00.150 --> 00:47:07.860 I mean, it's hard to imagine that they'll all be there. 00:47:07.860 --> 00:47:09.470 There is one website-- 00:47:09.470 --> 00:47:12.890 I didn't choose to put it up here-- that actually follows 00:47:12.890 --> 00:47:17.690 the top 10 challenger banks, by every six months, 00:47:17.690 --> 00:47:21.880 they post who is getting the most web traffic? 00:47:21.880 --> 00:47:26.320 And part of it-- if you're SoFi, and in one year 00:47:26.320 --> 00:47:29.680 you get the most web traffic, but three years later, people 00:47:29.680 --> 00:47:32.740 aren't going on your website, that's a problem. 00:47:32.740 --> 00:47:34.780 I'm not saying SoFi yet has that problem. 00:47:37.330 --> 00:47:40.930 So the way that these companies-- they've each 00:47:40.930 --> 00:47:42.230 started a little different. 00:47:42.230 --> 00:47:44.380 Some start in a slice of a domain, 00:47:44.380 --> 00:47:46.570 like Revolt is the cross-border. 00:47:46.570 --> 00:47:50.290 Some come at it from the open API side. 00:47:50.290 --> 00:47:52.810 Some come from the low-cost side. 00:47:52.810 --> 00:47:57.970 They find one pain point and they try to do that well. 00:47:57.970 --> 00:48:01.540 All of them that are successful now pretty much started 00:48:01.540 --> 00:48:03.402 between 2013 and 2018. 00:48:03.402 --> 00:48:05.110 So it's a little harder to think somebody 00:48:05.110 --> 00:48:06.490 is going to get in now. 00:48:06.490 --> 00:48:09.100 But it might be a geography. 00:48:09.100 --> 00:48:14.120 It might be like BigPay, which I think is in Southeast Asia. 00:48:14.120 --> 00:48:17.630 It might be a geography as well. 00:48:17.630 --> 00:48:19.270 But I do think there's going to be-- 00:48:19.270 --> 00:48:20.710 there's going to be shakeout. 00:48:20.710 --> 00:48:24.010 And even amongst the unicorns, those 00:48:24.010 --> 00:48:27.610 that are valued over $1 billion, customers matter. 00:48:27.610 --> 00:48:29.170 And maybe this sets up. 00:48:29.170 --> 00:48:31.750 I'm going to show you two competing 00:48:31.750 --> 00:48:34.280 charts on challenger banks. 00:48:34.280 --> 00:48:35.320 This is just Europe. 00:48:35.320 --> 00:48:36.910 This is just Europe. 00:48:36.910 --> 00:48:39.700 But you see the growth in customers-- 00:48:39.700 --> 00:48:44.170 Revolt, N26, Monese and so forth, 00:48:44.170 --> 00:48:47.410 just this remarkable growth of customers. 00:48:47.410 --> 00:48:51.010 That's getting noted by traditional banks. 00:48:51.010 --> 00:48:56.230 You get 5, 10 million customer accounts, that's 00:48:56.230 --> 00:48:58.640 a remarkable thing to happen. 00:48:58.640 --> 00:49:00.760 We'll talk more about this when we do asset 00:49:00.760 --> 00:49:02.980 management in the next lecture. 00:49:02.980 --> 00:49:08.050 But most of the biggest online brokers, to give you scaling, 00:49:08.050 --> 00:49:11.890 were somewhere between 6 and 12 million customer accounts, 00:49:11.890 --> 00:49:19.330 E-Trade and the like, TD Ameritrade, so forth. 00:49:19.330 --> 00:49:22.830 So to give this on a scale of customer accounts, 00:49:22.830 --> 00:49:25.290 these started to really get noticed. 00:49:25.290 --> 00:49:27.420 And another customer account chart-- 00:49:27.420 --> 00:49:31.200 different service, it's hard to know whether these numbers are 00:49:31.200 --> 00:49:34.440 accurate, but these are self-reported by the companies. 00:49:34.440 --> 00:49:37.980 This is by year of crop, the 2013 startups-- 00:49:37.980 --> 00:49:42.420 Nubank, Aspiration, Tandem, Chime. 00:49:42.420 --> 00:49:45.580 Well, Nubank getting to 15 million customer accounts 00:49:45.580 --> 00:49:48.960 or KakaoBank in Korea getting to 10 million customers, 00:49:48.960 --> 00:49:54.240 Chime, 6 million, 5 million for MoneyLion, Revolut, 6, 00:49:54.240 --> 00:49:56.400 and some other figures show Revolut might 00:49:56.400 --> 00:49:59.990 be closer to 9 million now-- 00:49:59.990 --> 00:50:03.980 these are significant in and of themselves, 00:50:03.980 --> 00:50:05.100 that they can get to that. 00:50:05.100 --> 00:50:07.930 So part of a business model-- 00:50:07.930 --> 00:50:09.980 you can see hundreds of challenger banks. 00:50:09.980 --> 00:50:15.720 But many of them still only have 100,000 to 400,000 accounts. 00:50:15.720 --> 00:50:17.490 And that's a great start, but you've 00:50:17.490 --> 00:50:20.560 got to kind of get further. 00:50:20.560 --> 00:50:22.440 And the venture capitalists behind it 00:50:22.440 --> 00:50:24.900 have to decide what's their exit strategy? 00:50:24.900 --> 00:50:26.820 Can you sell this to somebody else who's 00:50:26.820 --> 00:50:29.890 trying to get a foothold in this space or not? 00:50:29.890 --> 00:50:32.640 Stash, by the way, that's listed here 00:50:32.640 --> 00:50:36.660 is more in the online brokerage business than it 00:50:36.660 --> 00:50:38.050 on an online banking. 00:50:38.050 --> 00:50:41.170 So I chose not to list it earlier. 00:50:41.170 --> 00:50:44.730 Tandem, only half a million customers, 00:50:44.730 --> 00:50:47.010 is a really big one in the UK. 00:50:47.010 --> 00:50:51.540 But Starling Bank and Revolut and Monzo are bigger. 00:50:51.540 --> 00:50:53.940 So how does Tandem break through when 00:50:53.940 --> 00:50:58.050 you've got four or five that are bigger, just as an example, 00:50:58.050 --> 00:51:00.320 in the UK? 00:51:00.320 --> 00:51:03.300 And so then valuations-- 00:51:03.300 --> 00:51:05.980 and it's really hard to get behind valuations. 00:51:05.980 --> 00:51:10.237 All of this is before the corona crisis. 00:51:10.237 --> 00:51:11.820 Some of these are going to be winners. 00:51:11.820 --> 00:51:16.050 Some of them are going to be losers, this one taken 00:51:16.050 --> 00:51:19.620 from a Forbes article in late 2019 based 00:51:19.620 --> 00:51:24.140 on PitchBook Data from their last funding round. 00:51:24.140 --> 00:51:26.600 Now, Nubank-- look, let's go back. 00:51:26.600 --> 00:51:30.680 Nubank, 15 or so million customers-- 00:51:30.680 --> 00:51:34.010 Nubank's been the big success story here. 00:51:34.010 --> 00:51:37.460 Revolut, if that's a true number from their last funding round, 00:51:37.460 --> 00:51:43.750 that seems a little out of what the customer numbers are. 00:51:43.750 --> 00:51:46.700 It might be an out-of-date figure. 00:51:46.700 --> 00:51:47.930 Valuations aren't clear. 00:51:47.930 --> 00:51:49.860 This is not stock market valuation. 00:51:49.860 --> 00:51:52.760 So I'm going to give you another take on this as well, 00:51:52.760 --> 00:51:54.980 is this is the actual funding-- 00:51:54.980 --> 00:51:57.530 back to the FT Partners survey-- 00:51:57.530 --> 00:52:01.070 this is actual funding that these companies have done 00:52:01.070 --> 00:52:05.200 and then what they call pre-money valuations of Nubank 00:52:05.200 --> 00:52:07.350 and Chime and some of the others. 00:52:07.350 --> 00:52:11.690 So these are not insignificant players in the market. 00:52:11.690 --> 00:52:14.480 If you're thinking about going to work for one of these, 00:52:14.480 --> 00:52:17.090 it would be an exciting career path. 00:52:17.090 --> 00:52:21.200 But even the Nubanks and Chimes and N26s 00:52:21.200 --> 00:52:25.490 have to figure out how they stay viable, how they stay vibrant 00:52:25.490 --> 00:52:32.338 against the traditional players and the big tech companies. 00:52:32.338 --> 00:52:34.130 And if you're all the way at the other end, 00:52:34.130 --> 00:52:35.780 if you're a company like Dave-- 00:52:35.780 --> 00:52:39.140 Dave is thought of as just maybe a unicorn 00:52:39.140 --> 00:52:41.750 and it's only raised 76 million. 00:52:41.750 --> 00:52:44.600 Now, that number may or may not-- 00:52:44.600 --> 00:52:48.920 but if you're Dave and you're there with a couple employees, 00:52:48.920 --> 00:52:51.020 will it be there in three years or not? 00:52:51.020 --> 00:52:52.528 What's their exit strategy? 00:52:52.528 --> 00:52:54.320 So I think you've raised the right question 00:52:54.320 --> 00:52:56.970 about all of these. 00:52:56.970 --> 00:53:02.690 And the history of banking is we don't-- we do have 5,000 6,000 00:53:02.690 --> 00:53:03.680 or so in the US. 00:53:03.680 --> 00:53:05.510 But they're community banks. 00:53:05.510 --> 00:53:09.660 They locally serve and really work hard in their communities. 00:53:09.660 --> 00:53:11.240 They know their business community. 00:53:11.240 --> 00:53:11.900 They know that. 00:53:11.900 --> 00:53:13.820 And they have some bricks and mortars. 00:53:13.820 --> 00:53:18.130 In an online space, we know that network effects matter. 00:53:18.130 --> 00:53:21.700 And with network effects online, how many of these will exist 00:53:21.700 --> 00:53:23.920 is a great question. 00:53:23.920 --> 00:53:27.350 Romain one more slide on funding, 00:53:27.350 --> 00:53:30.980 but questions is I thought this was kind of interesting. 00:53:30.980 --> 00:53:32.760 I don't necessarily subscribe to it. 00:53:32.760 --> 00:53:35.810 But they-- this one site sort of said let's take 00:53:35.810 --> 00:53:38.240 a look at the funding numbers. 00:53:38.240 --> 00:53:43.010 What are their funding numbers and how many customers 00:53:43.010 --> 00:53:44.070 they have? 00:53:44.070 --> 00:53:48.600 So if you take what their last valuation number is divided 00:53:48.600 --> 00:53:51.510 by their customers, and they come up with this concept, 00:53:51.510 --> 00:53:55.510 that customers are worth about $730 apiece, 00:53:55.510 --> 00:53:59.190 if you just average everything, you divide their valuation 00:53:59.190 --> 00:54:02.680 by their customer count. 00:54:02.680 --> 00:54:04.600 It doesn't work perfectly. 00:54:04.600 --> 00:54:05.600 It absolutely doesn't. 00:54:05.600 --> 00:54:10.890 But you can get a sense of this is about an account aggregation 00:54:10.890 --> 00:54:12.060 model. 00:54:12.060 --> 00:54:14.770 Can you build accounts, get to a million? 00:54:14.770 --> 00:54:18.360 Can you get to 5 or 10 million and actually service 00:54:18.360 --> 00:54:18.900 and provide? 00:54:18.900 --> 00:54:21.210 So what's happening is a lot of these companies 00:54:21.210 --> 00:54:23.340 are negative funding. 00:54:23.340 --> 00:54:25.980 By negative funding, what they're really doing 00:54:25.980 --> 00:54:31.220 is they're offering products to the public, 00:54:31.220 --> 00:54:34.100 but they're not covering all of their costs. 00:54:34.100 --> 00:54:38.420 They might be offering interest rates on the deposits. 00:54:38.420 --> 00:54:45.620 They might be having lower costs and fees, lower late fees 00:54:45.620 --> 00:54:47.660 and account maintenance fees. 00:54:47.660 --> 00:54:49.370 They might not have account maintenance. 00:54:49.370 --> 00:54:52.640 A lot of these will allow you to have in essence micro payments 00:54:52.640 --> 00:54:54.440 and micro accounts. 00:54:54.440 --> 00:54:57.140 But how they're funding that is the venture capitalists 00:54:57.140 --> 00:55:00.020 are funding it, basically, because what they're trying 00:55:00.020 --> 00:55:02.630 to do is build valuation. 00:55:02.630 --> 00:55:06.830 Their whole model is about building value and then 00:55:06.830 --> 00:55:12.470 exiting somehow and judging when to do that exit. 00:55:12.470 --> 00:55:16.970 Now, some of them will get to be income positive. 00:55:16.970 --> 00:55:21.560 But those that right now aren't in the corona environment, 00:55:21.560 --> 00:55:25.670 coronavirus crisis we're in, there's a new term 00:55:25.670 --> 00:55:29.540 that you'll hear amongst venture capitalists, is runway. 00:55:29.540 --> 00:55:32.960 How much runway does a company have? 00:55:32.960 --> 00:55:37.070 Did its last funding round, did they raise enough money 00:55:37.070 --> 00:55:42.710 to sustain them for six months more or two years more? 00:55:42.710 --> 00:55:45.650 And a lot relates to one was their last funding round? 00:55:45.650 --> 00:55:48.080 If their last funding round was in the last six months 00:55:48.080 --> 00:55:51.345 and they have enough runway, enough cash 00:55:51.345 --> 00:55:56.210 so that their cash burn, they can last two or three years, 00:55:56.210 --> 00:56:00.412 that's more optimistic than if their runway is six months 00:56:00.412 --> 00:56:02.120 and they haven't done their last funding. 00:56:02.120 --> 00:56:04.560 It's been a year ago. 00:56:04.560 --> 00:56:07.060 They did their A round, but they haven't done their B round. 00:56:07.060 --> 00:56:10.240 And they've done their C round, haven't done their D round. 00:56:10.240 --> 00:56:12.370 And, in essence, they have to-- a lot 00:56:12.370 --> 00:56:14.830 of these companies in the middle of the coronavirus crisis 00:56:14.830 --> 00:56:20.710 have to figure out how to extend their runway, 00:56:20.710 --> 00:56:23.260 maybe do a suboptimal new funding 00:56:23.260 --> 00:56:27.520 round that doesn't boost their valuation, 00:56:27.520 --> 00:56:30.920 merge or consolidate. 00:56:30.920 --> 00:56:34.340 So it's also a challenging time, depending upon what 00:56:34.340 --> 00:56:35.990 they hadn't anticipated-- 00:56:35.990 --> 00:56:37.670 quote, how much runway they have, 00:56:37.670 --> 00:56:40.350 when was their last funding round and the like, 00:56:40.350 --> 00:56:44.340 and have they gone cash-flow positive? 00:56:44.340 --> 00:56:46.220 Most of these have not necessarily 00:56:46.220 --> 00:56:48.635 gone cash-flow positive. 00:56:48.635 --> 00:56:50.510 Romain I'll pause for questions, and then I'm 00:56:50.510 --> 00:56:53.287 going to talk about traditional banks. 00:56:53.287 --> 00:56:54.620 TA: So you almost just answered. 00:56:54.620 --> 00:56:56.450 We had a question from Alida in the chat 00:56:56.450 --> 00:57:00.238 asking whether this model is profitable. 00:57:00.238 --> 00:57:03.850 GARY GENSLER: Alida it is profitable in terms of selling 00:57:03.850 --> 00:57:06.670 equity to venture capitalists. 00:57:06.670 --> 00:57:09.100 It is profitable for some of the banks that 00:57:09.100 --> 00:57:14.030 have been sold even earlier times to traditional banks. 00:57:14.030 --> 00:57:19.800 And for some on bottom line has crossed over and is profitable. 00:57:19.800 --> 00:57:23.490 But we don't have financials for the hundreds of challenger 00:57:23.490 --> 00:57:24.660 and neobanks. 00:57:24.660 --> 00:57:29.790 But by and large, most, before the coronavirus crisis, 00:57:29.790 --> 00:57:33.680 would have been in a negative earnings space. 00:57:33.680 --> 00:57:36.180 And what they were trying to do is basically build 00:57:36.180 --> 00:57:39.870 digital footprint, build customer base 00:57:39.870 --> 00:57:41.610 to get to that first half million, 00:57:41.610 --> 00:57:43.110 to get to that first million, to get 00:57:43.110 --> 00:57:45.410 to the 5 and 10 million round. 00:57:45.410 --> 00:57:49.290 You get 5 or 10 million customers, what you've built 00:57:49.290 --> 00:57:52.050 is you've built a valuable thing that somebody else might 00:57:52.050 --> 00:57:53.700 want to buy. 00:57:53.700 --> 00:57:56.310 And you've tended-- you've tended to then to get 00:57:56.310 --> 00:57:57.840 to cash-flow positive by then. 00:57:57.840 --> 00:57:59.830 But I don't have the financials. 00:57:59.830 --> 00:58:01.770 I wouldn't be able to tell you which 00:58:01.770 --> 00:58:07.140 ones of these small players are yet cash-flow positive. 00:58:07.140 --> 00:58:08.430 I will say this-- 00:58:08.430 --> 00:58:10.830 three to six months from now, more of them 00:58:10.830 --> 00:58:12.480 will consolidate, go out of business, 00:58:12.480 --> 00:58:15.430 or get cash-flow positive. 00:58:15.430 --> 00:58:20.200 They'll have a need to, as I say, extend that runway. 00:58:20.200 --> 00:58:25.190 And then we have Carlos with his anecdote. 00:58:25.190 --> 00:58:26.820 AUDIENCE: Yeah, I have-- 00:58:26.820 --> 00:58:31.230 I have a question as it pertains to these banks in developed 00:58:31.230 --> 00:58:32.470 versus developing markets. 00:58:32.470 --> 00:58:33.990 And I guess it's for two reasons. 00:58:33.990 --> 00:58:37.080 One is that on one end, the extent 00:58:37.080 --> 00:58:40.138 of the unbanked or underbanked population in-- 00:58:40.138 --> 00:58:42.680 and especially because when you showed the valuations, right, 00:58:42.680 --> 00:58:45.720 Nubank is by far an outlier in a country like Brazil. 00:58:45.720 --> 00:58:48.120 So one is that probably a lot of people 00:58:48.120 --> 00:58:49.830 that are clients of Nubank did not 00:58:49.830 --> 00:58:53.486 previously have bank accounts or were significantly underbanked. 00:58:53.486 --> 00:58:55.820 And in a moment like right now with the corona crisis, 00:58:55.820 --> 00:58:58.320 I mean, they're going to need these accounts more than ever, 00:58:58.320 --> 00:59:01.920 whereas people using neobanks in developed markets, 00:59:01.920 --> 00:59:03.480 like the United States, arguably, 00:59:03.480 --> 00:59:04.980 in a lower-interest-rate environment 00:59:04.980 --> 00:59:07.950 might be less prompted or less interested in actually holding 00:59:07.950 --> 00:59:09.810 onto these accounts, versus what they 00:59:09.810 --> 00:59:11.082 can use in traditional banks. 00:59:11.082 --> 00:59:12.540 GARY GENSLER: I think you're right. 00:59:12.540 --> 00:59:17.450 But there's one other aspect that you imply, 00:59:17.450 --> 00:59:19.380 but you didn't explicitly say. 00:59:19.380 --> 00:59:21.780 It's the cost structure and the competitive structure 00:59:21.780 --> 00:59:23.140 in the countries. 00:59:23.140 --> 00:59:24.120 So taking Brazil-- 00:59:24.120 --> 00:59:28.980 Brazil is a very high-cost banking sector. 00:59:28.980 --> 00:59:32.730 It has a lot of historic reasons about the concentration. 00:59:32.730 --> 00:59:35.100 It has to do with the government involvement. 00:59:35.100 --> 00:59:38.760 But of not just Latin America, but around the globe, 00:59:38.760 --> 00:59:42.060 it has, if not the highest, one of the highest 00:59:42.060 --> 00:59:43.440 net-interest margin. 00:59:43.440 --> 00:59:46.320 This is the margin that the traditional banks, 00:59:46.320 --> 00:59:50.550 like Itaú and others, are able to charge between what they 00:59:50.550 --> 00:59:52.590 lend money at and what they borrow money. 00:59:52.590 --> 00:59:54.720 That's called the net interest margin. 00:59:54.720 --> 00:59:58.830 And if in the US and even in Mexico and Canada, 00:59:58.830 --> 01:00:02.040 we're running, give or take, around 300 basis points, 01:00:02.040 --> 01:00:04.590 meaning what you pay a depositor versus what 01:00:04.590 --> 01:00:06.810 you lend on the other side. 01:00:06.810 --> 01:00:08.940 In Brazil, it's multiples of that. 01:00:08.940 --> 01:00:13.160 Even for high-grade corporate borrowers, it's more than that. 01:00:13.160 --> 01:00:16.440 But for small, medium-sized businesses and the public, 01:00:16.440 --> 01:00:20.130 it's close to 10 times that, meaning it's 01:00:20.130 --> 01:00:25.770 20% to 30% net interest margins for a certain sector 01:00:25.770 --> 01:00:26.950 of the market. 01:00:26.950 --> 01:00:30.720 So I think Nubank and the four or five others in Brazil 01:00:30.720 --> 01:00:37.270 had an opportunity because it's a highly inefficient consumer 01:00:37.270 --> 01:00:40.180 banking and small and medium-sized banking market. 01:00:40.180 --> 01:00:43.780 And recall from our last class, I 01:00:43.780 --> 01:00:46.510 said that fintech companies have largely focused 01:00:46.510 --> 01:00:50.420 on the household sector, whether it's mortgages, personal loans, 01:00:50.420 --> 01:00:53.000 credit cards, and the like. 01:00:53.000 --> 01:00:57.170 They've focused on the consumer sector of savings accounts 01:00:57.170 --> 01:01:00.360 and credit card provisions and the like. 01:01:00.360 --> 01:01:02.760 And they focused on small and medium-sized business. 01:01:02.760 --> 01:01:04.020 They're not trying to compete. 01:01:04.020 --> 01:01:07.110 Nubank is not trying to compete with Itaú about the big 01:01:07.110 --> 01:01:08.460 corporate lending. 01:01:08.460 --> 01:01:10.540 And they're not going to compete in Brazil, 01:01:10.540 --> 01:01:13.230 as about half of the-- half of the lending at the big 01:01:13.230 --> 01:01:17.340 corporate level is from basically government-banked-- 01:01:17.340 --> 01:01:20.320 government-backed banks. 01:01:20.320 --> 01:01:23.160 So I think when you look at the developed world-- 01:01:23.160 --> 01:01:27.060 the Europe, the US, and so forth-- 01:01:27.060 --> 01:01:31.710 we have challenges in our banking system, but they're-- 01:01:31.710 --> 01:01:35.190 it's around those pain points I talked 01:01:35.190 --> 01:01:38.490 about earlier about legacy systems and legacy user 01:01:38.490 --> 01:01:42.190 interfaces and sometimes high fees and so forth. 01:01:42.190 --> 01:01:44.250 But in every country, you have to look 01:01:44.250 --> 01:01:48.060 at what's the inefficiency of the current traditional banking 01:01:48.060 --> 01:01:48.960 system? 01:01:48.960 --> 01:01:52.620 And is online banking, is that an opportunity 01:01:52.620 --> 01:02:00.030 to shed a costly branch network, shed costly legacy systems, 01:02:00.030 --> 01:02:04.200 or, frankly, just do it better, cheaper? 01:02:04.200 --> 01:02:06.750 Ultimately, that's what it is about being an entrepreneur. 01:02:06.750 --> 01:02:10.710 Can you do a better, cheaper, faster? 01:02:10.710 --> 01:02:15.430 And so how have the traditional banks reacted? 01:02:15.430 --> 01:02:17.440 They're not staying still. 01:02:17.440 --> 01:02:20.410 All the way back from the online banking movement, 01:02:20.410 --> 01:02:24.280 they were right foursquare in the online banking 01:02:24.280 --> 01:02:27.490 movement of the 1990s to the late noughts. 01:02:27.490 --> 01:02:29.620 What have they done in the last decade? 01:02:29.620 --> 01:02:32.320 They've started to say maybe we have to rebrand and do 01:02:32.320 --> 01:02:33.400 something different. 01:02:33.400 --> 01:02:35.395 Goldman Sachs is not the only one with Marcus. 01:02:35.395 --> 01:02:37.180 Now, Goldman Sachs is an investment bank. 01:02:37.180 --> 01:02:39.850 They were not protecting their brand. 01:02:39.850 --> 01:02:45.070 But, as I said, Cap One in 2011 bought this big ING Direct. 01:02:45.070 --> 01:02:49.600 Cap One said oh, for $9 billion, we can move to this other area. 01:02:49.600 --> 01:02:53.950 But then others are starting to have named branded products. 01:02:53.950 --> 01:02:56.920 JPMorgan and others are saying we maybe 01:02:56.920 --> 01:03:03.100 should do something that looks like an online-- 01:03:03.100 --> 01:03:06.880 we'll even use a different name and call it something else 01:03:06.880 --> 01:03:08.260 and react. 01:03:08.260 --> 01:03:14.440 So I wouldn't count out any of these big traditional banks. 01:03:14.440 --> 01:03:16.300 And where we are in three or five 01:03:16.300 --> 01:03:17.680 years is going to be interesting. 01:03:17.680 --> 01:03:21.930 I also would encourage you to think big tech. 01:03:21.930 --> 01:03:25.060 Apple Credit Card is not quite a challenger bank. 01:03:25.060 --> 01:03:28.450 Amazon Prime is not quite a challenger bank. 01:03:28.450 --> 01:03:33.550 But when do they, like Alibaba, file for a banking license, 01:03:33.550 --> 01:03:39.240 like Alibaba did and got Ant Financial, like in Korea 01:03:39.240 --> 01:03:47.430 Kakao has done and already is just getting huge market share? 01:03:47.430 --> 01:03:54.020 So I don't think that's too much in our distant future. 01:03:54.020 --> 01:03:58.070 I think that's near term, that we'll see that. 01:03:58.070 --> 01:04:00.140 Now, we have different traditions in the US. 01:04:00.140 --> 01:04:02.780 We have a tradition, both in culture 01:04:02.780 --> 01:04:05.330 and embedded in law, about separation 01:04:05.330 --> 01:04:07.400 of banking and commerce. 01:04:07.400 --> 01:04:10.730 And we went through this in the 1980s and 1990s, 01:04:10.730 --> 01:04:13.040 when the department store chains, like Walmart, 01:04:13.040 --> 01:04:14.360 wanted to get into banking. 01:04:14.360 --> 01:04:17.330 And they-- there was all this sort of back and forth about 01:04:17.330 --> 01:04:20.090 whether they can have banking licenses, what type of banking 01:04:20.090 --> 01:04:23.480 license, and industrial loan companies and other things 01:04:23.480 --> 01:04:28.340 really in the intricacy of banking law and licensing. 01:04:28.340 --> 01:04:30.980 We will have some of those debates and challenges 01:04:30.980 --> 01:04:37.280 as well as, I believe, Amazon and Apple and others and Google 01:04:37.280 --> 01:04:40.180 get further into this space.