WEBVTT 00:00:00.500 --> 00:00:02.455 [SQUEAKING] 00:00:02.455 --> 00:00:04.419 [RUSTLING] 00:00:04.419 --> 00:00:05.401 [CLICKING] 00:00:10.630 --> 00:00:12.280 GARY GENSLER: So this FinTech course-- 00:00:12.280 --> 00:00:14.740 this is for those who want to explore FinTech, 00:00:14.740 --> 00:00:17.710 how the technologies are disrupting financial services. 00:00:17.710 --> 00:00:20.440 That's the core of it. 00:00:20.440 --> 00:00:22.660 Technology is disrupting finance. 00:00:22.660 --> 00:00:25.640 And we'll talk a lot about this, that finance and technology 00:00:25.640 --> 00:00:28.900 have lived in simpatico, in some relationship 00:00:28.900 --> 00:00:30.800 together for thousands of years. 00:00:30.800 --> 00:00:37.090 In fact, money and ledgers were initial financial technologies. 00:00:37.090 --> 00:00:39.570 And we'll talk about what makes something 00:00:39.570 --> 00:00:44.200 in this faculty's mind a financial technology that's 00:00:44.200 --> 00:00:46.720 really changing the world and then just 00:00:46.720 --> 00:00:48.250 the technology that exists. 00:00:48.250 --> 00:00:50.380 The telephone, for instance, at one point, 00:00:50.380 --> 00:00:54.340 in that time, in the 1920s, was, in essence, 00:00:54.340 --> 00:00:57.040 a financial technology that rapidly 00:00:57.040 --> 00:00:59.860 changed the world of finance. 00:00:59.860 --> 00:01:03.250 Or even in the 19th century, the telegraph rapidly 00:01:03.250 --> 00:01:06.830 changed parts of finance, when you could send your first money 00:01:06.830 --> 00:01:07.653 gram-- 00:01:07.653 --> 00:01:09.820 or in those days, it was called something different, 00:01:09.820 --> 00:01:12.520 but it was a telegram attached with money 00:01:12.520 --> 00:01:15.490 in the 1870s and 1880s. 00:01:15.490 --> 00:01:17.813 But this course is going to be about the cutting edge. 00:01:17.813 --> 00:01:19.480 We're going to be talking about business 00:01:19.480 --> 00:01:23.020 models and the like around AI, deep learning, 00:01:23.020 --> 00:01:26.260 blockchain technology, OpenAPI. 00:01:26.260 --> 00:01:29.110 10 and 20 years from now, OpenAPI 00:01:29.110 --> 00:01:33.260 will not be taught, by my view, in a FinTech course. 00:01:33.260 --> 00:01:36.070 But it's the relevant topics of the day. 00:01:36.070 --> 00:01:38.950 And we'll be looking at the competitive landscape. 00:01:38.950 --> 00:01:45.380 Those of you that have decided to take this course on top 00:01:45.380 --> 00:01:48.500 of consumer finance course, the half-semester course that 00:01:48.500 --> 00:01:51.170 was at the same time, you will know 00:01:51.170 --> 00:01:54.990 that I usually teach in the concept of business strategy. 00:01:54.990 --> 00:01:59.480 What is the strategy that these startups which big tech, which 00:01:59.480 --> 00:02:03.650 incumbents are looking at this point in time, in this day 00:02:03.650 --> 00:02:05.630 and age, in this sector? 00:02:05.630 --> 00:02:09.199 And this course is also, I should say, being recorded. 00:02:09.199 --> 00:02:10.960 It's being recorded for some students 00:02:10.960 --> 00:02:13.940 who can't join us simultaneously or what's 00:02:13.940 --> 00:02:15.860 called synchronous learning. 00:02:15.860 --> 00:02:19.370 And these recordings will be posted on Canvas 00:02:19.370 --> 00:02:21.110 within a day or two. 00:02:21.110 --> 00:02:23.390 Lena and Romain and I just have to remember 00:02:23.390 --> 00:02:26.720 how to do that and actually post each of the recordings. 00:02:26.720 --> 00:02:30.140 They also might be shared, just to alert you, 00:02:30.140 --> 00:02:33.110 in OpenCourseWare in the fall or later. 00:02:33.110 --> 00:02:36.470 I've chose with MIT that if we're recording them anyway, 00:02:36.470 --> 00:02:42.100 maybe if they come out being anywhere valuable, 00:02:42.100 --> 00:02:44.470 that we would up to the broader community. 00:02:44.470 --> 00:02:50.340 So these might be shared more broadly come the fall as well. 00:02:50.340 --> 00:02:53.210 It's also really to gain critical reasoning skills 00:02:53.210 --> 00:02:55.790 around the ground truths of FinTech, 00:02:55.790 --> 00:03:01.370 separating hype from reality. 00:03:01.370 --> 00:03:06.890 Every week, there's a posting of three or four readings. 00:03:06.890 --> 00:03:09.530 I understand that even if we were all on campus, 00:03:09.530 --> 00:03:11.570 you might not read every word of that. 00:03:11.570 --> 00:03:15.260 But they really are sort of the foundation. 00:03:15.260 --> 00:03:17.550 And I hope that in each lecture, in each class, 00:03:17.550 --> 00:03:19.730 we can go beyond that. 00:03:19.730 --> 00:03:22.250 But this week, the Bank of International Settlement 00:03:22.250 --> 00:03:25.190 Working Paper and the Financial Stability Board 00:03:25.190 --> 00:03:28.670 are two papers that a lot of people turn to. 00:03:28.670 --> 00:03:31.220 The Financial Stability Board is a group 00:03:31.220 --> 00:03:35.420 of 20 countries, the G20 countries, that 00:03:35.420 --> 00:03:40.340 have banded together and their treasury secretaries or finance 00:03:40.340 --> 00:03:44.300 ministers and central banks and securities regulators 00:03:44.300 --> 00:03:47.180 have formed this thing called the Financial Stability Board. 00:03:47.180 --> 00:03:48.710 And they publish very good work. 00:03:48.710 --> 00:03:51.590 This paper came out in 2017. 00:03:51.590 --> 00:03:53.490 It feels a little dated right now. 00:03:53.490 --> 00:03:55.820 But it still felt quite relevant. 00:03:55.820 --> 00:03:58.220 And then, of course, the Bank of international Settlement 00:03:58.220 --> 00:04:02.240 is 60 or 70 central banks out of Basel. 00:04:02.240 --> 00:04:04.492 And they write very good work. 00:04:04.492 --> 00:04:06.200 I thought it was also interesting to take 00:04:06.200 --> 00:04:10.730 the current chair of the US bank regulator, the Federal Deposit 00:04:10.730 --> 00:04:16.519 Insurance Corporation, Chair McWilliams, and her view 00:04:16.519 --> 00:04:19.730 as to the future of banking, what's going on right now. 00:04:19.730 --> 00:04:23.150 So that's why I grabbed these three as an intro. 00:04:23.150 --> 00:04:24.920 If you've not yet read them, I think 00:04:24.920 --> 00:04:27.350 go back, try to at least skim them, 00:04:27.350 --> 00:04:31.520 get your sense of what they are. 00:04:31.520 --> 00:04:34.510 And each class, I will also list study questions. 00:04:34.510 --> 00:04:36.890 And the goal of my listing study questions 00:04:36.890 --> 00:04:39.710 is not just for you to think about these questions 00:04:39.710 --> 00:04:41.960 beforehand, but you will also see where 00:04:41.960 --> 00:04:43.550 do I want to land the class? 00:04:43.550 --> 00:04:48.660 Where do I think these are the central learning objectives? 00:04:48.660 --> 00:04:51.560 And this is usually in a classroom setting, 00:04:51.560 --> 00:04:53.930 where I'll say "let's pause here and I'll 00:04:53.930 --> 00:04:56.180 engage in some conversation." 00:04:56.180 --> 00:04:59.180 Now, I do cold call in the regular classroom. 00:04:59.180 --> 00:05:02.720 I don't know if anybody wants to raise their hand now and answer 00:05:02.720 --> 00:05:04.910 any one of these questions, but it 00:05:04.910 --> 00:05:08.390 would be great to get a little bit of life and community 00:05:08.390 --> 00:05:11.060 in this, if anybody can address themselves. 00:05:11.060 --> 00:05:15.200 What are the major technological trends materially influencing 00:05:15.200 --> 00:05:16.910 finance right now that you think about, 00:05:16.910 --> 00:05:21.710 whether it's in the US or anywhere around the globe? 00:05:21.710 --> 00:05:24.260 And this course will be taught from the perspective 00:05:24.260 --> 00:05:25.760 around the globe, even though I'm 00:05:25.760 --> 00:05:27.140 more knowledgeable in the US. 00:05:27.140 --> 00:05:31.980 We will be talking about Europe, Latin America, Asia throughout, 00:05:31.980 --> 00:05:35.960 a little bit about Africa as well. 00:05:35.960 --> 00:05:38.323 Romain, I'm pausing for you to do your-- 00:05:38.323 --> 00:05:40.490 ROMAIN DE SAINT PERIER: We have our first volunteer. 00:05:40.490 --> 00:05:42.050 Thank you very much, Luke. 00:05:42.050 --> 00:05:42.970 The floor is yours. 00:05:46.352 --> 00:05:48.060 AUDIENCE: I'll answer the first question. 00:05:48.060 --> 00:05:51.390 The main technological change that we see in US 00:05:51.390 --> 00:05:55.320 and outside of US is versus open banking, use of a lot of APIs. 00:05:55.320 --> 00:05:57.623 They can be applicable to other websites. 00:05:57.623 --> 00:05:59.040 GARY GENSLER: All right, and we're 00:05:59.040 --> 00:06:04.500 going to spend a whole class on OpenAPIs in two weeks. 00:06:04.500 --> 00:06:06.540 But this is an important part of marketing, 00:06:06.540 --> 00:06:09.930 opening up the banks' ledgers and their data. 00:06:09.930 --> 00:06:13.380 And data, as people would like to say, 00:06:13.380 --> 00:06:17.490 is sort of the new oil in the business. 00:06:17.490 --> 00:06:20.280 It's very valuable for us. 00:06:20.280 --> 00:06:21.773 Anybody else, Romain? 00:06:21.773 --> 00:06:22.940 ROMAIN DE SAINT PERIER: Yes. 00:06:27.030 --> 00:06:29.200 AUDIENCE: The natural language processing so that we 00:06:29.200 --> 00:06:32.807 can have the robotic advisors. 00:06:32.807 --> 00:06:33.640 GARY GENSLER: Right. 00:06:33.640 --> 00:06:36.310 So natural language processing is the concept 00:06:36.310 --> 00:06:40.390 that you can take something that's in human language 00:06:40.390 --> 00:06:43.270 and put it into machine or computer language 00:06:43.270 --> 00:06:45.320 or go the vice versa. 00:06:45.320 --> 00:06:47.710 And it's not actually new in 2020. 00:06:47.710 --> 00:06:50.020 Some form of natural language processing 00:06:50.020 --> 00:06:53.860 has been around for decades, just in terms of reading-- 00:06:53.860 --> 00:06:59.770 reading computer code and putting it into an audio voice 00:06:59.770 --> 00:07:01.210 or going backwards. 00:07:01.210 --> 00:07:05.240 Or every postal service of a major country around the globe 00:07:05.240 --> 00:07:08.320 has had something to read our scribbled handwriting 00:07:08.320 --> 00:07:11.060 and trying to read that handwriting 00:07:11.060 --> 00:07:13.210 and then put it into something where they know 00:07:13.210 --> 00:07:16.210 which post box to send it to. 00:07:16.210 --> 00:07:19.210 But natural language processing, we'll spend a fair bit of time, 00:07:19.210 --> 00:07:21.010 and robotics. 00:07:21.010 --> 00:07:21.703 Romain? 00:07:21.703 --> 00:07:23.120 ROMAIN DE SAINT PERIER: And now we 00:07:23.120 --> 00:07:26.750 have Ivy, who raised her hand. 00:07:26.750 --> 00:07:30.320 AUDIENCE: Yeah, so I think we've seen a lot of digitization 00:07:30.320 --> 00:07:33.320 in the e-commerce space as well, especially 00:07:33.320 --> 00:07:34.880 in places like China. 00:07:34.880 --> 00:07:38.540 And you see this kind of divergence between China 00:07:38.540 --> 00:07:41.300 and, say, the US and the way we use mobile pay 00:07:41.300 --> 00:07:45.200 and the way that they've really adopted like Alipay and WePay 00:07:45.200 --> 00:07:45.715 as well. 00:07:45.715 --> 00:07:47.090 GARY GENSLER: And Ivy, why do you 00:07:47.090 --> 00:07:51.030 think it happened so, as you say, at this divergence, 00:07:51.030 --> 00:07:54.840 why it happened maybe a little faster in China? 00:07:54.840 --> 00:07:57.950 AUDIENCE: So I think it's pretty interesting, because I think 00:07:57.950 --> 00:07:59.660 a place like China, as an example, 00:07:59.660 --> 00:08:03.140 is probably less developed in terms 00:08:03.140 --> 00:08:06.920 of even just their financial structure, whereas the place 00:08:06.920 --> 00:08:08.750 like the US, it's quite dominated. 00:08:08.750 --> 00:08:10.410 And it's really competitive. 00:08:10.410 --> 00:08:12.050 But it's also really consolidated. 00:08:12.050 --> 00:08:14.420 So you see these countries where-- 00:08:14.420 --> 00:08:16.260 I mean, I think the way I think about it 00:08:16.260 --> 00:08:20.487 is the subway systems in China or Taiwan 00:08:20.487 --> 00:08:22.070 or a lot of these developing countries 00:08:22.070 --> 00:08:24.100 are much better because they were just-- 00:08:24.100 --> 00:08:26.540 they came a little bit later. 00:08:26.540 --> 00:08:30.080 And I just look at that analogy similarly to kind 00:08:30.080 --> 00:08:33.860 of where payments are, because you kind of go 00:08:33.860 --> 00:08:36.363 from 0 to 100 versus we are kind of something-- 00:08:36.363 --> 00:08:38.530 GARY GENSLER: No, I think Ivy's raised a good point. 00:08:38.530 --> 00:08:41.960 There's times when a country is growing rapidly. 00:08:41.960 --> 00:08:46.010 And China, for instance, had been growing at 8% to 10% GDP 00:08:46.010 --> 00:08:47.770 growth a year. 00:08:47.770 --> 00:08:51.650 And before corona, it had come down to still a robust 6% 00:08:51.650 --> 00:08:52.970 a year. 00:08:52.970 --> 00:08:55.960 But within that context, many things 00:08:55.960 --> 00:09:02.090 leapfrogged incumbents in Europe and in North America. 00:09:02.090 --> 00:09:05.060 And in the payment space in particular, two big tech 00:09:05.060 --> 00:09:07.460 companies-- 00:09:07.460 --> 00:09:12.380 Alibaba, that really is the dominant online retailing 00:09:12.380 --> 00:09:16.180 company, and Tencent, which was the dominant online sort 00:09:16.180 --> 00:09:20.300 of social networking and messaging company-- 00:09:20.300 --> 00:09:23.510 leapfrogged the banking system, the traditional banking system, 00:09:23.510 --> 00:09:25.460 and now with WeChat Pay and Alipay 00:09:25.460 --> 00:09:29.780 control well over 90% of retail payments, small dollar, 00:09:29.780 --> 00:09:32.660 and small and medium-sized enterprise payments. 00:09:32.660 --> 00:09:35.240 They don't dominate large wholesale payments, 00:09:35.240 --> 00:09:37.685 but put in the retail space, absolutely. 00:09:37.685 --> 00:09:38.810 And I would agree with Ivy. 00:09:38.810 --> 00:09:40.010 They kind of leapfrogged us. 00:09:40.010 --> 00:09:45.530 But even Kenya leapfrogged us with M-Pesa, a technology that 00:09:45.530 --> 00:09:49.400 was pushed forward by a telephone company, Safaricom, 00:09:49.400 --> 00:09:53.030 when they noticed that folks were trading mobile minutes 00:09:53.030 --> 00:09:54.958 as a form of money. 00:09:54.958 --> 00:09:56.500 ROMAIN DE SAINT PERIER: Gary, we have 00:09:56.500 --> 00:09:59.160 two more hands that are up. 00:09:59.160 --> 00:10:00.040 We can start with-- 00:10:00.040 --> 00:10:00.460 GARY GENSLER: All right, why don't we 00:10:00.460 --> 00:10:01.543 do those and then move on? 00:10:01.543 --> 00:10:03.510 So who are the two people? 00:10:03.510 --> 00:10:05.153 And they can just go in turn. 00:10:05.153 --> 00:10:06.820 ROMAIN DE SAINT PERIER: So we had Laira, 00:10:06.820 --> 00:10:08.090 but she just disappeared. 00:10:08.090 --> 00:10:09.730 So we'll go with Alida. 00:10:09.730 --> 00:10:12.550 GARY GENSLER: All right, one, thank you. 00:10:12.550 --> 00:10:15.450 AUDIENCE: Yeah, so I, to kind of add on to Ivy's point, 00:10:15.450 --> 00:10:18.100 is a lot of financial institutions in emerging 00:10:18.100 --> 00:10:23.380 markets did not typically cater towards the mass-market 00:10:23.380 --> 00:10:25.400 consumer population. 00:10:25.400 --> 00:10:28.570 And so it really allowed very quickly for these big tech 00:10:28.570 --> 00:10:32.380 companies to jump in a way that you couldn't do so in the-- 00:10:32.380 --> 00:10:36.010 in more developed markets, where the financial institutions 00:10:36.010 --> 00:10:39.130 already were catering to the large majority of the consumer 00:10:39.130 --> 00:10:41.100 population. 00:10:41.100 --> 00:10:42.930 GARY GENSLER: Right, so it's about 00:10:42.930 --> 00:10:47.890 actual financial inclusion and reaching out and so forth. 00:10:47.890 --> 00:10:52.050 So what I'm going to do today is try to cover, in the minutes 00:10:52.050 --> 00:10:54.840 we have, a little bit about the financial world. 00:10:54.840 --> 00:10:58.230 What do we mean, FinTech shaping the future 00:10:58.230 --> 00:10:59.490 of the financial world? 00:10:59.490 --> 00:11:02.880 What do I what do I think of it, having spent my life-- first, 00:11:02.880 --> 00:11:06.180 I was 18 years at Goldman Sachs. 00:11:06.180 --> 00:11:08.580 Then I worked in the public sector, 00:11:08.580 --> 00:11:12.120 but always sort of around finance, with the US Treasury 00:11:12.120 --> 00:11:15.660 Department, with Paul Sarbanes doing Sarbanes-Oxley, and then 00:11:15.660 --> 00:11:19.470 later running a market regulator, the Commodity 00:11:19.470 --> 00:11:24.290 Futures Trading Commission, in the Obama administration. 00:11:24.290 --> 00:11:27.330 What do we mean by the financial world? 00:11:27.330 --> 00:11:28.860 A little touch on FinTech-- 00:11:28.860 --> 00:11:30.360 that's the whole class, of course, 00:11:30.360 --> 00:11:32.760 but just a little touch on FinTech. 00:11:32.760 --> 00:11:35.340 Thirdly, again, just a little review 00:11:35.340 --> 00:11:37.840 of these three big trends-- 00:11:37.840 --> 00:11:41.290 of AI, open banking, blockchain technology-- 00:11:41.290 --> 00:11:44.520 what do these trends mean? 00:11:44.520 --> 00:11:46.050 And then the actors-- 00:11:46.050 --> 00:11:49.200 and the actors, I think that some people will use the word 00:11:49.200 --> 00:11:51.960 FinTech to mean these disruptors, companies 00:11:51.960 --> 00:11:55.470 like Toast getting into the payment space for restaurants 00:11:55.470 --> 00:12:00.090 or Lending Club and peer-to-peer lending or Robinhood, an app 00:12:00.090 --> 00:12:03.000 you can download and trade stocks. 00:12:03.000 --> 00:12:06.510 A lot of people constrain the study and the topic of FinTech 00:12:06.510 --> 00:12:08.370 just to the disruptors. 00:12:08.370 --> 00:12:10.630 I think that that's too narrow. 00:12:10.630 --> 00:12:12.720 I think that we really need to think 00:12:12.720 --> 00:12:14.610 of the actors and the field, more 00:12:14.610 --> 00:12:17.190 broadly about the incumbents. 00:12:17.190 --> 00:12:20.270 This is sort of big finance, we might say, 00:12:20.270 --> 00:12:26.190 the Barclays banks and the JPMorgans and so forth. 00:12:26.190 --> 00:12:28.320 And we need to think of big tech, 00:12:28.320 --> 00:12:33.600 as we just talked with Ivy about Alibaba and Tencent 00:12:33.600 --> 00:12:34.850 getting into this business. 00:12:34.850 --> 00:12:38.550 But we see Apple Credit Card and others and Facebook trying 00:12:38.550 --> 00:12:40.950 to stand up a world currency. 00:12:40.950 --> 00:12:42.870 And then it's the disruptors. 00:12:42.870 --> 00:12:47.010 So I think it's a much more robust conversation 00:12:47.010 --> 00:12:49.980 and an important conversation, the strategy 00:12:49.980 --> 00:12:51.208 amongst these three pieces. 00:12:51.208 --> 00:12:53.250 And then, of course, we've got to do a little bit 00:12:53.250 --> 00:12:55.620 on our teaching team, our schedule and assignments 00:12:55.620 --> 00:12:58.000 and so forth. 00:12:58.000 --> 00:13:01.920 So what do I think of is in the financial world and what it is? 00:13:01.920 --> 00:13:05.900 Well, finance basically stands, like this hourglass 00:13:05.900 --> 00:13:08.160 in the top-right-hand corner, stands 00:13:08.160 --> 00:13:11.580 right at the neck of an hourglass, intermediating, 00:13:11.580 --> 00:13:16.080 standing between people that have money and need money, 00:13:16.080 --> 00:13:20.180 people that have risk and want to get rid of it, lay it off, 00:13:20.180 --> 00:13:22.590 and somebody that wants to pick it up. 00:13:22.590 --> 00:13:25.290 And I have for decades, since I was at Goldman Sachs, 00:13:25.290 --> 00:13:28.860 thought where we were, we were at the neck of the hourglass. 00:13:28.860 --> 00:13:31.800 And for good or for bad, that's also 00:13:31.800 --> 00:13:34.800 part of why finance in many countries 00:13:34.800 --> 00:13:37.290 is able to collect economic rents. 00:13:37.290 --> 00:13:40.470 Economic rents is that classic conceptual framework 00:13:40.470 --> 00:13:43.860 of collecting profits or revenues in excess 00:13:43.860 --> 00:13:47.100 of what classic economics might tell you 00:13:47.100 --> 00:13:50.320 would be a competitive supply and demand space. 00:13:50.320 --> 00:13:52.950 But if you stand at the neck of an hourglass, 00:13:52.950 --> 00:13:57.510 between trillions of money flowing from those who want it 00:13:57.510 --> 00:14:00.780 and those who have it, and effectively trillions 00:14:00.780 --> 00:14:05.460 of risk between those who have risk and want to lay it off 00:14:05.460 --> 00:14:07.290 and others who are willing to hedge it-- 00:14:07.290 --> 00:14:10.230 if you're at that neck of the hourglass, so to speak, 00:14:10.230 --> 00:14:13.170 if you just collect a few grains of sand 00:14:13.170 --> 00:14:16.470 for the trillions that go by, it can collect a lot. 00:14:16.470 --> 00:14:19.420 In the United States, for one, for instance, 00:14:19.420 --> 00:14:21.825 our financial sector takes about 7 and 1/2% 00:14:21.825 --> 00:14:24.450 of our Gross Domestic Product. 00:14:24.450 --> 00:14:26.520 Nowhere is it written it has to. 00:14:26.520 --> 00:14:29.280 In fact, in the 1950s and '60s, it was more like 3 00:14:29.280 --> 00:14:31.470 and 1/2% to 4%. 00:14:31.470 --> 00:14:33.960 But persistently, it's grown as a percentage 00:14:33.960 --> 00:14:37.560 of our economy, standing, intermediating money and risk. 00:14:41.670 --> 00:14:44.690 Let's just see if I can get this to work. 00:14:44.690 --> 00:14:46.600 There, so the functions, the functions 00:14:46.600 --> 00:14:47.890 are intermediating credit. 00:14:47.890 --> 00:14:49.130 That's lending. 00:14:49.130 --> 00:14:50.980 Investments, we all know that. 00:14:50.980 --> 00:14:53.890 Risk transformation-- think of any time one of us 00:14:53.890 --> 00:14:58.510 buys insurance on an automobile or a car or on our life, 00:14:58.510 --> 00:15:02.410 but also risk transformation that investment banks do, even 00:15:02.410 --> 00:15:05.680 between somebody that's issuing stock and somebody that's 00:15:05.680 --> 00:15:06.550 buying stock. 00:15:06.550 --> 00:15:09.010 That's a transference of risk in terms 00:15:09.010 --> 00:15:11.530 of whether that startup will do well. 00:15:11.530 --> 00:15:13.450 Of course, there's the capital markets. 00:15:13.450 --> 00:15:15.460 And at the center of the capital markets 00:15:15.460 --> 00:15:18.520 is the price, the money and risk that's 00:15:18.520 --> 00:15:20.120 flowing through the system. 00:15:20.120 --> 00:15:22.570 And there's plenty of advice to go around. 00:15:22.570 --> 00:15:24.820 Now, we usually think about it in sectors. 00:15:24.820 --> 00:15:26.830 And every one of these sectors, whether it's 00:15:26.830 --> 00:15:29.530 commercial banking, asset management, insurance, 00:15:29.530 --> 00:15:32.350 investment banking, advisories, we 00:15:32.350 --> 00:15:34.810 will touch upon during this semester. 00:15:34.810 --> 00:15:36.130 And please, interrupt. 00:15:36.130 --> 00:15:39.430 If your keen interest is about insurance companies 00:15:39.430 --> 00:15:42.670 or your keen interest is about investment banks, 00:15:42.670 --> 00:15:47.170 then pull the community into that in these discussions. 00:15:47.170 --> 00:15:50.350 But we're going to try to talk about multiple sectors, 00:15:50.350 --> 00:15:52.510 multiple functions, as contrasted 00:15:52.510 --> 00:15:55.780 to the half-semester consumer finance course that was really 00:15:55.780 --> 00:16:01.090 just about one slice, household lending, and largely 00:16:01.090 --> 00:16:05.020 the commercial banks and investment banks around that. 00:16:05.020 --> 00:16:07.360 This is a much broader topic. 00:16:07.360 --> 00:16:09.760 And I hope the learning objective is ultimately 00:16:09.760 --> 00:16:14.320 to understand how technology can transform finance 00:16:14.320 --> 00:16:18.360 at any particular given time. 00:16:18.360 --> 00:16:20.140 Romain, are there any hands up? 00:16:20.140 --> 00:16:22.600 I'm sort of every once in a while looking to you to see 00:16:22.600 --> 00:16:24.322 if I keep going or pause. 00:16:24.322 --> 00:16:26.530 ROMAIN DE SAINT PERIER: No hands at the moment, Gary. 00:16:26.530 --> 00:16:27.530 GARY GENSLER: All right. 00:16:27.530 --> 00:16:31.120 The financial world, in this, I think, four key things 00:16:31.120 --> 00:16:32.110 to think about. 00:16:32.110 --> 00:16:34.330 And this is sort of in a framework of thinking 00:16:34.330 --> 00:16:35.680 about financial technology. 00:16:35.680 --> 00:16:40.720 Data-- of course, data is that new oil, so to speak, 00:16:40.720 --> 00:16:44.260 for investing, for market-making, for marketing, 00:16:44.260 --> 00:16:45.760 trying to get new customers. 00:16:45.760 --> 00:16:48.550 I mean, how many times do we get a pop-up ads. 00:16:48.550 --> 00:16:51.890 I found in teaching about student loans, 00:16:51.890 --> 00:16:53.540 I was researching student loans. 00:16:53.540 --> 00:16:55.780 And my god, the last six weeks I've 00:16:55.780 --> 00:16:59.830 got more advertisement for student loans, 00:16:59.830 --> 00:17:02.380 even now I'm a professor at MIT. 00:17:02.380 --> 00:17:05.589 It's because I was researching the topic of student loans, 00:17:05.589 --> 00:17:07.960 and all of a sudden, now I am getting 00:17:07.960 --> 00:17:15.160 a lot of unsolicited ads and emails, even, on the topic. 00:17:15.160 --> 00:17:16.599 The financial world it always has 00:17:16.599 --> 00:17:19.420 to think about the management of their balance sheet. 00:17:19.420 --> 00:17:22.089 And if you're starting a FinTech company, 00:17:22.089 --> 00:17:24.910 are you using your balance sheet or somebody else's balance 00:17:24.910 --> 00:17:25.660 sheet? 00:17:25.660 --> 00:17:27.980 And just as there's cloud computing, 00:17:27.980 --> 00:17:31.030 that today, cloud computing has dramatically 00:17:31.030 --> 00:17:34.270 shifted the ability of startups, a disruptor 00:17:34.270 --> 00:17:40.540 can come into a business and basically the rent versus bill 00:17:40.540 --> 00:17:41.680 decision changes. 00:17:41.680 --> 00:17:45.220 I can read somebody else's data storage capacity. 00:17:45.220 --> 00:17:51.010 I can basically rent the cloud instead of building my own data 00:17:51.010 --> 00:17:52.030 warehouse. 00:17:52.030 --> 00:17:54.190 That was a big change about 15 years ago. 00:17:54.190 --> 00:17:58.240 And it's made startups more viable in the 2020s. 00:17:58.240 --> 00:18:02.320 Also, the ability to raise money in the capital markets, what's 00:18:02.320 --> 00:18:06.670 called securitizations, which started decades ago, really 00:18:06.670 --> 00:18:10.870 took off by the 1990s, also allows a startup like Lending 00:18:10.870 --> 00:18:13.540 Club and others to say I'll raise my money elsewhere. 00:18:13.540 --> 00:18:16.240 I don't have to have a balance sheet. 00:18:16.240 --> 00:18:19.390 And then there's the various risks that you have to manage. 00:18:19.390 --> 00:18:22.060 And most importantly, we're going to spend a lot of time 00:18:22.060 --> 00:18:24.130 doing this class on this fourth point-- user 00:18:24.130 --> 00:18:26.200 experience, user interface. 00:18:26.200 --> 00:18:29.860 Much of what's happened with mobile phones, 00:18:29.860 --> 00:18:35.110 with apps that you can download for free have given all of us 00:18:35.110 --> 00:18:39.070 a better user experience than online banking. 00:18:39.070 --> 00:18:41.900 Online banking has been around for over 20 years. 00:18:41.900 --> 00:18:43.870 Most of the major banks around the globe 00:18:43.870 --> 00:18:47.920 had a viable platform for online banking by the naughts, 00:18:47.920 --> 00:18:52.380 whether it was 2005 for some or 2008 or others or so forth. 00:18:52.380 --> 00:18:56.020 But by the naughts, most had online banking. 00:18:56.020 --> 00:18:58.300 And yet, their user experience wasn't what 00:18:58.300 --> 00:19:01.300 we all wanted, and certainly wasn't what-- maybe 00:19:01.300 --> 00:19:04.300 if the newer users, as millennials, 00:19:04.300 --> 00:19:06.490 were coming into the marketplace, 00:19:06.490 --> 00:19:09.890 to do it on your mobile phone, to do it conveniently. 00:19:09.890 --> 00:19:12.890 So the user experience and the user interface 00:19:12.890 --> 00:19:16.990 is the critical competitive place. 00:19:16.990 --> 00:19:18.520 So what do we mean by FinTech? 00:19:18.520 --> 00:19:22.210 I like going back to that 2017 Financial Stability Board 00:19:22.210 --> 00:19:22.950 report. 00:19:22.950 --> 00:19:26.020 That doesn't mean that they have it quite right. 00:19:26.020 --> 00:19:27.880 But they basically talk about that it's 00:19:27.880 --> 00:19:34.170 technology-enabled innovation in the financial world that's 00:19:34.170 --> 00:19:39.650 going to some new business and it's material. 00:19:39.650 --> 00:19:43.060 So, as I mentioned earlier, the telegraph in the 1830s 00:19:43.060 --> 00:19:43.750 comes along. 00:19:43.750 --> 00:19:46.180 And by the 1860s and 1870s, we're 00:19:46.180 --> 00:19:49.090 starting to see digital money. 00:19:49.090 --> 00:19:56.130 The first form of digital money is already about 140 years old. 00:19:56.130 --> 00:20:00.300 I mean, we think we're living in this digital age, and we are. 00:20:00.300 --> 00:20:03.300 And I would note this about the corona crisis 00:20:03.300 --> 00:20:04.650 that we're living in-- 00:20:04.650 --> 00:20:06.820 the corona crisis, in my opinion, 00:20:06.820 --> 00:20:09.600 will accelerate this digitization. 00:20:09.600 --> 00:20:11.730 We've already, in the last 30 years, 00:20:11.730 --> 00:20:15.960 significantly digitized our world. 00:20:15.960 --> 00:20:18.280 And if we were all together in a classroom, 00:20:18.280 --> 00:20:20.470 I would ask by a show of hands how many of you 00:20:20.470 --> 00:20:24.240 have used paper money or currency in the last two 00:20:24.240 --> 00:20:25.770 or three days. 00:20:25.770 --> 00:20:27.630 And maybe a quarter of you might have 00:20:27.630 --> 00:20:31.230 said you had used paper money in the last two or three days. 00:20:31.230 --> 00:20:33.910 But let me ask you in the middle of the corona crisis-- 00:20:33.910 --> 00:20:37.240 and Romain, you can tell me if any hands go up-- 00:20:37.240 --> 00:20:39.690 we'll you use the blue hand, anybody 00:20:39.690 --> 00:20:44.905 that's used paper, physical money in the last two days. 00:20:46.492 --> 00:20:48.950 ROMAIN DE SAINT PERIER: Thank you, Alida, for volunteering. 00:20:48.950 --> 00:20:49.760 The floor is yours. 00:20:54.220 --> 00:20:57.138 AUDIENCE: I have for grocery shopping and so forth. 00:20:57.138 --> 00:20:58.430 GARY GENSLER: So you actually-- 00:20:58.430 --> 00:21:03.030 and they'll still take the paper money behind the counter 00:21:03.030 --> 00:21:06.920 without wiping it down with some cleaner? 00:21:06.920 --> 00:21:07.570 AUDIENCE: Yes. 00:21:07.570 --> 00:21:10.420 And I took out a lot of cash before everything 00:21:10.420 --> 00:21:11.542 happened, so-- 00:21:11.542 --> 00:21:13.310 GARY GENSLER: Ah, ah. 00:21:13.310 --> 00:21:16.090 Alida, may I ask, was that just sort of insurance, 00:21:16.090 --> 00:21:18.837 managing risk, as we say, as part of finance, where you're-- 00:21:18.837 --> 00:21:19.420 AUDIENCE: Yes. 00:21:19.420 --> 00:21:20.795 GARY GENSLER: --managing the risk 00:21:20.795 --> 00:21:23.830 that the banking sector and the ATMs and things might not work? 00:21:23.830 --> 00:21:25.480 AUDIENCE: Yes. 00:21:25.480 --> 00:21:27.693 GARY GENSLER: And do I see Devin's hand? 00:21:27.693 --> 00:21:29.360 ROMAIN DE SAINT PERIER: That is correct. 00:21:29.360 --> 00:21:33.022 AUDIENCE: I can only do laundry using cash. 00:21:33.022 --> 00:21:36.490 So I use it for now, but-- 00:21:36.490 --> 00:21:39.560 GARY GENSLER: But see how few of us, of 80 plus people, 00:21:39.560 --> 00:21:40.250 two people. 00:21:40.250 --> 00:21:42.740 Now, we're in the middle of a corona crisis. 00:21:42.740 --> 00:21:47.180 I would predict that as we come out of this, whether this is-- 00:21:47.180 --> 00:21:49.670 whether this is a matter of a couple of few months 00:21:49.670 --> 00:21:51.410 or whether this is, unfortunately, 00:21:51.410 --> 00:21:55.670 as long as couple of years, wherever we come out, 00:21:55.670 --> 00:21:58.310 that the corona crisis will accelerate 00:21:58.310 --> 00:22:00.470 an already existing trend. 00:22:00.470 --> 00:22:04.370 And that already existing trend distorts further and further 00:22:04.370 --> 00:22:07.910 digitalization of commerce, that that laundromat 00:22:07.910 --> 00:22:14.000 that Devin goes to will take a QR code or a swipe of a phone 00:22:14.000 --> 00:22:15.410 more readily. 00:22:15.410 --> 00:22:20.600 And yes, I think it was Anita said that she took out cash 00:22:20.600 --> 00:22:21.720 before the crisis. 00:22:21.720 --> 00:22:22.850 And that's true. 00:22:22.850 --> 00:22:25.310 There is still going to be some people that probably 00:22:25.310 --> 00:22:27.530 also went and bought gold. 00:22:27.530 --> 00:22:29.960 And we should respect that, that that's 00:22:29.960 --> 00:22:33.260 sort of an insurance policy against the digital world 00:22:33.260 --> 00:22:34.467 collapsing. 00:22:34.467 --> 00:22:36.050 ROMAIN DE SAINT PERIER: Gary, it seems 00:22:36.050 --> 00:22:37.520 like Devin has a follow-up. 00:22:37.520 --> 00:22:39.974 GARY GENSLER: Yes, please. 00:22:39.974 --> 00:22:42.150 No, and maybe Devin just left his hand up. 00:22:42.150 --> 00:22:42.900 AUDIENCE: I apol-- 00:22:42.900 --> 00:22:44.540 GARY GENSLER: Maybe after you speak, 00:22:44.540 --> 00:22:46.515 you actually then take your hand down. 00:22:46.515 --> 00:22:46.800 AUDIENCE: All right. 00:22:46.800 --> 00:22:49.133 GARY GENSLER: So what are the technologies of our times? 00:22:49.133 --> 00:22:52.590 What's sort of rolling with it now, no longer the telegraph 00:22:52.590 --> 00:22:54.240 or so forth? 00:22:54.240 --> 00:22:55.855 Well, I put up my favorites. 00:22:55.855 --> 00:22:57.480 And then I kind of think, well, maybe I 00:22:57.480 --> 00:22:58.938 shouldn't have put the cloud there. 00:22:58.938 --> 00:23:01.680 Maybe the cloud really isn't a technology that's 00:23:01.680 --> 00:23:03.900 pushing us forward right now. 00:23:03.900 --> 00:23:07.860 And yet, what's interesting is finance uses the cloud less 00:23:07.860 --> 00:23:09.450 than most industries. 00:23:09.450 --> 00:23:13.245 And this is particularly the case of incumbents. 00:23:13.245 --> 00:23:15.870 Country by country-- it doesn't matter whether we're in Brazil, 00:23:15.870 --> 00:23:19.670 whether we're in Europe, whether we're in the US-- 00:23:19.670 --> 00:23:22.370 big incumbents, like JPMorgan and Bank 00:23:22.370 --> 00:23:25.710 of America and Barclays, tend to still have their own data 00:23:25.710 --> 00:23:26.210 centers. 00:23:26.210 --> 00:23:27.140 Now, that's shifting. 00:23:27.140 --> 00:23:29.450 I think the 2020s will see them shift over. 00:23:29.450 --> 00:23:33.260 But they've tended to want to sort of control their destiny. 00:23:33.260 --> 00:23:36.230 I think they will shift because it's lower cost and better 00:23:36.230 --> 00:23:40.070 cybersecurity generally if you have it, frankly, 00:23:40.070 --> 00:23:45.960 at Baidu in China or Microsoft or AWS and so forth here. 00:23:45.960 --> 00:23:48.680 But startups dramatically use the cloud. 00:23:48.680 --> 00:23:51.980 This is this buy versus build sort of scenario. 00:23:51.980 --> 00:23:54.260 Why build my own data center? 00:23:54.260 --> 00:23:55.280 I can rent it. 00:23:55.280 --> 00:23:57.050 I can use somebody else. 00:23:57.050 --> 00:23:59.240 But all the others we're going to talk about-- 00:23:59.240 --> 00:24:02.480 and I should say that there's a little-- 00:24:02.480 --> 00:24:06.320 a little sleight of hand by my list, because many of these 00:24:06.320 --> 00:24:09.440 are actually artificial intelligence and machine 00:24:09.440 --> 00:24:11.600 learning, which is part of artificial intelligence. 00:24:11.600 --> 00:24:14.030 Natural language processing, in essence, 00:24:14.030 --> 00:24:16.040 is artificial intelligence. 00:24:16.040 --> 00:24:20.870 Chatbots are sort of that as well, and robotic process 00:24:20.870 --> 00:24:21.520 automation. 00:24:21.520 --> 00:24:23.870 But I split them out because I think 00:24:23.870 --> 00:24:26.300 it's relevant that we kind of have a chance to talk 00:24:26.300 --> 00:24:30.370 about each of these as we go. 00:24:30.370 --> 00:24:32.950 I'm pausing every once in a while, Romain, 00:24:32.950 --> 00:24:34.000 but you can let me now. 00:24:36.600 --> 00:24:39.210 I like to think of this in the context of history. 00:24:39.210 --> 00:24:42.290 Maybe that's just because I've always loved studying history. 00:24:42.290 --> 00:24:44.940 But what's the customer interface 00:24:44.940 --> 00:24:49.530 that we've seen all the way back from sort of prehistory, 00:24:49.530 --> 00:24:53.940 that the customer interface was in tents or bricks and mortar 00:24:53.940 --> 00:24:57.360 later, all the way up through where 00:24:57.360 --> 00:25:00.960 we got credit cards invented in the 1940s and '50s? 00:25:00.960 --> 00:25:02.580 That was a FinTech. 00:25:02.580 --> 00:25:05.520 That was a new financial technology. 00:25:05.520 --> 00:25:08.580 Visa, here in the US, was a network 00:25:08.580 --> 00:25:12.450 of banks started by a California bank called Bank of America. 00:25:12.450 --> 00:25:15.270 And they wanted to have a nationwide network. 00:25:15.270 --> 00:25:16.740 And other banks joined it. 00:25:16.740 --> 00:25:19.130 That became the Visa network. 00:25:19.130 --> 00:25:20.860 But where are we now? 00:25:20.860 --> 00:25:26.003 What are the technologies now that-- 00:25:26.003 --> 00:25:27.920 I want to make sure I'm clicking on the right. 00:25:30.440 --> 00:25:32.810 So the base that you might be thinking about-- 00:25:32.810 --> 00:25:37.490 mobile payments, the internet, even contactless cards-- 00:25:37.490 --> 00:25:40.370 I would say that was kind of a last phase. 00:25:40.370 --> 00:25:43.640 That was the phase that really shifted finance 00:25:43.640 --> 00:25:47.660 in the naughts and early teens, and that we really 00:25:47.660 --> 00:25:52.590 are in terms of customer interface 00:25:52.590 --> 00:25:58.610 is now conversational interfaces, chatbots 00:25:58.610 --> 00:26:00.860 and conversational interfaces. 00:26:00.860 --> 00:26:03.350 That's the cutting edge. 00:26:03.350 --> 00:26:05.990 Yes, contactless cards are important. 00:26:05.990 --> 00:26:08.180 Yes, of course, we need mobile payments. 00:26:08.180 --> 00:26:09.990 No doubt about it. 00:26:09.990 --> 00:26:12.740 But if you're starting a new business right now, 00:26:12.740 --> 00:26:14.150 you're going to start a disruptor 00:26:14.150 --> 00:26:18.110 and say I'm going to do online banking, that's yesterday. 00:26:18.110 --> 00:26:20.930 You have to find a way to do that online banking 00:26:20.930 --> 00:26:25.040 in a way that has such a better customer interface. 00:26:25.040 --> 00:26:28.430 And you might be using some form of OpenAPIs 00:26:28.430 --> 00:26:32.490 or robotic process automation and chatbots to do it. 00:26:32.490 --> 00:26:36.290 But these are all kind of build up on each other. 00:26:36.290 --> 00:26:38.190 Then there's risk management. 00:26:38.190 --> 00:26:40.220 So one is the customer side. 00:26:40.220 --> 00:26:42.440 One is the funding and risk management side. 00:26:42.440 --> 00:26:44.380 And you can look throughout history. 00:26:44.380 --> 00:26:47.240 And I spent a lot of my life in my career 00:26:47.240 --> 00:26:49.040 around the capital markets. 00:26:49.040 --> 00:26:51.890 I was at Goldman Sachs for 18 years. 00:26:51.890 --> 00:26:54.470 And I had to think a lot with hundreds 00:26:54.470 --> 00:26:56.572 of other people about risk management 00:26:56.572 --> 00:26:57.530 and what we were doing. 00:26:57.530 --> 00:27:01.520 And in the 1980s and the 1990s, a big thing 00:27:01.520 --> 00:27:05.450 was asset-backed securitization and interest rate swaps 00:27:05.450 --> 00:27:07.910 and even, yes, credit default swaps that helped 00:27:07.910 --> 00:27:11.780 bring down several economies in 2008. 00:27:11.780 --> 00:27:16.190 But those innovations of the 1970s, the 1990s 00:27:16.190 --> 00:27:19.550 were dramatically shifting, dramatically 00:27:19.550 --> 00:27:21.280 shifting what was going on. 00:27:21.280 --> 00:27:25.140 But I would say now, it's really about machine learning. 00:27:25.140 --> 00:27:29.210 Machine learning is an ability to extract correlations, 00:27:29.210 --> 00:27:35.190 extract patterns from data in a way that we couldn't before. 00:27:35.190 --> 00:27:39.950 And many of you have studied linear algebra. 00:27:39.950 --> 00:27:43.970 We're blessed in this class to have remarkable science 00:27:43.970 --> 00:27:47.300 technology and math folks. 00:27:47.300 --> 00:27:50.690 Not all of you-- we accept all the English majors and history 00:27:50.690 --> 00:27:52.050 majors as well. 00:27:52.050 --> 00:27:56.780 But I'm saying that you know that from linear algebra 00:27:56.780 --> 00:28:01.470 and basic statistics, you can do a lot of regression analyses. 00:28:01.470 --> 00:28:03.890 Machine learning is going a little beyond that 00:28:03.890 --> 00:28:06.020 and extracting patterns from that 00:28:06.020 --> 00:28:08.810 which was more difficult to extract when you were just 00:28:08.810 --> 00:28:10.940 doing regression analysis. 00:28:10.940 --> 00:28:14.500 And so there's patterns that we might 00:28:14.500 --> 00:28:19.810 see in the data that says you're a good risk or a less good risk 00:28:19.810 --> 00:28:23.740 that you wouldn't quite say in a traditional credit scoring 00:28:23.740 --> 00:28:24.430 systems. 00:28:24.430 --> 00:28:27.710 I think that's where we're going right now. 00:28:27.710 --> 00:28:29.860 Romain, anything in the chat rooms? 00:28:29.860 --> 00:28:31.690 Or I'm going to pause for a second. 00:28:35.545 --> 00:28:37.420 ROMAIN DE SAINT PERIER: Nothing so far, Gary. 00:28:37.420 --> 00:28:38.600 GARY GENSLER: All right. 00:28:38.600 --> 00:28:42.010 So it's fertile ground. 00:28:42.010 --> 00:28:44.310 And we talked a little bit about this before. 00:28:44.310 --> 00:28:46.750 But the fertile ground of finance 00:28:46.750 --> 00:28:50.920 is we've basically digitized money securities and credit 00:28:50.920 --> 00:28:52.270 over the last 30 years. 00:28:52.270 --> 00:28:56.950 The corona crisis is just going to push that further faster, 00:28:56.950 --> 00:28:58.570 in my opinion. 00:28:58.570 --> 00:29:00.850 We have this vast amount of data. 00:29:00.850 --> 00:29:03.310 And we're starting to see patterns in that data 00:29:03.310 --> 00:29:06.370 that we didn't recognize before. 00:29:06.370 --> 00:29:12.070 I mentioned this in the consumer finance course that some of you 00:29:12.070 --> 00:29:16.480 were with me on, but somebody has actually studied something 00:29:16.480 --> 00:29:18.430 like this to say that if you charge 00:29:18.430 --> 00:29:22.480 your phone overnight every night, if you charge 00:29:22.480 --> 00:29:24.730 your phone every night, you apparently 00:29:24.730 --> 00:29:26.410 are a better credit than if you don't 00:29:26.410 --> 00:29:28.480 charge your phone every night. 00:29:28.480 --> 00:29:30.400 You might think, oh my gosh, I'd better 00:29:30.400 --> 00:29:36.040 be charging my phone because Apple is watching. 00:29:36.040 --> 00:29:39.490 They're going to be watching whether I charge my phone. 00:29:39.490 --> 00:29:42.300 And in China, there's a whole social credit 00:29:42.300 --> 00:29:47.140 system that takes data from many of your online apps. 00:29:47.140 --> 00:29:49.380 It's not just taking your payment information 00:29:49.380 --> 00:29:53.220 from Alipay or WeChat Pay, but that social credit system 00:29:53.220 --> 00:29:56.520 is checking even your participation 00:29:56.520 --> 00:29:58.590 in dating websites. 00:29:58.590 --> 00:29:59.610 I kid you not. 00:29:59.610 --> 00:30:00.930 I kid you not. 00:30:00.930 --> 00:30:04.200 So that social credit system in China 00:30:04.200 --> 00:30:09.000 or the private sector's approach to collecting data in the West 00:30:09.000 --> 00:30:13.110 have sort of the same goal in mind-- 00:30:13.110 --> 00:30:19.320 to understand the customers more and to basically 00:30:19.320 --> 00:30:23.290 provide more marketing, but also to assess risk. 00:30:23.290 --> 00:30:24.810 And, by the way, the corona crisis 00:30:24.810 --> 00:30:28.710 might change even the West's view of data sharing, 00:30:28.710 --> 00:30:32.640 because when we have Google and the US partnering up to say 00:30:32.640 --> 00:30:36.480 we can track everybody to see how this disease, how 00:30:36.480 --> 00:30:40.320 this disease is propagating through society, that we 00:30:40.320 --> 00:30:46.740 need the modern technology of our position location trackers. 00:30:46.740 --> 00:30:50.100 A position location tracker is called a cell phone. 00:30:50.100 --> 00:30:52.110 A smartphone is tracking where we 00:30:52.110 --> 00:30:56.920 are if you go running with it, if you go driving, hiking, 00:30:56.920 --> 00:30:58.750 all of that data. 00:30:58.750 --> 00:31:01.900 But now here, even in the US and in Europe, 00:31:01.900 --> 00:31:03.640 we're starting to say do we partner, 00:31:03.640 --> 00:31:07.390 as the official sector, with that data 00:31:07.390 --> 00:31:12.990 to keep our societies maybe a little safer, and then, 00:31:12.990 --> 00:31:16.050 of course, the rapid expansion of computation power 00:31:16.050 --> 00:31:16.857 and so forth. 00:31:16.857 --> 00:31:18.440 Now, I think it will change something. 00:31:18.440 --> 00:31:21.780 This disruptive potential, I think, has a dramatic change. 00:31:21.780 --> 00:31:25.500 We'll be talking about this slide the whole half semester. 00:31:25.500 --> 00:31:30.210 But managing risks, updating the customer user interfaces, 00:31:30.210 --> 00:31:31.980 and financial inclusion are the three 00:31:31.980 --> 00:31:36.180 that I want to focus a lot on, managing risk. 00:31:36.180 --> 00:31:40.200 And AI is also about targeting products as well. 00:31:40.200 --> 00:31:43.440 I think the interesting challenge is, near the bottom, 00:31:43.440 --> 00:31:46.260 will some revenue models shift? 00:31:46.260 --> 00:31:51.120 We saw a company called Credit Karma start here in the US. 00:31:51.120 --> 00:31:56.250 Credit Karma started basically with the idea to get a free-- 00:31:56.250 --> 00:31:59.980 free is the operative word-- a free credit score. 00:31:59.980 --> 00:32:03.420 The founders of Credit Karma couldn't get their credit score 00:32:03.420 --> 00:32:04.770 from the traditional companies-- 00:32:04.770 --> 00:32:08.270 TransUnion, Experian, and the like. 00:32:08.270 --> 00:32:13.350 And so they started an app just in the last 10 years. 00:32:13.350 --> 00:32:18.934 And in January, they sold Credit Karma for $7 billion. 00:32:18.934 --> 00:32:21.920 Now, Credit Karma is still a free app. 00:32:21.920 --> 00:32:26.930 And you might say how can Credit Karma commercialize a free app? 00:32:26.930 --> 00:32:30.320 In 2019, there was $1 billion in revenue for Credit Karma 00:32:30.320 --> 00:32:35.300 because they basically had built files on 106 million Americans. 00:32:35.300 --> 00:32:39.530 They built data files on 106 million Americans providing 00:32:39.530 --> 00:32:40.220 a free app. 00:32:40.220 --> 00:32:43.730 And, by the way, they do not have 106 million customers. 00:32:43.730 --> 00:32:46.100 They even built credit files on people 00:32:46.100 --> 00:32:47.920 that weren't their customers. 00:32:47.920 --> 00:32:50.120 And they were able to commercialize and build 00:32:50.120 --> 00:32:54.950 revenues around that data stream and, in essence, referencing. 00:32:54.950 --> 00:32:59.090 They get referral fees when somebody then does buy a loan 00:32:59.090 --> 00:33:03.740 or takes out a loan through the Credit Karma platform. 00:33:03.740 --> 00:33:05.740 ROMAIN DE SAINT PERIER: Gary, we have a question 00:33:05.740 --> 00:33:07.000 from [INAUDIBLE]. 00:33:07.000 --> 00:33:07.875 GARY GENSLER: Please. 00:33:11.000 --> 00:33:13.970 You might need to unmute yourself. 00:33:13.970 --> 00:33:14.890 We-- 00:33:14.890 --> 00:33:16.160 ROMAIN DE SAINT PERIER: So I am normally unmuting? 00:33:16.160 --> 00:33:16.993 AUDIENCE: I'm just-- 00:33:16.993 --> 00:33:18.500 ROMAIN DE SAINT PERIER: Yeah, OK. 00:33:18.500 --> 00:33:19.917 AUDIENCE: I'm just wondering, what 00:33:19.917 --> 00:33:23.150 is the difference between big tech and FinTech, 00:33:23.150 --> 00:33:26.810 and why it is important preparing and learning 00:33:26.810 --> 00:33:29.330 the FinTech now? 00:33:29.330 --> 00:33:31.273 GARY GENSLER: So I understand the second part 00:33:31.273 --> 00:33:31.940 of the question. 00:33:31.940 --> 00:33:34.250 You said the difference between what and FinTech 00:33:34.250 --> 00:33:35.300 at the beginning? 00:33:35.300 --> 00:33:36.305 AUDIENCE: Big tech. 00:33:36.305 --> 00:33:37.430 GARY GENSLER: OK, big tech. 00:33:37.430 --> 00:33:41.300 So by that, I think of-- 00:33:41.300 --> 00:33:45.260 I use the word FinTech in the broad way the Financial 00:33:45.260 --> 00:33:46.700 Stability Board does. 00:33:46.700 --> 00:33:50.660 I use it as is the intersection of finance and technology 00:33:50.660 --> 00:33:52.780 where the technology is new-- 00:33:52.780 --> 00:33:56.180 so not the telephone, not the internet, 00:33:56.180 --> 00:33:59.960 but it's something new, like AI and machine learning, 00:33:59.960 --> 00:34:04.860 that may materially change the provision of finance. 00:34:04.860 --> 00:34:09.170 So I use it to capture the whole field. 00:34:09.170 --> 00:34:11.989 Why it's important is I think it's important every decade. 00:34:11.989 --> 00:34:14.600 I don't think it's just important now, 00:34:14.600 --> 00:34:17.989 because I think technologies will come along each half 00:34:17.989 --> 00:34:20.360 decade, each decade that will materially 00:34:20.360 --> 00:34:25.100 change finance and provide the entrepreneurs in this class 00:34:25.100 --> 00:34:30.500 an opportunity to break into the wide margins. 00:34:30.500 --> 00:34:35.060 In the US, 7 and 1/2% of our economy or $1 and 1/2 trillion 00:34:35.060 --> 00:34:39.139 is the revenues of finance. 00:34:39.139 --> 00:34:41.389 So if you're an entrepreneur, you want part of that $1 00:34:41.389 --> 00:34:42.350 and 1/2 trillion. 00:34:42.350 --> 00:34:44.120 You want an opportunity. 00:34:44.120 --> 00:34:46.010 And usually, it's technology that's 00:34:46.010 --> 00:34:47.420 changing business models. 00:34:47.420 --> 00:34:50.870 When the internet came along in the 1990s, that 00:34:50.870 --> 00:34:54.350 provided an opportunity for PayPal to start and say maybe 00:34:54.350 --> 00:34:57.170 we can provide a better payment solution. 00:34:57.170 --> 00:34:59.420 And subsequent payment solutions, 00:34:59.420 --> 00:35:03.320 like Venmo and TransferWise and Square, 00:35:03.320 --> 00:35:07.310 have all been opportunities to chip away a little bit. 00:35:07.310 --> 00:35:10.790 I would say only in the opportunity-- 00:35:10.790 --> 00:35:13.100 the opportunities came because technology 00:35:13.100 --> 00:35:15.950 was changing the field. 00:35:15.950 --> 00:35:18.320 What's the difference between big tech and FinTech? 00:35:18.320 --> 00:35:22.010 Big tech, to me, are big platform companies-- 00:35:22.010 --> 00:35:27.020 in the US, the Facebooks and the Amazons and the Apples, 00:35:27.020 --> 00:35:31.730 in China, Baidu, Alibaba, Tencent, in Africa, 00:35:31.730 --> 00:35:33.920 even, Safaricom got in. 00:35:33.920 --> 00:35:35.230 We could go country by country. 00:35:35.230 --> 00:35:39.590 India, the big tech has gone into payments as well. 00:35:39.590 --> 00:35:44.630 I think that big tech companies have dramatic advantages. 00:35:44.630 --> 00:35:47.870 And then I separate what I would call FinTech startups 00:35:47.870 --> 00:35:50.030 or FinTech disruptors. 00:35:50.030 --> 00:35:53.630 I always put the second noun in there, disruptor or startup, 00:35:53.630 --> 00:35:57.630 because to me, the word FinTech is the whole field. 00:35:57.630 --> 00:35:58.533 I hope that helps. 00:35:58.533 --> 00:36:00.700 ROMAIN DE SAINT PERIER: And we have another question 00:36:00.700 --> 00:36:01.685 from [INAUDIBLE]. 00:36:01.685 --> 00:36:02.560 GARY GENSLER: Please. 00:36:05.420 --> 00:36:06.870 AUDIENCE: Yeah, hi, Professor. 00:36:06.870 --> 00:36:08.520 One question that I have is actually 00:36:08.520 --> 00:36:11.055 one concerned with privacy. 00:36:14.010 --> 00:36:16.650 At some point, do you think that we 00:36:16.650 --> 00:36:20.310 would have up give up to some extent our privacy in order 00:36:20.310 --> 00:36:21.240 to-- 00:36:21.240 --> 00:36:24.600 for me to get a credit from a startup 00:36:24.600 --> 00:36:25.883 or get a credit from somebody? 00:36:25.883 --> 00:36:27.300 GARY GENSLER: Well, I think you've 00:36:27.300 --> 00:36:31.140 raised a dramatic issue for society at large. 00:36:31.140 --> 00:36:33.670 And finance is one example of it. 00:36:33.670 --> 00:36:35.070 But yes, we have-- 00:36:35.070 --> 00:36:39.030 we have shared our personal data much more broadly 00:36:39.030 --> 00:36:42.120 in the last 10 years, and certainly in the last 30 years, 00:36:42.120 --> 00:36:44.810 than we did in societies before that. 00:36:44.810 --> 00:36:49.410 And in terms of getting credit card, yes, we've 00:36:49.410 --> 00:36:56.220 been sharing data for 50 years through various credit-- 00:36:56.220 --> 00:36:58.020 consumer credit companies. 00:36:58.020 --> 00:37:05.370 The Fair Isaac Company was founded almost 60 years ago 00:37:05.370 --> 00:37:07.980 by two people out of Stanford, actually, 00:37:07.980 --> 00:37:10.680 one named Fair and one named Isaac. 00:37:10.680 --> 00:37:13.080 And that led to something called FICO scores, 00:37:13.080 --> 00:37:16.230 which are used in over 30 countries around the globe. 00:37:16.230 --> 00:37:19.500 These FICO scores took some personal data, 00:37:19.500 --> 00:37:22.860 as to whether you were paying your bills on time. 00:37:22.860 --> 00:37:27.600 But now, we can go beyond that and capture alternative data. 00:37:27.600 --> 00:37:31.130 We can capture somebody's digital footprint. 00:37:31.130 --> 00:37:34.470 And in China, they are doing that with social credit scoring 00:37:34.470 --> 00:37:38.580 and Alibaba is with Alipay and WeChat Pay. 00:37:38.580 --> 00:37:41.040 But Amazon is capturing our data as well. 00:37:41.040 --> 00:37:44.330 Amazon captures any Amazon Prime customer. 00:37:44.330 --> 00:37:47.280 And I'm sure amongst at of us, there are many Amazon Prime 00:37:47.280 --> 00:37:48.090 customers. 00:37:48.090 --> 00:37:51.490 That data is being captured in some way. 00:37:51.490 --> 00:37:54.660 Now, it leads to more financial inclusion. 00:37:54.660 --> 00:37:57.810 But it also raises all sorts of issues around privacy 00:37:57.810 --> 00:38:00.300 that we're grappling with as societies. 00:38:00.300 --> 00:38:02.490 In Europe, they passed something called 00:38:02.490 --> 00:38:08.760 the GDPR, Global Directive on Privacy Regulation. 00:38:08.760 --> 00:38:12.390 Here in the US, only California's stepped into this 00:38:12.390 --> 00:38:15.450 and passed something that went into place last year, 00:38:15.450 --> 00:38:18.540 the California Consumer Protection Act. 00:38:18.540 --> 00:38:21.240 So these are things that society will grapple with. 00:38:21.240 --> 00:38:25.560 Technology can enable privacy as well as take away privacy. 00:38:25.560 --> 00:38:31.360 Technology actually can enable us to keep our privacy. 00:38:31.360 --> 00:38:34.180 But the technology companies and the financial companies 00:38:34.180 --> 00:38:35.260 want our data. 00:38:35.260 --> 00:38:38.050 So they're not going to necessarily want 00:38:38.050 --> 00:38:39.550 us to keep our privacy. 00:38:39.550 --> 00:38:41.980 So it's an interesting-- 00:38:41.980 --> 00:38:44.350 technology can enable it, but technology 00:38:44.350 --> 00:38:47.070 can take it away as well. 00:38:47.070 --> 00:38:51.115 So just moving on a little bit, this three big areas-- 00:38:51.115 --> 00:38:52.740 we're not going to spend a lot of time, 00:38:52.740 --> 00:38:55.020 but artificial intelligence, machine learning. 00:38:55.020 --> 00:38:57.840 I love to give a shout out to another 00:38:57.840 --> 00:39:01.440 MITer, or Lex Fridman, who has a wonderful set 00:39:01.440 --> 00:39:04.950 of online courses, if you wanted to take Lex's courses. 00:39:04.950 --> 00:39:08.260 This was online before online became such the vogue now 00:39:08.260 --> 00:39:09.690 that we're all doing it. 00:39:09.690 --> 00:39:12.240 But Lex has this wonderful course. 00:39:12.240 --> 00:39:15.000 And I captured his one slide. 00:39:15.000 --> 00:39:17.940 What is AI and machine learning? 00:39:17.940 --> 00:39:21.210 It's extracting useful patterns from data. 00:39:21.210 --> 00:39:23.460 You don't have to be a computer scientist. 00:39:23.460 --> 00:39:26.640 But it's basically that's the key thing, and something 00:39:26.640 --> 00:39:28.620 that might not be linear, something 00:39:28.620 --> 00:39:32.430 that might not fit into that old statistics class and regression 00:39:32.430 --> 00:39:35.850 analysis or linear algebra that we think about. 00:39:35.850 --> 00:39:38.400 It all relies on good data, cleaning up 00:39:38.400 --> 00:39:41.598 data, and good questions. 00:39:41.598 --> 00:39:42.390 Where do we see it? 00:39:42.390 --> 00:39:44.970 We see it in facial recognition, image classification, 00:39:44.970 --> 00:39:48.210 speech recognition, et cetera, this list from Lex's list 00:39:48.210 --> 00:39:52.680 that you can see online, medical diagnoses-- 00:39:52.680 --> 00:39:54.360 in the midst of the corona crisis, 00:39:54.360 --> 00:39:57.360 a lot are turning to AI and machine learning to see 00:39:57.360 --> 00:40:00.420 what patterns can we see beyond the patterns you 00:40:00.420 --> 00:40:03.150 can see by classic statistics? 00:40:03.150 --> 00:40:05.260 It's going beyond that. 00:40:05.260 --> 00:40:08.010 But in this field, in this field, 00:40:08.010 --> 00:40:11.490 in finance, we're seeing it in every one of these areas. 00:40:11.490 --> 00:40:15.540 And we have two classes, so I won't dive into it now. 00:40:15.540 --> 00:40:17.410 But whether it's asset management, where 00:40:17.410 --> 00:40:20.760 BlackRock is literally taking-- 00:40:20.760 --> 00:40:27.880 and all the news items for the top companies, every quarter 00:40:27.880 --> 00:40:30.270 that a company reports its earnings, 00:40:30.270 --> 00:40:35.320 BlackRock is listening digitally to their shareholder meetings 00:40:35.320 --> 00:40:37.780 and their shareholder conference calls 00:40:37.780 --> 00:40:42.880 and seeing which words in there, which words relate to stock 00:40:42.880 --> 00:40:44.680 markets going up or stock markets 00:40:44.680 --> 00:40:47.950 going down, using machine learning and asset management. 00:40:47.950 --> 00:40:53.380 We talked about call centers and chatbots and so forth. 00:40:53.380 --> 00:40:57.220 I don't know how many of you are Bank of America customers. 00:40:57.220 --> 00:41:01.900 But bank of America has millions of its customers using Erica. 00:41:01.900 --> 00:41:05.120 Think the Siri of banking. 00:41:05.120 --> 00:41:08.560 Think the Alexa of banking, a virtual assistant 00:41:08.560 --> 00:41:10.740 called Erica-- 00:41:10.740 --> 00:41:13.230 Bank of America, get it? 00:41:13.230 --> 00:41:17.480 All right, that was their marketing thing, I guess, now. 00:41:17.480 --> 00:41:21.000 So AI and machine learning is dramatically shifting. 00:41:21.000 --> 00:41:23.940 The question in FinTech for the big incumbents, 00:41:23.940 --> 00:41:28.140 how do I do it to raise my revenues, 00:41:28.140 --> 00:41:31.710 lower the amount of capital that I have to use, 00:41:31.710 --> 00:41:33.570 raise my profits? 00:41:33.570 --> 00:41:36.870 The question for big tech is how do I do this maybe 00:41:36.870 --> 00:41:40.560 to get into the business, to leapfrog, as Ivy said, 00:41:40.560 --> 00:41:44.520 that Alibaba and WeChat Pay leapfrogged the Chinese banks 00:41:44.520 --> 00:41:45.660 and payments? 00:41:45.660 --> 00:41:49.200 If I'm big tech, can I leapfrog big finance? 00:41:49.200 --> 00:41:53.130 Because, frankly, if I'm Google, I'm better at it right now. 00:41:53.130 --> 00:41:55.740 Google has a comparative advantage. 00:41:55.740 --> 00:41:59.610 Can I maybe use my comparative advantage and AI and leapfrog? 00:41:59.610 --> 00:42:03.090 If I'm a startup, maybe I can give a better customer 00:42:03.090 --> 00:42:03.940 interface. 00:42:03.940 --> 00:42:07.620 I can do something with this to better manage risk 00:42:07.620 --> 00:42:10.260 with alternative data. 00:42:10.260 --> 00:42:14.800 So that's how I think of it. 00:42:14.800 --> 00:42:16.870 OpenAPI-- I should pause. 00:42:16.870 --> 00:42:18.873 Romain, any questions or hands up? 00:42:18.873 --> 00:42:21.040 ROMAIN DE SAINT PERIER: Yes, we have one from Laira. 00:42:21.040 --> 00:42:21.915 GARY GENSLER: Please. 00:42:24.860 --> 00:42:27.370 AUDIENCE: Yeah, so I was just curious to know how, 00:42:27.370 --> 00:42:31.510 internationally speaking, regulation 00:42:31.510 --> 00:42:36.640 hampers the capacity of FinTech companies to expand, just 00:42:36.640 --> 00:42:37.910 on an international level. 00:42:37.910 --> 00:42:43.340 So for the US, is it more regulated and, hence, more 00:42:43.340 --> 00:42:47.782 harder for FinTech companies to expand than for in China? 00:42:47.782 --> 00:42:48.990 GARY GENSLER: Great question. 00:42:48.990 --> 00:42:50.230 And again, who asked? 00:42:50.230 --> 00:42:53.090 I just didn't rem-- is Leia? 00:42:53.090 --> 00:42:54.130 AUDIENCE: Laira. 00:42:54.130 --> 00:42:57.620 GARY GENSLER: Laira, all right, Laira, good to see you again. 00:42:57.620 --> 00:43:00.950 I'm sorry that I don't physically see you. 00:43:00.950 --> 00:43:04.420 But good to see you remotely. 00:43:04.420 --> 00:43:07.330 Every country is taking a little bit different approach. 00:43:07.330 --> 00:43:12.790 And the range of approaches here could be from you're a-- 00:43:12.790 --> 00:43:15.250 let's call them a startup or a disruptor. 00:43:15.250 --> 00:43:17.080 You're starting something. 00:43:17.080 --> 00:43:19.020 You'd better just come into whatever 00:43:19.020 --> 00:43:21.160 our traditional regulatory framework. 00:43:21.160 --> 00:43:24.730 If you're taking deposits and offering loans, 00:43:24.730 --> 00:43:26.170 you've got to be a bank. 00:43:26.170 --> 00:43:28.000 If you're just doing payments, you 00:43:28.000 --> 00:43:33.520 might come under a European, US African e-money law 00:43:33.520 --> 00:43:37.270 and just have to do the things around money laundering 00:43:37.270 --> 00:43:39.010 and anti-money laundering. 00:43:39.010 --> 00:43:43.100 If you're like Robinhood here in the US, 00:43:43.100 --> 00:43:45.790 you would need to register as a broker-dealer. 00:43:45.790 --> 00:43:48.640 And there's been this big debate around cryptocurrencies. 00:43:48.640 --> 00:43:50.320 Are they securities or not? 00:43:50.320 --> 00:43:52.110 And in some countries, like the US, 00:43:52.110 --> 00:43:56.860 they generally are, unless you're 00:43:56.860 --> 00:43:59.860 like Bitcoin and Ethereum. 00:43:59.860 --> 00:44:03.610 But the debate, Lyra, is really country by country, 00:44:03.610 --> 00:44:11.000 is are these startups and these technologies, 00:44:11.000 --> 00:44:14.240 do they fit into the current regulatory regimes? 00:44:14.240 --> 00:44:17.210 By and large, if you take deposits and you make loans, 00:44:17.210 --> 00:44:20.220 you're a bank pretty much anywhere around the globe. 00:44:20.220 --> 00:44:22.370 If you facilitate the movement of money, 00:44:22.370 --> 00:44:26.390 you're probably in some e-money laws around the globe. 00:44:26.390 --> 00:44:29.780 Securities, if you're actually facilitating 00:44:29.780 --> 00:44:32.510 the raising of money and the selling of securities, 00:44:32.510 --> 00:44:35.480 you usually have to register as a securities broker-dealer 00:44:35.480 --> 00:44:37.460 somewhere around the globe. 00:44:37.460 --> 00:44:40.240 But a lot of places also have this concept 00:44:40.240 --> 00:44:44.900 of some form of regulatory forbearance called sandboxes. 00:44:44.900 --> 00:44:48.620 The idea is let's promote some innovation, whether it's 00:44:48.620 --> 00:44:51.590 in Hong Kong, whether it's in Asia, whether it's in the US, 00:44:51.590 --> 00:44:56.330 promote some motivation by saying if it stays small enough 00:44:56.330 --> 00:44:58.490 and it's new enough, you might not 00:44:58.490 --> 00:45:03.540 have to comply with all of the regulatory regimes. 00:45:03.540 --> 00:45:06.330 The other interesting challenge is sometimes things come along 00:45:06.330 --> 00:45:07.710 that don't fit in a box. 00:45:07.710 --> 00:45:10.200 They don't quite fit in to something. 00:45:10.200 --> 00:45:12.950 So the internet came along in the 1990s. 00:45:12.950 --> 00:45:16.160 Internet in the 1990s was facilitating 00:45:16.160 --> 00:45:18.215 a rapid change in finance. 00:45:18.215 --> 00:45:19.590 The internet comes along and then 00:45:19.590 --> 00:45:21.720 the question is literally a question 00:45:21.720 --> 00:45:24.150 that the securities regulators around the globe 00:45:24.150 --> 00:45:25.290 had to deal with-- 00:45:25.290 --> 00:45:26.970 what if I put a bulletin board up 00:45:26.970 --> 00:45:29.760 on the internet that offers people to buy and sell 00:45:29.760 --> 00:45:31.560 securities on the internet? 00:45:31.560 --> 00:45:33.150 Now, it wasn't a traditional exchange. 00:45:33.150 --> 00:45:37.260 It didn't look like the Tokyo or Shanghai or London or New York 00:45:37.260 --> 00:45:40.470 exchanges of old, where there were humans yelling 00:45:40.470 --> 00:45:42.750 and screaming on the floors of stock exchanges 00:45:42.750 --> 00:45:43.920 around the globe. 00:45:43.920 --> 00:45:45.990 It was just a bulletin board where 00:45:45.990 --> 00:45:48.060 buyers and sellers could meet. 00:45:48.060 --> 00:45:51.300 And usually they were insurance companies and various asset 00:45:51.300 --> 00:45:52.800 managers. 00:45:52.800 --> 00:45:55.890 That question was a ripe question in the 1990s. 00:45:55.890 --> 00:45:59.880 And over time, we ended up with two tiers of regulation 00:45:59.880 --> 00:46:00.810 for exchanges. 00:46:00.810 --> 00:46:03.260 We had fully-regulated exchanges, 00:46:03.260 --> 00:46:06.060 and this was true in Europe and the US at the time. 00:46:06.060 --> 00:46:09.760 China sort of got there a little later. 00:46:09.760 --> 00:46:11.860 But in Europe and the US, it was like all right, 00:46:11.860 --> 00:46:14.068 there's going to be these fully-regulated traditional 00:46:14.068 --> 00:46:15.010 exchanges. 00:46:15.010 --> 00:46:17.160 And then there would be another tier. 00:46:17.160 --> 00:46:19.170 In the US, we called them broker dealers, 00:46:19.170 --> 00:46:22.590 alternative trading systems, ATS's. 00:46:22.590 --> 00:46:24.630 In Europe, there were various rules 00:46:24.630 --> 00:46:29.220 that became known as MiFID, which now I can't remember 00:46:29.220 --> 00:46:30.540 what it all stands for. 00:46:30.540 --> 00:46:33.360 But electronic trading platforms were regulated a little 00:46:33.360 --> 00:46:34.317 differently. 00:46:34.317 --> 00:46:36.150 So I hope that gives you a sense and puts it 00:46:36.150 --> 00:46:38.010 in a historic concept. 00:46:38.010 --> 00:46:40.920 I think over the 2020s, AI and machine 00:46:40.920 --> 00:46:44.640 learning will lead to tremendous challenges 00:46:44.640 --> 00:46:50.940 around regulation, about if you see a pattern in the data 00:46:50.940 --> 00:46:54.360 but you can't explain why the pattern is there, 00:46:54.360 --> 00:46:57.360 you fall into some challenges of explainability. 00:46:57.360 --> 00:47:00.780 And for the last 50 years, or in many countries, 00:47:00.780 --> 00:47:02.520 if you deny somebody credit, you're 00:47:02.520 --> 00:47:06.240 supposed to be able to explain why you deny them credit. 00:47:06.240 --> 00:47:08.280 We talked about privacy earlier. 00:47:08.280 --> 00:47:11.060 It bumps up against privacy issues. 00:47:11.060 --> 00:47:14.380 And a third area it bumps up against is biases. 00:47:14.380 --> 00:47:16.050 What if there is a bias in the data, 00:47:16.050 --> 00:47:19.110 like when Apple Credit Card just rolled out 00:47:19.110 --> 00:47:22.980 and it seemed like husbands were getting more credit than wives 00:47:22.980 --> 00:47:25.730 in the same household? 00:47:25.730 --> 00:47:28.990 So biases, privacy, explainability 00:47:28.990 --> 00:47:32.260 are the three sort of cutting edge, when I call the big three 00:47:32.260 --> 00:47:36.790 public policy issues around AI and finance, though there 00:47:36.790 --> 00:47:38.420 are other issues as well. 00:47:38.420 --> 00:47:39.855 Romain, did I see you-- were you-- 00:47:39.855 --> 00:47:41.230 ROMAIN DE SAINT PERIER: Yes, sir. 00:47:41.230 --> 00:47:43.335 We have Carlos, who has his hand up. 00:47:43.335 --> 00:47:44.710 GARY GENSLER: All right, and then 00:47:44.710 --> 00:47:46.180 I'm going to keep going, because I 00:47:46.180 --> 00:47:49.540 want to talk about where we're going in this class as well. 00:47:49.540 --> 00:47:50.580 Carlos? 00:47:50.580 --> 00:47:52.270 AUDIENCE: Hi, how are you? 00:47:52.270 --> 00:47:55.330 Just a comment on regulations, sort of to build up on that. 00:47:55.330 --> 00:47:57.490 So I think ironically, for example, 00:47:57.490 --> 00:47:59.950 in Latin America, a lot of countries 00:47:59.950 --> 00:48:01.750 have an issue where the big banks have 00:48:01.750 --> 00:48:03.907 a massive concentration of deposits. 00:48:03.907 --> 00:48:06.490 But, for example, if you look at the Mexico FinTech law, which 00:48:06.490 --> 00:48:09.640 was passed end of 2018, it actually raised the barriers 00:48:09.640 --> 00:48:11.320 to entry for other FinTechs. 00:48:11.320 --> 00:48:12.808 So sort of ironically-- 00:48:12.808 --> 00:48:15.350 GARY GENSLER: I'm sorry, Carlos, it raised what for FinTechs? 00:48:15.350 --> 00:48:16.550 AUDIENCE: The barriers to entry. 00:48:16.550 --> 00:48:18.050 GARY GENSLER: Barriers to entry, OK. 00:48:18.050 --> 00:48:19.030 AUDIENCE: And so-- 00:48:19.030 --> 00:48:22.030 GARY GENSLER: I think our faculty member, Luis Videgaray, 00:48:22.030 --> 00:48:25.360 who helped work on that when he was finance minister of Mexico, 00:48:25.360 --> 00:48:27.430 we should ask him. 00:48:27.430 --> 00:48:30.010 And I'll see if I can get his answer for the next Wednesday 00:48:30.010 --> 00:48:31.180 or next Monday's class. 00:48:31.180 --> 00:48:32.570 But keep going. 00:48:32.570 --> 00:48:34.330 AUDIENCE: OK, that would be great. 00:48:34.330 --> 00:48:36.080 But the question is do you think there 00:48:36.080 --> 00:48:38.740 is a risk that new regulations in the FinTech scope 00:48:38.740 --> 00:48:41.080 are actually going to perpetuate some of the problems 00:48:41.080 --> 00:48:44.710 that we saw before with the more traditional banking sector? 00:48:44.710 --> 00:48:46.400 GARY GENSLER: Great question. 00:48:46.400 --> 00:48:47.980 Carlos, can I hold that for a minute, 00:48:47.980 --> 00:48:50.140 because I'm going to do that when I do the actors? 00:48:50.140 --> 00:48:54.540 But I think yes, incumbents in every field-- 00:48:54.540 --> 00:48:57.330 and these would be incumbents in the pharmaceutical field, 00:48:57.330 --> 00:49:01.470 in the tech fields, in airlines, whatever-- incumbents 00:49:01.470 --> 00:49:07.050 tend to be able to deal with regulation a little easier. 00:49:07.050 --> 00:49:08.700 They're big. 00:49:08.700 --> 00:49:10.200 They've got great resources. 00:49:10.200 --> 00:49:14.880 They can build systems to comply with those regulations. 00:49:14.880 --> 00:49:17.880 Now, they don't necessarily embrace new regulation. 00:49:17.880 --> 00:49:20.310 But once those regulations are put in place 00:49:20.310 --> 00:49:22.770 by an official sector, they tend to have 00:49:22.770 --> 00:49:24.810 the resources to embrace them. 00:49:24.810 --> 00:49:27.510 And startups sometimes have more challenges. 00:49:27.510 --> 00:49:30.060 And thus, you may be seeing that regulations 00:49:30.060 --> 00:49:31.820 become a barrier to entry. 00:49:31.820 --> 00:49:36.150 They're kind of grains in the sand of innovation at times. 00:49:36.150 --> 00:49:38.310 So there's always a public policy tradeoff-- 00:49:38.310 --> 00:49:40.800 protecting the public, whether it's 00:49:40.800 --> 00:49:46.620 protecting the public against consumer fraud or investor 00:49:46.620 --> 00:49:50.390 protection or protecting the public against systemic risk, 00:49:50.390 --> 00:49:53.970 that big banks will fail and hurt the rest of the economy, 00:49:53.970 --> 00:49:57.420 also comes with some tradeoffs, that it could 00:49:57.420 --> 00:49:59.250 raise the barriers to entry. 00:49:59.250 --> 00:50:02.625 You're absolutely right there. 00:50:02.625 --> 00:50:05.610 We're going to talk a lot about OpenAPI and open banking. 00:50:05.610 --> 00:50:08.040 We have a whole class on that. 00:50:08.040 --> 00:50:10.980 So I'm going to keep moving on just so that we finish 00:50:10.980 --> 00:50:13.230 by our 10 o'clock deadline. 00:50:13.230 --> 00:50:16.110 Blockchain technology-- you've heard about it. 00:50:16.110 --> 00:50:17.640 We're going to have a class on this, 00:50:17.640 --> 00:50:20.040 about cryptocurrencies and blockchains. 00:50:20.040 --> 00:50:24.160 Some of you actually were in our fall blockchain and money 00:50:24.160 --> 00:50:24.660 class. 00:50:24.660 --> 00:50:27.360 Some of you are in the crypto finance class that 00:50:27.360 --> 00:50:30.170 starts in about 30 minutes. 00:50:30.170 --> 00:50:32.580 But we will talk about blockchain technology. 00:50:32.580 --> 00:50:34.650 I want to say and lay it out right 00:50:34.650 --> 00:50:38.250 from the beginning, these two issues, AI 00:50:38.250 --> 00:50:42.420 and machine learning, in these eight areas are dramatically 00:50:42.420 --> 00:50:44.040 more relevant. 00:50:44.040 --> 00:50:48.360 And OpenAPI and open banking, dramatically more relevant 00:50:48.360 --> 00:50:53.340 than blockchain technology potential use cases as of 2020. 00:50:53.340 --> 00:50:56.820 The interesting question is will that shift? 00:50:56.820 --> 00:51:01.350 Is there an overabundance of investment in AI and machine 00:51:01.350 --> 00:51:03.940 learning and not enough in blockchain technology? 00:51:03.940 --> 00:51:06.180 You get to decide for yourselves. 00:51:06.180 --> 00:51:11.610 But I'm saying as of 2020, sort of the real potential 00:51:11.610 --> 00:51:14.790 that we're seeing and the dramatic changes around user 00:51:14.790 --> 00:51:19.320 interface an OpenAPI and machine learning and natural language 00:51:19.320 --> 00:51:22.690 processing are more dramatic in this space. 00:51:22.690 --> 00:51:24.630 And yet cryptocurrencies have dramatically 00:51:24.630 --> 00:51:26.830 changed what central banks are doing. 00:51:26.830 --> 00:51:29.970 And we saw Facebook trying to stand up a worldwide currency 00:51:29.970 --> 00:51:31.150 last year. 00:51:31.150 --> 00:51:33.060 So I don't think you can adequately 00:51:33.060 --> 00:51:35.910 discuss and have a course on financial technology 00:51:35.910 --> 00:51:40.680 without really having some slice of blockchain technology. 00:51:40.680 --> 00:51:41.760 And it is shifting. 00:51:41.760 --> 00:51:45.820 Everything that's on this list, it is a catalyst for change. 00:51:45.820 --> 00:51:49.080 I would say my takeaway on blockchain technology, 00:51:49.080 --> 00:51:52.620 it is definitely pushing the financial sector 00:51:52.620 --> 00:51:57.580 in places that wouldn't be pushed otherwise. 00:51:57.580 --> 00:51:59.580 I guess that's really saying OpenAPI 00:51:59.580 --> 00:52:01.780 and artificial intelligence, machine learning 00:52:01.780 --> 00:52:07.353 are so much bigger in terms of what's happening in 2020. 00:52:07.353 --> 00:52:08.770 Romain, unless there's a question, 00:52:08.770 --> 00:52:10.572 I'm going to do the actors quickly. 00:52:10.572 --> 00:52:11.072 Anything? 00:52:11.072 --> 00:52:13.280 ROMAIN DE SAINT PERIER: We have one specific question 00:52:13.280 --> 00:52:16.930 from [INAUDIBLE] on whether machine learning and AI can 00:52:16.930 --> 00:52:20.710 cause a dark box, or creditors could deny and approve credits 00:52:20.710 --> 00:52:22.115 based on unreasonable grounds. 00:52:22.115 --> 00:52:22.948 How do you see that? 00:52:22.948 --> 00:52:26.830 GARY GENSLER: That absolutely is a risk. 00:52:26.830 --> 00:52:30.130 Our first big data revolution in the US, 00:52:30.130 --> 00:52:32.310 and then it was about a decade later elsewhere, 00:52:32.310 --> 00:52:35.410 was in the late '50s and early '60s, 00:52:35.410 --> 00:52:38.810 credit cards came along, invented really 00:52:38.810 --> 00:52:44.330 in the late 1940s and then popularized by the mid 1960s, 00:52:44.330 --> 00:52:44.990 came along. 00:52:44.990 --> 00:52:47.090 And then the official sector passed laws. 00:52:47.090 --> 00:52:50.570 And two of the first laws we passed in the US 00:52:50.570 --> 00:52:52.700 was something called the Fair Credit Reporting 00:52:52.700 --> 00:52:55.070 Act and the Equal Credit Opportunity Act. 00:52:55.070 --> 00:52:58.010 And why I speak about those two and this question 00:52:58.010 --> 00:53:00.560 50 years later is this was a question 00:53:00.560 --> 00:53:03.930 with the first big data analytics at that time. 00:53:03.930 --> 00:53:07.070 And the idea was you couldn't-- you couldn't use data analytics 00:53:07.070 --> 00:53:11.240 to deny somebody credit because of their race, 00:53:11.240 --> 00:53:13.580 because of their color, because their ethnicity, 00:53:13.580 --> 00:53:18.812 because of their gender and other protected attributes. 00:53:18.812 --> 00:53:21.020 And that was called the Equal Credit Opportunity Act. 00:53:21.020 --> 00:53:24.320 That same act is important now as we move into machine 00:53:24.320 --> 00:53:27.230 learning credit decisions. 00:53:27.230 --> 00:53:30.260 Fair Credit Reporting Act also said in the US, 00:53:30.260 --> 00:53:33.230 and Europe did some similar things elsewhere later, 00:53:33.230 --> 00:53:37.810 said if you deny credit, you have to be able to explain why. 00:53:37.810 --> 00:53:42.000 So to this question, just because you have a black box, 00:53:42.000 --> 00:53:46.890 you still need to have those basic tenets of explainability 00:53:46.890 --> 00:53:49.890 and fairness or lack of bias. 00:53:49.890 --> 00:53:52.470 And that's why I say the three big challenges are 00:53:52.470 --> 00:53:56.350 explainability, bias, and then privacy. 00:53:56.350 --> 00:53:59.605 There's also challenges of robustness and so forth, 00:53:59.605 --> 00:54:02.280 but great question. 00:54:02.280 --> 00:54:04.590 The actors-- I think of the actors-- 00:54:04.590 --> 00:54:09.180 we've talked about this a little bit in several buckets. 00:54:09.180 --> 00:54:11.460 I think of big finance-- 00:54:11.460 --> 00:54:13.890 I apologize, I sort of borrowed this a little bit 00:54:13.890 --> 00:54:15.950 from the central bank governor of Brazil. 00:54:15.950 --> 00:54:17.670 I met with him last year. 00:54:17.670 --> 00:54:20.220 And he and I were talking about Brazilian banking. 00:54:20.220 --> 00:54:22.170 And he said they're like fortresses. 00:54:22.170 --> 00:54:26.680 So this was actually his kind of articulation of Itaú 00:54:26.680 --> 00:54:28.185 and the others in Brazil. 00:54:28.185 --> 00:54:30.060 And I said how do you see them as fortresses? 00:54:30.060 --> 00:54:33.780 And he says they all have their moats, towers, 00:54:33.780 --> 00:54:35.930 and they have sovereign protection. 00:54:35.930 --> 00:54:39.420 And to him-- and I liked it so much I repeat it-- 00:54:39.420 --> 00:54:42.390 their towers, their sort of basic tenets 00:54:42.390 --> 00:54:46.320 of sort of financial strength is around payments. 00:54:46.320 --> 00:54:48.310 They usually control payments. 00:54:48.310 --> 00:54:50.820 They have big balance sheets that they can use. 00:54:50.820 --> 00:54:55.860 And balance sheets allow you to lower your risk, frankly, 00:54:55.860 --> 00:54:56.910 and leverage. 00:54:56.910 --> 00:54:58.380 They have a lot of data. 00:54:58.380 --> 00:55:00.430 And yes, they have hundreds of legal entities. 00:55:00.430 --> 00:55:05.220 Their corporate structure is one of their both complexities 00:55:05.220 --> 00:55:06.810 but benefits. 00:55:06.810 --> 00:55:09.780 A company like a JPMorgan or a Goldman Sachs 00:55:09.780 --> 00:55:14.040 or Barclays at a minimum has probably 1,000 legal entities 00:55:14.040 --> 00:55:16.260 and might have 3,000 or 5,000 legal entities. 00:55:16.260 --> 00:55:19.380 When I left Goldman Sachs, which was already 22 years ago, 00:55:19.380 --> 00:55:23.310 I was the co-head of finance with David Viniar, who 00:55:23.310 --> 00:55:28.380 went on to be the CFO, we had 700 legal entities. 00:55:28.380 --> 00:55:31.710 But if I recall, in that quarter of a trillion balance sheet 00:55:31.710 --> 00:55:34.140 that we had to sort of help manage and fund, 00:55:34.140 --> 00:55:38.040 only about 70 or 80 of those were regulated companies. 00:55:38.040 --> 00:55:39.840 So we had a lot-- 00:55:39.840 --> 00:55:42.190 we did everything legal, I just want to say-- 00:55:42.190 --> 00:55:45.570 but we had a lot going on amongst those 700 00:55:45.570 --> 00:55:46.920 legal entities. 00:55:46.920 --> 00:55:50.970 That's kind of big finance from a sort of central bank 00:55:50.970 --> 00:55:54.150 governor of Brazil's point of view, like fortresses. 00:55:54.150 --> 00:55:56.420 But then big tech-- 00:55:56.420 --> 00:55:57.980 the Bank of International Settlements 00:55:57.980 --> 00:55:59.740 did a wonderful report last year. 00:55:59.740 --> 00:56:01.680 And they said it's like DNA-- 00:56:01.680 --> 00:56:04.700 data, networks, activities. 00:56:04.700 --> 00:56:09.920 And if you're Alibaba or you're Facebook, 00:56:09.920 --> 00:56:13.580 you want to layer another activity on your network. 00:56:13.580 --> 00:56:15.860 Facebook already has 2 plus billion people 00:56:15.860 --> 00:56:17.130 in their network. 00:56:17.130 --> 00:56:19.430 They have a lot of data already. 00:56:19.430 --> 00:56:22.530 They are supposedly a free app. 00:56:22.530 --> 00:56:25.110 They are free if you download it. 00:56:25.110 --> 00:56:27.200 But it's data for services. 00:56:27.200 --> 00:56:29.580 And if they can put another activity on top of it, 00:56:29.580 --> 00:56:32.190 that means they get more data. 00:56:32.190 --> 00:56:35.210 So every time they add an activity, more data. 00:56:35.210 --> 00:56:38.010 And data they can commercialize. 00:56:38.010 --> 00:56:42.870 And so that DNA network is why you see big tech trying 00:56:42.870 --> 00:56:45.240 to get in payments around the globe, 00:56:45.240 --> 00:56:48.790 and then adding credit products on top of it. 00:56:48.790 --> 00:56:51.360 Startups-- startups have advantages. 00:56:51.360 --> 00:56:53.250 Don't count them out. 00:56:53.250 --> 00:56:55.290 Some people would just call that the FinTech. 00:56:55.290 --> 00:56:56.790 But they're flexible. 00:56:56.790 --> 00:56:58.530 They're disruptive innovators. 00:56:58.530 --> 00:57:02.670 They can sort of rent their data storage on the cloud. 00:57:02.670 --> 00:57:05.250 In some circumstances, they can rent their balance sheets 00:57:05.250 --> 00:57:07.530 by doing securitizations. 00:57:07.530 --> 00:57:10.240 They also have some asymmetric risks. 00:57:10.240 --> 00:57:13.930 And that asymmetric risk we'll talk about all half semester. 00:57:13.930 --> 00:57:18.390 The important asymmetries they have is one, 00:57:18.390 --> 00:57:21.100 they're not protecting a business model. 00:57:21.100 --> 00:57:23.100 So let's just talk about payments and credit 00:57:23.100 --> 00:57:25.000 cards for a minute. 00:57:25.000 --> 00:57:28.820 The big banks are protecting a very profitable credit card 00:57:28.820 --> 00:57:29.320 business. 00:57:29.320 --> 00:57:32.040 And there's seven big banks in the US. 00:57:32.040 --> 00:57:34.780 There's seven big actors in the credit card space-- 00:57:34.780 --> 00:57:38.230 Bank of America and Chase and Citi, of course, 00:57:38.230 --> 00:57:43.750 but also American Express and Discover and the like, Cap One. 00:57:43.750 --> 00:57:45.980 They're protecting that business model. 00:57:45.980 --> 00:57:47.950 But then somebody comes along. 00:57:47.950 --> 00:57:51.490 Maybe it's a small company like Toast in the payment system 00:57:51.490 --> 00:57:54.520 space for restaurant payments. 00:57:54.520 --> 00:57:57.580 And before corona crisis, Toast was doing pretty well. 00:57:57.580 --> 00:58:00.340 And they did a C or series C or D round 00:58:00.340 --> 00:58:03.100 at $4.9 billion valuation. 00:58:03.100 --> 00:58:05.980 Well, Toast can come along and provide a payment product 00:58:05.980 --> 00:58:09.250 for the restaurant business. 00:58:09.250 --> 00:58:11.860 They're not protecting any business model. 00:58:11.860 --> 00:58:14.170 Or even Lending Club that came along 10 00:58:14.170 --> 00:58:18.490 or 11 years ago can come into the personal loan space 00:58:18.490 --> 00:58:22.000 and say we're not protecting wider profit 00:58:22.000 --> 00:58:25.030 margins and interest rate margins in the credit card 00:58:25.030 --> 00:58:26.320 space. 00:58:26.320 --> 00:58:28.840 You can come into the personal lending space, 00:58:28.840 --> 00:58:31.660 which is growing dramatically. 00:58:31.660 --> 00:58:36.460 Personal lending in the US is about $170 billion asset class. 00:58:36.460 --> 00:58:38.890 Credit cards is $1 trillion. 00:58:38.890 --> 00:58:41.760 So it's only one sixth the size, but the personal loan space 00:58:41.760 --> 00:58:45.540 is growing rapidly, in part because those actors 00:58:45.540 --> 00:58:47.460 in the disruptive startup space are not 00:58:47.460 --> 00:58:51.040 protecting the trillion-dollar asset class, which 00:58:51.040 --> 00:58:53.700 is called credit cards. 00:58:53.700 --> 00:58:55.750 And then there's the official sector. 00:58:55.750 --> 00:58:59.520 So I think of these actors as, importantly, all of them. 00:58:59.520 --> 00:59:01.350 And just some pictures, just for fun-- 00:59:01.350 --> 00:59:03.550 we don't need to stop, but big finance. 00:59:03.550 --> 00:59:06.150 And, of course, I left companies out. 00:59:06.150 --> 00:59:08.580 But to give you a sense, it's an asset management, 00:59:08.580 --> 00:59:11.720 like BlackRock and Fidelity and Vanguard. 00:59:11.720 --> 00:59:13.170 It's in banking. 00:59:13.170 --> 00:59:14.460 It's in investment banking. 00:59:14.460 --> 00:59:15.660 It's global. 00:59:15.660 --> 00:59:18.780 If I left your country or your favorite company out, 00:59:18.780 --> 00:59:19.680 I apologize. 00:59:19.680 --> 00:59:23.460 But I could have put 200, 500 companies on this page. 00:59:23.460 --> 00:59:26.820 But then there's also big tech, which I only 00:59:26.820 --> 00:59:28.530 picked six or seven at the top. 00:59:28.530 --> 00:59:30.720 And then the startups-- and we're 00:59:30.720 --> 00:59:34.380 going to talk about startups in every one of our classes. 00:59:34.380 --> 00:59:37.050 But I think you've got to sort of bear with me 00:59:37.050 --> 00:59:41.750 and think about it more broadly as well. 00:59:41.750 --> 00:59:43.500 And then I'm just going to close before we 00:59:43.500 --> 00:59:47.990 talk about our actual course and so forth, is 00:59:47.990 --> 00:59:49.140 where's the investments? 00:59:49.140 --> 00:59:51.810 Accenture puts out this wonderful report, 00:59:51.810 --> 00:59:55.140 I think on a quarterly basis, as to the number 00:59:55.140 --> 00:59:59.200 of deals in different sectors and then the funding. 00:59:59.200 --> 01:00:01.920 And I don't ask you to study this on your screen now. 01:00:01.920 --> 01:00:02.760 But think about it. 01:00:02.760 --> 01:00:06.390 Maybe pull up the Accenture report itself. 01:00:06.390 --> 01:00:10.350 But the big bulk of it is in payments and credit. 01:00:10.350 --> 01:00:14.760 If you look at the kind of purplish blue boxes, nearly 50% 01:00:14.760 --> 01:00:17.280 of the funding is in those boxes. 01:00:17.280 --> 01:00:20.010 Insurance, pretty good size, too. 01:00:20.010 --> 01:00:24.270 But it gives you the sectors that actual funding is going on 01:00:24.270 --> 01:00:25.920 in this marketplace. 01:00:25.920 --> 01:00:28.662 Romain, questions? 01:00:28.662 --> 01:00:29.620 AUDIENCE: No questions. 01:00:29.620 --> 01:00:31.930 GARY GENSLER: My god, Romain, where did you 01:00:31.930 --> 01:00:34.630 get this picture taken anyway that I grabbed off 01:00:34.630 --> 01:00:36.645 the internet? 01:00:36.645 --> 01:00:38.770 ROMAIN DE SAINT PERIER: That was when I was working 01:00:38.770 --> 01:00:41.020 in the Middle East for BCG. 01:00:41.020 --> 01:00:42.970 GARY GENSLER: I see, I see. 01:00:42.970 --> 01:00:45.670 All right, so you've met Romain. 01:00:45.670 --> 01:00:46.650 You've met myself. 01:00:46.650 --> 01:00:49.630 Lena is the course administrator. 01:00:49.630 --> 01:00:52.060 If you want to set up office hours-- and, yes, 01:00:52.060 --> 01:00:54.760 I am committed to office hours-- 01:00:54.760 --> 01:00:56.890 it's great to also copy Lena. 01:00:56.890 --> 01:00:58.740 You can probably do it with me, too. 01:00:58.740 --> 01:01:05.320 But Lena is going to be better to sign it up as well. 01:01:05.320 --> 01:01:07.600 The course-- so after this intro, 01:01:07.600 --> 01:01:09.420 we're going to take the technologies. 01:01:09.420 --> 01:01:12.670 We're going to take two classes on artificial intelligence, 01:01:12.670 --> 01:01:14.980 machine learning, natural language processing 01:01:14.980 --> 01:01:19.000 and the like, and then talk about the customer interface 01:01:19.000 --> 01:01:22.270 on April 8 and then blockchain and technology. 01:01:22.270 --> 01:01:24.730 These three slices-- now, there are other slices 01:01:24.730 --> 01:01:27.050 we could address as well. 01:01:27.050 --> 01:01:29.290 So then we're going to go through the sectors. 01:01:29.290 --> 01:01:31.090 We're going to talk about these sectors. 01:01:31.090 --> 01:01:35.830 And what I could see is payment and credit, trading, a little 01:01:35.830 --> 01:01:37.570 less on the risk management. 01:01:37.570 --> 01:01:42.830 So maybe on May 4, we'll squeeze that down a little bit as well. 01:01:42.830 --> 01:01:44.470 And then, of course, we have the intro. 01:01:44.470 --> 01:01:47.530 I've just, [INAUDIBLE] and I, revised the syllabus 01:01:47.530 --> 01:01:48.700 this past week. 01:01:48.700 --> 01:01:51.430 I want to take the next-to-last class 01:01:51.430 --> 01:01:53.540 and just talk about corona crisis 01:01:53.540 --> 01:01:56.500 and how it might be shifting the landscape. 01:01:56.500 --> 01:01:59.340 I already said, I really do think this shifting, 01:01:59.340 --> 01:02:05.110 this deep trend towards online from bricks and mortar, 01:02:05.110 --> 01:02:07.190 that was already happening. 01:02:07.190 --> 01:02:09.910 But we've even seen in the last three weeks, 01:02:09.910 --> 01:02:11.900 we've seen winners and losers. 01:02:11.900 --> 01:02:14.110 I asked each of you to think about this. 01:02:14.110 --> 01:02:19.410 Who are the winners and losers within the financial sector, 01:02:19.410 --> 01:02:22.200 big tech, and the financial startups? 01:02:22.200 --> 01:02:23.670 I talked a little bit about Toast. 01:02:23.670 --> 01:02:27.120 Toast is a very successful Boston company 01:02:27.120 --> 01:02:29.340 that is providing payment services 01:02:29.340 --> 01:02:31.163 and credit to restaurants. 01:02:31.163 --> 01:02:33.330 For a moment, that would have to-- you'd have to say 01:02:33.330 --> 01:02:35.670 that's a company that's taking-- 01:02:35.670 --> 01:02:38.940 taking it on the chin, so to speak, not as badly 01:02:38.940 --> 01:02:41.520 as all those individuals that have health care worries, 01:02:41.520 --> 01:02:43.710 that are ending up in the hospitals and the families 01:02:43.710 --> 01:02:45.160 losing loved ones. 01:02:45.160 --> 01:02:47.940 But I'm saying from an economic perspective, 01:02:47.940 --> 01:02:49.470 there are some winners and losers. 01:02:49.470 --> 01:02:55.440 Robinhood, an online app, mobile app for trading, 01:02:55.440 --> 01:02:58.860 has crashed several times in the last three weeks. 01:02:58.860 --> 01:03:00.510 And there's some data out of Europe 01:03:00.510 --> 01:03:06.810 already that online FinTech apps as a sector have 01:03:06.810 --> 01:03:12.450 seen usage up 70% and 100%, and some apps up 700%. 01:03:12.450 --> 01:03:14.130 But not all will be winners. 01:03:14.130 --> 01:03:16.020 Not all will be losers. 01:03:16.020 --> 01:03:19.800 And so I thought we'll take one class towards the end 01:03:19.800 --> 01:03:23.850 and just discuss it and get your feelings and thoughts 01:03:23.850 --> 01:03:29.080 as well going forward how this might play out. 01:03:29.080 --> 01:03:32.130 So MIT chose this as pass-fail. 01:03:32.130 --> 01:03:36.060 So welcome to not only remote learning MIT, 01:03:36.060 --> 01:03:39.420 but technically pass emergency, no-credit emergency, 01:03:39.420 --> 01:03:41.100 incomplete emergency. 01:03:41.100 --> 01:03:44.940 Just so that you understand what this is, it's almost pass-fail. 01:03:44.940 --> 01:03:47.940 Pass emergency, PE, will be on your transcript, 01:03:47.940 --> 01:03:49.990 hopefully for all of you. 01:03:49.990 --> 01:03:52.530 I can't commit to it, because it's up to you 01:03:52.530 --> 01:03:54.060 whether you pass. 01:03:54.060 --> 01:03:56.160 There are assignments. 01:03:56.160 --> 01:04:00.660 And no credit emergency, you can think of is an F, 01:04:00.660 --> 01:04:03.700 but it's not going to be on your transcript. 01:04:03.700 --> 01:04:07.430 So this is an emergency circumstance right now. 01:04:07.430 --> 01:04:09.440 There's still assignments because we 01:04:09.440 --> 01:04:12.380 want to give you the most learning 01:04:12.380 --> 01:04:13.380 experience you can have. 01:04:13.380 --> 01:04:15.672 You might say wait a minute, wait a minute, I thought-- 01:04:15.672 --> 01:04:18.020 I thought assignments were just about so that a faculty 01:04:18.020 --> 01:04:22.370 member can decide who gets A's and who gets B's and the like. 01:04:22.370 --> 01:04:24.830 I look at assignments in a different way than that. 01:04:24.830 --> 01:04:27.080 I look at assignments really as a way 01:04:27.080 --> 01:04:29.710 to help you engage in this subject. 01:04:29.710 --> 01:04:31.040 And so in this class-- 01:04:31.040 --> 01:04:32.870 and those of you who know me, I do 01:04:32.870 --> 01:04:36.050 this in a couple of classes-- is one individual paper, one group 01:04:36.050 --> 01:04:36.750 paper. 01:04:36.750 --> 01:04:38.870 And it's all geared to writing a group 01:04:38.870 --> 01:04:43.560 paper for either a big incumbent, Bank of America, 01:04:43.560 --> 01:04:49.170 a big tech company, Jeff Bezos and Amazon, or kind 01:04:49.170 --> 01:04:53.870 of a startup company, a big VC company, Andreesen Horowitz, 01:04:53.870 --> 01:04:56.550 that you form groups and you decide on a sector. 01:04:56.550 --> 01:05:00.360 You can decide on credit or payment or trading. 01:05:00.360 --> 01:05:04.230 You choose, and it helps you engage in the subject. 01:05:04.230 --> 01:05:07.170 And then I ask you to split, if it's a three or four-person 01:05:07.170 --> 01:05:09.300 team, or even if it's a one-person team, 01:05:09.300 --> 01:05:14.100 if you choose to do that, because we're all so separated, 01:05:14.100 --> 01:05:18.030 then split up and write a three or four page paper, 01:05:18.030 --> 01:05:24.080 900 words or so, on one of the topics that I lay out here-- 01:05:24.080 --> 01:05:28.620 the traditional competitors, the startup competitors 01:05:28.620 --> 01:05:33.450 and so forth, the technology that you're interested in. 01:05:33.450 --> 01:05:36.810 Why do I still do assignments when it's PE NE? 01:05:36.810 --> 01:05:38.970 It's to help you engage in this subject. 01:05:38.970 --> 01:05:42.900 And Romain and I are committed, even if we slap a PE on most 01:05:42.900 --> 01:05:45.090 of these-- hopefully all of them-- 01:05:45.090 --> 01:05:46.890 we're trying to give you feedback 01:05:46.890 --> 01:05:48.390 so you engage in a subject. 01:05:48.390 --> 01:05:53.460 And yes, I'm willing to do Zoom group meetings, Zoom 01:05:53.460 --> 01:05:55.390 individual meetings. 01:05:55.390 --> 01:05:58.610 Look, this is not an easy time for any of us. 01:05:58.610 --> 01:06:01.410 But I want to make sure that I deliver as much as I can 01:06:01.410 --> 01:06:03.750 and that MIT continues to deliver 01:06:03.750 --> 01:06:08.460 for you as we're going through this sort of challenging time. 01:06:08.460 --> 01:06:11.800 Class participation is still important. 01:06:11.800 --> 01:06:15.330 If you can't sign up, if it's the time zone doesn't work 01:06:15.330 --> 01:06:18.740 or there's something going on in your family, it doesn't-- 01:06:18.740 --> 01:06:22.200 if you're interviewing for a job, God bless, 01:06:22.200 --> 01:06:23.970 then listen to the recording. 01:06:23.970 --> 01:06:29.470 We'll put the recordings up on Canvas as well. 01:06:29.470 --> 01:06:32.400 And so professionalism, I just want to say something. 01:06:32.400 --> 01:06:32.550 ROMAIN DE SAINT PERIER: Gary? 01:06:32.550 --> 01:06:33.240 GARY GENSLER: Yeah? 01:06:33.240 --> 01:06:35.698 ROMAIN DE SAINT PERIER: Excuse me, just on the assignments, 01:06:35.698 --> 01:06:37.470 I'm being asked whether listeners also 01:06:37.470 --> 01:06:39.210 have to comply with these assignments. 01:06:39.210 --> 01:06:40.933 GARY GENSLER: No. 01:06:40.933 --> 01:06:42.350 ROMAIN DE SAINT PERIER: Thank you. 01:06:45.278 --> 01:06:48.220 GARY GENSLER: I'm trying to get rid of the poll here. 01:06:48.220 --> 01:06:48.720 OK. 01:06:52.140 --> 01:06:55.230 I've never been asked that question. 01:06:55.230 --> 01:06:57.000 A little word on professionalism, 01:06:57.000 --> 01:07:01.680 just for my, God knows, 30 plus years in business. 01:07:01.680 --> 01:07:04.050 And this is just sort of my closing thing, 01:07:04.050 --> 01:07:08.160 is my advice for all of us is success 01:07:08.160 --> 01:07:11.890 goes for those prepared, curious, and self-starters. 01:07:11.890 --> 01:07:13.950 If you read the assignments for this, 01:07:13.950 --> 01:07:15.970 you'll do better in this class. 01:07:15.970 --> 01:07:18.720 But if you go into a meeting, if you go into a pitch, 01:07:18.720 --> 01:07:23.190 you go into an interview, if you read about the person, 01:07:23.190 --> 01:07:25.122 you're going to learn more about them. 01:07:25.122 --> 01:07:26.580 And, by the way, if you're curious, 01:07:26.580 --> 01:07:28.270 when you walk into somebody's office, 01:07:28.270 --> 01:07:34.190 whether it's a video office or a real office, look at the walls. 01:07:34.190 --> 01:07:36.440 What does it mean that Gary Gensler has 01:07:36.440 --> 01:07:38.570 this stuff behind him or not? 01:07:38.570 --> 01:07:41.360 I'm not asking you to analyze me right now. 01:07:41.360 --> 01:07:44.570 But I'm telling you, if you walk into somebody's office, 01:07:44.570 --> 01:07:48.020 whether they're a US senator, the president of the United 01:07:48.020 --> 01:07:52.220 States, some job interview, a colleague, and you look 01:07:52.220 --> 01:07:54.440 at their walls and you ask them about their families, 01:07:54.440 --> 01:07:55.640 show some curiosity. 01:07:55.640 --> 01:07:58.040 You'll do better off. 01:07:58.040 --> 01:08:01.460 As I said, respect and courtesy builds reputation and networks 01:08:01.460 --> 01:08:04.380 and so forth. 01:08:04.380 --> 01:08:05.250 Engage. 01:08:05.250 --> 01:08:08.580 It's going to be hard with this many students online, 01:08:08.580 --> 01:08:10.270 but engage in this class. 01:08:10.270 --> 01:08:12.600 You'll learn more if you engage with me 01:08:12.600 --> 01:08:19.140 also offline, Gensler@MIT.edu, but also engage 01:08:19.140 --> 01:08:21.840 by setting up office time. 01:08:21.840 --> 01:08:25.210 I also think understanding both strategy and detail matter. 01:08:25.210 --> 01:08:27.779 Some people are really good tactical people, 01:08:27.779 --> 01:08:30.500 really good detail folks. 01:08:30.500 --> 01:08:32.000 They'll do fine in their careers. 01:08:32.000 --> 01:08:34.609 But if you step back and understand the trends as well, 01:08:34.609 --> 01:08:35.850 you'll do better. 01:08:35.850 --> 01:08:37.760 Some people are really global strategists 01:08:37.760 --> 01:08:39.500 and not really good at the details. 01:08:39.500 --> 01:08:41.270 They'll probably do OK. 01:08:41.270 --> 01:08:42.870 But I'll tell you, my experience, 01:08:42.870 --> 01:08:46.430 whether it's in finance, whether it's watching people 01:08:46.430 --> 01:08:51.040 at MIT and the faculty, or my time in public service, 01:08:51.040 --> 01:08:54.319 the people that can merge both broad strategy 01:08:54.319 --> 01:08:56.870 and can execute on the details, those folks 01:08:56.870 --> 01:08:58.538 are unstoppable often. 01:08:58.538 --> 01:09:00.080 Those are folks that you really-- you 01:09:00.080 --> 01:09:03.770 want them on your team, that they can do a bit of both. 01:09:03.770 --> 01:09:06.859 So we're going to talk a lot about strategy and the trends. 01:09:06.859 --> 01:09:10.231 But we're going to get into the granular details as well. 01:09:10.231 --> 01:09:11.689 And then lastly, it's always better 01:09:11.689 --> 01:09:13.520 to stay true to your values. 01:09:13.520 --> 01:09:15.350 Now, I do say this-- 01:09:15.350 --> 01:09:16.630 it's going to be a little-- 01:09:16.630 --> 01:09:18.130 we're in this pass-fail thing. 01:09:18.130 --> 01:09:21.320 But if you want to know one way that Romain and I, 01:09:21.320 --> 01:09:23.569 it will drive us a little nutty, we're 01:09:23.569 --> 01:09:26.660 going to try to put your papers through some plagiarism 01:09:26.660 --> 01:09:27.680 software. 01:09:27.680 --> 01:09:29.420 I use Grammarly. 01:09:29.420 --> 01:09:32.750 I actually check, yes. 01:09:32.750 --> 01:09:36.859 And if you have one like eight or ten-word section 01:09:36.859 --> 01:09:38.660 that you pulled off of Wikipedia, 01:09:38.660 --> 01:09:42.410 and I've had students grab the first 15 words off of Wikipedia 01:09:42.410 --> 01:09:45.080 and put it right in their paper, that's 01:09:45.080 --> 01:09:46.880 kind of a sloppy thing to do. 01:09:46.880 --> 01:09:49.279 And usually, if I was grading, that paper 01:09:49.279 --> 01:09:52.279 would get like a C or a D and it wouldn't get an A or a B. 01:09:52.279 --> 01:09:56.390 And I'm not going to be a hard-nosed guy about 10 words. 01:09:56.390 --> 01:09:59.870 But if you extensively plagiarize a couple hundred 01:09:59.870 --> 01:10:02.930 words in a 900-word paper, you're 01:10:02.930 --> 01:10:05.300 going to get an F on the paper. 01:10:05.300 --> 01:10:07.370 Now, you can recover on the group paper. 01:10:07.370 --> 01:10:09.417 But don't plagiarize on your group paper 01:10:09.417 --> 01:10:11.750 because you're also going to bring down your colleagues. 01:10:11.750 --> 01:10:15.080 I don't know in pass-fail land what I will do. 01:10:15.080 --> 01:10:18.770 But if somebody really is trying to test the limit, 01:10:18.770 --> 01:10:22.100 you would test it by extensive plagiarism. 01:10:22.100 --> 01:10:22.820 Enough on that. 01:10:22.820 --> 01:10:26.330 I've had it in the past occasionally. 01:10:26.330 --> 01:10:30.380 So I got to say it. 01:10:30.380 --> 01:10:32.750 I would say speak up in class if you can. 01:10:32.750 --> 01:10:34.940 Keep your videos on, as most of you have. 01:10:34.940 --> 01:10:36.770 Keep your audios muted. 01:10:36.770 --> 01:10:38.360 But please speak up. 01:10:38.360 --> 01:10:44.440 I mean, don't be hesitant at all. 01:10:44.440 --> 01:10:47.050 And then I do have office hours. 01:10:47.050 --> 01:10:50.800 And the crazy thing was I set up office lunches. 01:10:50.800 --> 01:10:54.860 So on those following Tuesdays and Thursdays, I had-- 01:10:54.860 --> 01:10:56.590 there's a spreadsheet. 01:10:56.590 --> 01:11:00.160 Romain, maybe we should send it around to this group. 01:11:00.160 --> 01:11:03.520 There's a Google Spreadsheet if you wanted to sign up. 01:11:03.520 --> 01:11:08.390 And right before we left, before SIP week, a student said hey, 01:11:08.390 --> 01:11:10.430 I'm signed up for the 31. 01:11:10.430 --> 01:11:12.020 Will you still do the lunches? 01:11:12.020 --> 01:11:12.770 I laughed. 01:11:12.770 --> 01:11:15.870 I said listen, I'm willing to do it. 01:11:15.870 --> 01:11:17.750 I'm willing to do remote lunches. 01:11:17.750 --> 01:11:20.480 It's crazy. 01:11:20.480 --> 01:11:23.540 So if you want it-- if nobody signs up, I'm fine. 01:11:23.540 --> 01:11:25.310 I'll go for a run. 01:11:25.310 --> 01:11:28.190 I live in Baltimore and I'm still able to run here. 01:11:28.190 --> 01:11:30.510 They might shut that down, too, at some point in time. 01:11:30.510 --> 01:11:33.210 But for now, I'm able to do my runs. 01:11:33.210 --> 01:11:36.410 So I think that's it on the slides. 01:11:36.410 --> 01:11:42.570 And then I think I'm supposed to finish this class right now. 01:11:42.570 --> 01:11:43.288 I went over. 01:11:43.288 --> 01:11:44.330 I want to thank you all-- 01:11:44.330 --> 01:11:45.460 ROMAIN DE SAINT PERIER: Maybe just one-- 01:11:45.460 --> 01:11:46.470 GARY GENSLER: What's that? 01:11:46.470 --> 01:11:47.580 ROMAIN DE SAINT PERIER: Just one clarification, 01:11:47.580 --> 01:11:49.230 because I'm getting a lot of questions on the group 01:11:49.230 --> 01:11:49.860 formation. 01:11:49.860 --> 01:11:52.940 So groups will be from three to four students, 01:11:52.940 --> 01:11:55.650 and you are supposed to group yourselves, 01:11:55.650 --> 01:11:57.255 right, through the Canvas function. 01:11:57.255 --> 01:11:58.630 If that doesn't work out for you, 01:11:58.630 --> 01:12:00.210 we're going to send out a link where 01:12:00.210 --> 01:12:02.700 you can find other team members who also 01:12:02.700 --> 01:12:04.390 are looking for team members. 01:12:04.390 --> 01:12:06.533 So do not worry about the group formation. 01:12:06.533 --> 01:12:07.950 GARY GENSLER: And I thank you all. 01:12:07.950 --> 01:12:09.960 I know this is unusual. 01:12:09.960 --> 01:12:13.690 I see you on Wednesday morning, 8:30 AM. 01:12:13.690 --> 01:12:17.480 And please stay safe and be well.