1 00:00:00,500 --> 00:00:02,455 [SQUEAKING] 2 00:00:02,455 --> 00:00:04,419 [RUSTLING] 3 00:00:04,419 --> 00:00:05,401 [CLICKING] 4 00:00:10,630 --> 00:00:12,280 GARY GENSLER: So this FinTech course-- 5 00:00:12,280 --> 00:00:14,740 this is for those who want to explore FinTech, 6 00:00:14,740 --> 00:00:17,710 how the technologies are disrupting financial services. 7 00:00:17,710 --> 00:00:20,440 That's the core of it. 8 00:00:20,440 --> 00:00:22,660 Technology is disrupting finance. 9 00:00:22,660 --> 00:00:25,640 And we'll talk a lot about this, that finance and technology 10 00:00:25,640 --> 00:00:28,900 have lived in simpatico, in some relationship 11 00:00:28,900 --> 00:00:30,800 together for thousands of years. 12 00:00:30,800 --> 00:00:37,090 In fact, money and ledgers were initial financial technologies. 13 00:00:37,090 --> 00:00:39,570 And we'll talk about what makes something 14 00:00:39,570 --> 00:00:44,200 in this faculty's mind a financial technology that's 15 00:00:44,200 --> 00:00:46,720 really changing the world and then just 16 00:00:46,720 --> 00:00:48,250 the technology that exists. 17 00:00:48,250 --> 00:00:50,380 The telephone, for instance, at one point, 18 00:00:50,380 --> 00:00:54,340 in that time, in the 1920s, was, in essence, 19 00:00:54,340 --> 00:00:57,040 a financial technology that rapidly 20 00:00:57,040 --> 00:00:59,860 changed the world of finance. 21 00:00:59,860 --> 00:01:03,250 Or even in the 19th century, the telegraph rapidly 22 00:01:03,250 --> 00:01:06,830 changed parts of finance, when you could send your first money 23 00:01:06,830 --> 00:01:07,653 gram-- 24 00:01:07,653 --> 00:01:09,820 or in those days, it was called something different, 25 00:01:09,820 --> 00:01:12,520 but it was a telegram attached with money 26 00:01:12,520 --> 00:01:15,490 in the 1870s and 1880s. 27 00:01:15,490 --> 00:01:17,813 But this course is going to be about the cutting edge. 28 00:01:17,813 --> 00:01:19,480 We're going to be talking about business 29 00:01:19,480 --> 00:01:23,020 models and the like around AI, deep learning, 30 00:01:23,020 --> 00:01:26,260 blockchain technology, OpenAPI. 31 00:01:26,260 --> 00:01:29,110 10 and 20 years from now, OpenAPI 32 00:01:29,110 --> 00:01:33,260 will not be taught, by my view, in a FinTech course. 33 00:01:33,260 --> 00:01:36,070 But it's the relevant topics of the day. 34 00:01:36,070 --> 00:01:38,950 And we'll be looking at the competitive landscape. 35 00:01:38,950 --> 00:01:45,380 Those of you that have decided to take this course on top 36 00:01:45,380 --> 00:01:48,500 of consumer finance course, the half-semester course that 37 00:01:48,500 --> 00:01:51,170 was at the same time, you will know 38 00:01:51,170 --> 00:01:54,990 that I usually teach in the concept of business strategy. 39 00:01:54,990 --> 00:01:59,480 What is the strategy that these startups which big tech, which 40 00:01:59,480 --> 00:02:03,650 incumbents are looking at this point in time, in this day 41 00:02:03,650 --> 00:02:05,630 and age, in this sector? 42 00:02:05,630 --> 00:02:09,199 And this course is also, I should say, being recorded. 43 00:02:09,199 --> 00:02:10,960 It's being recorded for some students 44 00:02:10,960 --> 00:02:13,940 who can't join us simultaneously or what's 45 00:02:13,940 --> 00:02:15,860 called synchronous learning. 46 00:02:15,860 --> 00:02:19,370 And these recordings will be posted on Canvas 47 00:02:19,370 --> 00:02:21,110 within a day or two. 48 00:02:21,110 --> 00:02:23,390 Lena and Romain and I just have to remember 49 00:02:23,390 --> 00:02:26,720 how to do that and actually post each of the recordings. 50 00:02:26,720 --> 00:02:30,140 They also might be shared, just to alert you, 51 00:02:30,140 --> 00:02:33,110 in OpenCourseWare in the fall or later. 52 00:02:33,110 --> 00:02:36,470 I've chose with MIT that if we're recording them anyway, 53 00:02:36,470 --> 00:02:42,100 maybe if they come out being anywhere valuable, 54 00:02:42,100 --> 00:02:44,470 that we would up to the broader community. 55 00:02:44,470 --> 00:02:50,340 So these might be shared more broadly come the fall as well. 56 00:02:50,340 --> 00:02:53,210 It's also really to gain critical reasoning skills 57 00:02:53,210 --> 00:02:55,790 around the ground truths of FinTech, 58 00:02:55,790 --> 00:03:01,370 separating hype from reality. 59 00:03:01,370 --> 00:03:06,890 Every week, there's a posting of three or four readings. 60 00:03:06,890 --> 00:03:09,530 I understand that even if we were all on campus, 61 00:03:09,530 --> 00:03:11,570 you might not read every word of that. 62 00:03:11,570 --> 00:03:15,260 But they really are sort of the foundation. 63 00:03:15,260 --> 00:03:17,550 And I hope that in each lecture, in each class, 64 00:03:17,550 --> 00:03:19,730 we can go beyond that. 65 00:03:19,730 --> 00:03:22,250 But this week, the Bank of International Settlement 66 00:03:22,250 --> 00:03:25,190 Working Paper and the Financial Stability Board 67 00:03:25,190 --> 00:03:28,670 are two papers that a lot of people turn to. 68 00:03:28,670 --> 00:03:31,220 The Financial Stability Board is a group 69 00:03:31,220 --> 00:03:35,420 of 20 countries, the G20 countries, that 70 00:03:35,420 --> 00:03:40,340 have banded together and their treasury secretaries or finance 71 00:03:40,340 --> 00:03:44,300 ministers and central banks and securities regulators 72 00:03:44,300 --> 00:03:47,180 have formed this thing called the Financial Stability Board. 73 00:03:47,180 --> 00:03:48,710 And they publish very good work. 74 00:03:48,710 --> 00:03:51,590 This paper came out in 2017. 75 00:03:51,590 --> 00:03:53,490 It feels a little dated right now. 76 00:03:53,490 --> 00:03:55,820 But it still felt quite relevant. 77 00:03:55,820 --> 00:03:58,220 And then, of course, the Bank of international Settlement 78 00:03:58,220 --> 00:04:02,240 is 60 or 70 central banks out of Basel. 79 00:04:02,240 --> 00:04:04,492 And they write very good work. 80 00:04:04,492 --> 00:04:06,200 I thought it was also interesting to take 81 00:04:06,200 --> 00:04:10,730 the current chair of the US bank regulator, the Federal Deposit 82 00:04:10,730 --> 00:04:16,519 Insurance Corporation, Chair McWilliams, and her view 83 00:04:16,519 --> 00:04:19,730 as to the future of banking, what's going on right now. 84 00:04:19,730 --> 00:04:23,150 So that's why I grabbed these three as an intro. 85 00:04:23,150 --> 00:04:24,920 If you've not yet read them, I think 86 00:04:24,920 --> 00:04:27,350 go back, try to at least skim them, 87 00:04:27,350 --> 00:04:31,520 get your sense of what they are. 88 00:04:31,520 --> 00:04:34,510 And each class, I will also list study questions. 89 00:04:34,510 --> 00:04:36,890 And the goal of my listing study questions 90 00:04:36,890 --> 00:04:39,710 is not just for you to think about these questions 91 00:04:39,710 --> 00:04:41,960 beforehand, but you will also see where 92 00:04:41,960 --> 00:04:43,550 do I want to land the class? 93 00:04:43,550 --> 00:04:48,660 Where do I think these are the central learning objectives? 94 00:04:48,660 --> 00:04:51,560 And this is usually in a classroom setting, 95 00:04:51,560 --> 00:04:53,930 where I'll say "let's pause here and I'll 96 00:04:53,930 --> 00:04:56,180 engage in some conversation." 97 00:04:56,180 --> 00:04:59,180 Now, I do cold call in the regular classroom. 98 00:04:59,180 --> 00:05:02,720 I don't know if anybody wants to raise their hand now and answer 99 00:05:02,720 --> 00:05:04,910 any one of these questions, but it 100 00:05:04,910 --> 00:05:08,390 would be great to get a little bit of life and community 101 00:05:08,390 --> 00:05:11,060 in this, if anybody can address themselves. 102 00:05:11,060 --> 00:05:15,200 What are the major technological trends materially influencing 103 00:05:15,200 --> 00:05:16,910 finance right now that you think about, 104 00:05:16,910 --> 00:05:21,710 whether it's in the US or anywhere around the globe? 105 00:05:21,710 --> 00:05:24,260 And this course will be taught from the perspective 106 00:05:24,260 --> 00:05:25,760 around the globe, even though I'm 107 00:05:25,760 --> 00:05:27,140 more knowledgeable in the US. 108 00:05:27,140 --> 00:05:31,980 We will be talking about Europe, Latin America, Asia throughout, 109 00:05:31,980 --> 00:05:35,960 a little bit about Africa as well. 110 00:05:35,960 --> 00:05:38,323 Romain, I'm pausing for you to do your-- 111 00:05:38,323 --> 00:05:40,490 ROMAIN DE SAINT PERIER: We have our first volunteer. 112 00:05:40,490 --> 00:05:42,050 Thank you very much, Luke. 113 00:05:42,050 --> 00:05:42,970 The floor is yours. 114 00:05:46,352 --> 00:05:48,060 AUDIENCE: I'll answer the first question. 115 00:05:48,060 --> 00:05:51,390 The main technological change that we see in US 116 00:05:51,390 --> 00:05:55,320 and outside of US is versus open banking, use of a lot of APIs. 117 00:05:55,320 --> 00:05:57,623 They can be applicable to other websites. 118 00:05:57,623 --> 00:05:59,040 GARY GENSLER: All right, and we're 119 00:05:59,040 --> 00:06:04,500 going to spend a whole class on OpenAPIs in two weeks. 120 00:06:04,500 --> 00:06:06,540 But this is an important part of marketing, 121 00:06:06,540 --> 00:06:09,930 opening up the banks' ledgers and their data. 122 00:06:09,930 --> 00:06:13,380 And data, as people would like to say, 123 00:06:13,380 --> 00:06:17,490 is sort of the new oil in the business. 124 00:06:17,490 --> 00:06:20,280 It's very valuable for us. 125 00:06:20,280 --> 00:06:21,773 Anybody else, Romain? 126 00:06:21,773 --> 00:06:22,940 ROMAIN DE SAINT PERIER: Yes. 127 00:06:27,030 --> 00:06:29,200 AUDIENCE: The natural language processing so that we 128 00:06:29,200 --> 00:06:32,807 can have the robotic advisors. 129 00:06:32,807 --> 00:06:33,640 GARY GENSLER: Right. 130 00:06:33,640 --> 00:06:36,310 So natural language processing is the concept 131 00:06:36,310 --> 00:06:40,390 that you can take something that's in human language 132 00:06:40,390 --> 00:06:43,270 and put it into machine or computer language 133 00:06:43,270 --> 00:06:45,320 or go the vice versa. 134 00:06:45,320 --> 00:06:47,710 And it's not actually new in 2020. 135 00:06:47,710 --> 00:06:50,020 Some form of natural language processing 136 00:06:50,020 --> 00:06:53,860 has been around for decades, just in terms of reading-- 137 00:06:53,860 --> 00:06:59,770 reading computer code and putting it into an audio voice 138 00:06:59,770 --> 00:07:01,210 or going backwards. 139 00:07:01,210 --> 00:07:05,240 Or every postal service of a major country around the globe 140 00:07:05,240 --> 00:07:08,320 has had something to read our scribbled handwriting 141 00:07:08,320 --> 00:07:11,060 and trying to read that handwriting 142 00:07:11,060 --> 00:07:13,210 and then put it into something where they know 143 00:07:13,210 --> 00:07:16,210 which post box to send it to. 144 00:07:16,210 --> 00:07:19,210 But natural language processing, we'll spend a fair bit of time, 145 00:07:19,210 --> 00:07:21,010 and robotics. 146 00:07:21,010 --> 00:07:21,703 Romain? 147 00:07:21,703 --> 00:07:23,120 ROMAIN DE SAINT PERIER: And now we 148 00:07:23,120 --> 00:07:26,750 have Ivy, who raised her hand. 149 00:07:26,750 --> 00:07:30,320 AUDIENCE: Yeah, so I think we've seen a lot of digitization 150 00:07:30,320 --> 00:07:33,320 in the e-commerce space as well, especially 151 00:07:33,320 --> 00:07:34,880 in places like China. 152 00:07:34,880 --> 00:07:38,540 And you see this kind of divergence between China 153 00:07:38,540 --> 00:07:41,300 and, say, the US and the way we use mobile pay 154 00:07:41,300 --> 00:07:45,200 and the way that they've really adopted like Alipay and WePay 155 00:07:45,200 --> 00:07:45,715 as well. 156 00:07:45,715 --> 00:07:47,090 GARY GENSLER: And Ivy, why do you 157 00:07:47,090 --> 00:07:51,030 think it happened so, as you say, at this divergence, 158 00:07:51,030 --> 00:07:54,840 why it happened maybe a little faster in China? 159 00:07:54,840 --> 00:07:57,950 AUDIENCE: So I think it's pretty interesting, because I think 160 00:07:57,950 --> 00:07:59,660 a place like China, as an example, 161 00:07:59,660 --> 00:08:03,140 is probably less developed in terms 162 00:08:03,140 --> 00:08:06,920 of even just their financial structure, whereas the place 163 00:08:06,920 --> 00:08:08,750 like the US, it's quite dominated. 164 00:08:08,750 --> 00:08:10,410 And it's really competitive. 165 00:08:10,410 --> 00:08:12,050 But it's also really consolidated. 166 00:08:12,050 --> 00:08:14,420 So you see these countries where-- 167 00:08:14,420 --> 00:08:16,260 I mean, I think the way I think about it 168 00:08:16,260 --> 00:08:20,487 is the subway systems in China or Taiwan 169 00:08:20,487 --> 00:08:22,070 or a lot of these developing countries 170 00:08:22,070 --> 00:08:24,100 are much better because they were just-- 171 00:08:24,100 --> 00:08:26,540 they came a little bit later. 172 00:08:26,540 --> 00:08:30,080 And I just look at that analogy similarly to kind 173 00:08:30,080 --> 00:08:33,860 of where payments are, because you kind of go 174 00:08:33,860 --> 00:08:36,363 from 0 to 100 versus we are kind of something-- 175 00:08:36,363 --> 00:08:38,530 GARY GENSLER: No, I think Ivy's raised a good point. 176 00:08:38,530 --> 00:08:41,960 There's times when a country is growing rapidly. 177 00:08:41,960 --> 00:08:46,010 And China, for instance, had been growing at 8% to 10% GDP 178 00:08:46,010 --> 00:08:47,770 growth a year. 179 00:08:47,770 --> 00:08:51,650 And before corona, it had come down to still a robust 6% 180 00:08:51,650 --> 00:08:52,970 a year. 181 00:08:52,970 --> 00:08:55,960 But within that context, many things 182 00:08:55,960 --> 00:09:02,090 leapfrogged incumbents in Europe and in North America. 183 00:09:02,090 --> 00:09:05,060 And in the payment space in particular, two big tech 184 00:09:05,060 --> 00:09:07,460 companies-- 185 00:09:07,460 --> 00:09:12,380 Alibaba, that really is the dominant online retailing 186 00:09:12,380 --> 00:09:16,180 company, and Tencent, which was the dominant online sort 187 00:09:16,180 --> 00:09:20,300 of social networking and messaging company-- 188 00:09:20,300 --> 00:09:23,510 leapfrogged the banking system, the traditional banking system, 189 00:09:23,510 --> 00:09:25,460 and now with WeChat Pay and Alipay 190 00:09:25,460 --> 00:09:29,780 control well over 90% of retail payments, small dollar, 191 00:09:29,780 --> 00:09:32,660 and small and medium-sized enterprise payments. 192 00:09:32,660 --> 00:09:35,240 They don't dominate large wholesale payments, 193 00:09:35,240 --> 00:09:37,685 but put in the retail space, absolutely. 194 00:09:37,685 --> 00:09:38,810 And I would agree with Ivy. 195 00:09:38,810 --> 00:09:40,010 They kind of leapfrogged us. 196 00:09:40,010 --> 00:09:45,530 But even Kenya leapfrogged us with M-Pesa, a technology that 197 00:09:45,530 --> 00:09:49,400 was pushed forward by a telephone company, Safaricom, 198 00:09:49,400 --> 00:09:53,030 when they noticed that folks were trading mobile minutes 199 00:09:53,030 --> 00:09:54,958 as a form of money. 200 00:09:54,958 --> 00:09:56,500 ROMAIN DE SAINT PERIER: Gary, we have 201 00:09:56,500 --> 00:09:59,160 two more hands that are up. 202 00:09:59,160 --> 00:10:00,040 We can start with-- 203 00:10:00,040 --> 00:10:00,460 GARY GENSLER: All right, why don't we 204 00:10:00,460 --> 00:10:01,543 do those and then move on? 205 00:10:01,543 --> 00:10:03,510 So who are the two people? 206 00:10:03,510 --> 00:10:05,153 And they can just go in turn. 207 00:10:05,153 --> 00:10:06,820 ROMAIN DE SAINT PERIER: So we had Laira, 208 00:10:06,820 --> 00:10:08,090 but she just disappeared. 209 00:10:08,090 --> 00:10:09,730 So we'll go with Alida. 210 00:10:09,730 --> 00:10:12,550 GARY GENSLER: All right, one, thank you. 211 00:10:12,550 --> 00:10:15,450 AUDIENCE: Yeah, so I, to kind of add on to Ivy's point, 212 00:10:15,450 --> 00:10:18,100 is a lot of financial institutions in emerging 213 00:10:18,100 --> 00:10:23,380 markets did not typically cater towards the mass-market 214 00:10:23,380 --> 00:10:25,400 consumer population. 215 00:10:25,400 --> 00:10:28,570 And so it really allowed very quickly for these big tech 216 00:10:28,570 --> 00:10:32,380 companies to jump in a way that you couldn't do so in the-- 217 00:10:32,380 --> 00:10:36,010 in more developed markets, where the financial institutions 218 00:10:36,010 --> 00:10:39,130 already were catering to the large majority of the consumer 219 00:10:39,130 --> 00:10:41,100 population. 220 00:10:41,100 --> 00:10:42,930 GARY GENSLER: Right, so it's about 221 00:10:42,930 --> 00:10:47,890 actual financial inclusion and reaching out and so forth. 222 00:10:47,890 --> 00:10:52,050 So what I'm going to do today is try to cover, in the minutes 223 00:10:52,050 --> 00:10:54,840 we have, a little bit about the financial world. 224 00:10:54,840 --> 00:10:58,230 What do we mean, FinTech shaping the future 225 00:10:58,230 --> 00:10:59,490 of the financial world? 226 00:10:59,490 --> 00:11:02,880 What do I what do I think of it, having spent my life-- first, 227 00:11:02,880 --> 00:11:06,180 I was 18 years at Goldman Sachs. 228 00:11:06,180 --> 00:11:08,580 Then I worked in the public sector, 229 00:11:08,580 --> 00:11:12,120 but always sort of around finance, with the US Treasury 230 00:11:12,120 --> 00:11:15,660 Department, with Paul Sarbanes doing Sarbanes-Oxley, and then 231 00:11:15,660 --> 00:11:19,470 later running a market regulator, the Commodity 232 00:11:19,470 --> 00:11:24,290 Futures Trading Commission, in the Obama administration. 233 00:11:24,290 --> 00:11:27,330 What do we mean by the financial world? 234 00:11:27,330 --> 00:11:28,860 A little touch on FinTech-- 235 00:11:28,860 --> 00:11:30,360 that's the whole class, of course, 236 00:11:30,360 --> 00:11:32,760 but just a little touch on FinTech. 237 00:11:32,760 --> 00:11:35,340 Thirdly, again, just a little review 238 00:11:35,340 --> 00:11:37,840 of these three big trends-- 239 00:11:37,840 --> 00:11:41,290 of AI, open banking, blockchain technology-- 240 00:11:41,290 --> 00:11:44,520 what do these trends mean? 241 00:11:44,520 --> 00:11:46,050 And then the actors-- 242 00:11:46,050 --> 00:11:49,200 and the actors, I think that some people will use the word 243 00:11:49,200 --> 00:11:51,960 FinTech to mean these disruptors, companies 244 00:11:51,960 --> 00:11:55,470 like Toast getting into the payment space for restaurants 245 00:11:55,470 --> 00:12:00,090 or Lending Club and peer-to-peer lending or Robinhood, an app 246 00:12:00,090 --> 00:12:03,000 you can download and trade stocks. 247 00:12:03,000 --> 00:12:06,510 A lot of people constrain the study and the topic of FinTech 248 00:12:06,510 --> 00:12:08,370 just to the disruptors. 249 00:12:08,370 --> 00:12:10,630 I think that that's too narrow. 250 00:12:10,630 --> 00:12:12,720 I think that we really need to think 251 00:12:12,720 --> 00:12:14,610 of the actors and the field, more 252 00:12:14,610 --> 00:12:17,190 broadly about the incumbents. 253 00:12:17,190 --> 00:12:20,270 This is sort of big finance, we might say, 254 00:12:20,270 --> 00:12:26,190 the Barclays banks and the JPMorgans and so forth. 255 00:12:26,190 --> 00:12:28,320 And we need to think of big tech, 256 00:12:28,320 --> 00:12:33,600 as we just talked with Ivy about Alibaba and Tencent 257 00:12:33,600 --> 00:12:34,850 getting into this business. 258 00:12:34,850 --> 00:12:38,550 But we see Apple Credit Card and others and Facebook trying 259 00:12:38,550 --> 00:12:40,950 to stand up a world currency. 260 00:12:40,950 --> 00:12:42,870 And then it's the disruptors. 261 00:12:42,870 --> 00:12:47,010 So I think it's a much more robust conversation 262 00:12:47,010 --> 00:12:49,980 and an important conversation, the strategy 263 00:12:49,980 --> 00:12:51,208 amongst these three pieces. 264 00:12:51,208 --> 00:12:53,250 And then, of course, we've got to do a little bit 265 00:12:53,250 --> 00:12:55,620 on our teaching team, our schedule and assignments 266 00:12:55,620 --> 00:12:58,000 and so forth. 267 00:12:58,000 --> 00:13:01,920 So what do I think of is in the financial world and what it is? 268 00:13:01,920 --> 00:13:05,900 Well, finance basically stands, like this hourglass 269 00:13:05,900 --> 00:13:08,160 in the top-right-hand corner, stands 270 00:13:08,160 --> 00:13:11,580 right at the neck of an hourglass, intermediating, 271 00:13:11,580 --> 00:13:16,080 standing between people that have money and need money, 272 00:13:16,080 --> 00:13:20,180 people that have risk and want to get rid of it, lay it off, 273 00:13:20,180 --> 00:13:22,590 and somebody that wants to pick it up. 274 00:13:22,590 --> 00:13:25,290 And I have for decades, since I was at Goldman Sachs, 275 00:13:25,290 --> 00:13:28,860 thought where we were, we were at the neck of the hourglass. 276 00:13:28,860 --> 00:13:31,800 And for good or for bad, that's also 277 00:13:31,800 --> 00:13:34,800 part of why finance in many countries 278 00:13:34,800 --> 00:13:37,290 is able to collect economic rents. 279 00:13:37,290 --> 00:13:40,470 Economic rents is that classic conceptual framework 280 00:13:40,470 --> 00:13:43,860 of collecting profits or revenues in excess 281 00:13:43,860 --> 00:13:47,100 of what classic economics might tell you 282 00:13:47,100 --> 00:13:50,320 would be a competitive supply and demand space. 283 00:13:50,320 --> 00:13:52,950 But if you stand at the neck of an hourglass, 284 00:13:52,950 --> 00:13:57,510 between trillions of money flowing from those who want it 285 00:13:57,510 --> 00:14:00,780 and those who have it, and effectively trillions 286 00:14:00,780 --> 00:14:05,460 of risk between those who have risk and want to lay it off 287 00:14:05,460 --> 00:14:07,290 and others who are willing to hedge it-- 288 00:14:07,290 --> 00:14:10,230 if you're at that neck of the hourglass, so to speak, 289 00:14:10,230 --> 00:14:13,170 if you just collect a few grains of sand 290 00:14:13,170 --> 00:14:16,470 for the trillions that go by, it can collect a lot. 291 00:14:16,470 --> 00:14:19,420 In the United States, for one, for instance, 292 00:14:19,420 --> 00:14:21,825 our financial sector takes about 7 and 1/2% 293 00:14:21,825 --> 00:14:24,450 of our Gross Domestic Product. 294 00:14:24,450 --> 00:14:26,520 Nowhere is it written it has to. 295 00:14:26,520 --> 00:14:29,280 In fact, in the 1950s and '60s, it was more like 3 296 00:14:29,280 --> 00:14:31,470 and 1/2% to 4%. 297 00:14:31,470 --> 00:14:33,960 But persistently, it's grown as a percentage 298 00:14:33,960 --> 00:14:37,560 of our economy, standing, intermediating money and risk. 299 00:14:41,670 --> 00:14:44,690 Let's just see if I can get this to work. 300 00:14:44,690 --> 00:14:46,600 There, so the functions, the functions 301 00:14:46,600 --> 00:14:47,890 are intermediating credit. 302 00:14:47,890 --> 00:14:49,130 That's lending. 303 00:14:49,130 --> 00:14:50,980 Investments, we all know that. 304 00:14:50,980 --> 00:14:53,890 Risk transformation-- think of any time one of us 305 00:14:53,890 --> 00:14:58,510 buys insurance on an automobile or a car or on our life, 306 00:14:58,510 --> 00:15:02,410 but also risk transformation that investment banks do, even 307 00:15:02,410 --> 00:15:05,680 between somebody that's issuing stock and somebody that's 308 00:15:05,680 --> 00:15:06,550 buying stock. 309 00:15:06,550 --> 00:15:09,010 That's a transference of risk in terms 310 00:15:09,010 --> 00:15:11,530 of whether that startup will do well. 311 00:15:11,530 --> 00:15:13,450 Of course, there's the capital markets. 312 00:15:13,450 --> 00:15:15,460 And at the center of the capital markets 313 00:15:15,460 --> 00:15:18,520 is the price, the money and risk that's 314 00:15:18,520 --> 00:15:20,120 flowing through the system. 315 00:15:20,120 --> 00:15:22,570 And there's plenty of advice to go around. 316 00:15:22,570 --> 00:15:24,820 Now, we usually think about it in sectors. 317 00:15:24,820 --> 00:15:26,830 And every one of these sectors, whether it's 318 00:15:26,830 --> 00:15:29,530 commercial banking, asset management, insurance, 319 00:15:29,530 --> 00:15:32,350 investment banking, advisories, we 320 00:15:32,350 --> 00:15:34,810 will touch upon during this semester. 321 00:15:34,810 --> 00:15:36,130 And please, interrupt. 322 00:15:36,130 --> 00:15:39,430 If your keen interest is about insurance companies 323 00:15:39,430 --> 00:15:42,670 or your keen interest is about investment banks, 324 00:15:42,670 --> 00:15:47,170 then pull the community into that in these discussions. 325 00:15:47,170 --> 00:15:50,350 But we're going to try to talk about multiple sectors, 326 00:15:50,350 --> 00:15:52,510 multiple functions, as contrasted 327 00:15:52,510 --> 00:15:55,780 to the half-semester consumer finance course that was really 328 00:15:55,780 --> 00:16:01,090 just about one slice, household lending, and largely 329 00:16:01,090 --> 00:16:05,020 the commercial banks and investment banks around that. 330 00:16:05,020 --> 00:16:07,360 This is a much broader topic. 331 00:16:07,360 --> 00:16:09,760 And I hope the learning objective is ultimately 332 00:16:09,760 --> 00:16:14,320 to understand how technology can transform finance 333 00:16:14,320 --> 00:16:18,360 at any particular given time. 334 00:16:18,360 --> 00:16:20,140 Romain, are there any hands up? 335 00:16:20,140 --> 00:16:22,600 I'm sort of every once in a while looking to you to see 336 00:16:22,600 --> 00:16:24,322 if I keep going or pause. 337 00:16:24,322 --> 00:16:26,530 ROMAIN DE SAINT PERIER: No hands at the moment, Gary. 338 00:16:26,530 --> 00:16:27,530 GARY GENSLER: All right. 339 00:16:27,530 --> 00:16:31,120 The financial world, in this, I think, four key things 340 00:16:31,120 --> 00:16:32,110 to think about. 341 00:16:32,110 --> 00:16:34,330 And this is sort of in a framework of thinking 342 00:16:34,330 --> 00:16:35,680 about financial technology. 343 00:16:35,680 --> 00:16:40,720 Data-- of course, data is that new oil, so to speak, 344 00:16:40,720 --> 00:16:44,260 for investing, for market-making, for marketing, 345 00:16:44,260 --> 00:16:45,760 trying to get new customers. 346 00:16:45,760 --> 00:16:48,550 I mean, how many times do we get a pop-up ads. 347 00:16:48,550 --> 00:16:51,890 I found in teaching about student loans, 348 00:16:51,890 --> 00:16:53,540 I was researching student loans. 349 00:16:53,540 --> 00:16:55,780 And my god, the last six weeks I've 350 00:16:55,780 --> 00:16:59,830 got more advertisement for student loans, 351 00:16:59,830 --> 00:17:02,380 even now I'm a professor at MIT. 352 00:17:02,380 --> 00:17:05,589 It's because I was researching the topic of student loans, 353 00:17:05,589 --> 00:17:07,960 and all of a sudden, now I am getting 354 00:17:07,960 --> 00:17:15,160 a lot of unsolicited ads and emails, even, on the topic. 355 00:17:15,160 --> 00:17:16,599 The financial world it always has 356 00:17:16,599 --> 00:17:19,420 to think about the management of their balance sheet. 357 00:17:19,420 --> 00:17:22,089 And if you're starting a FinTech company, 358 00:17:22,089 --> 00:17:24,910 are you using your balance sheet or somebody else's balance 359 00:17:24,910 --> 00:17:25,660 sheet? 360 00:17:25,660 --> 00:17:27,980 And just as there's cloud computing, 361 00:17:27,980 --> 00:17:31,030 that today, cloud computing has dramatically 362 00:17:31,030 --> 00:17:34,270 shifted the ability of startups, a disruptor 363 00:17:34,270 --> 00:17:40,540 can come into a business and basically the rent versus bill 364 00:17:40,540 --> 00:17:41,680 decision changes. 365 00:17:41,680 --> 00:17:45,220 I can read somebody else's data storage capacity. 366 00:17:45,220 --> 00:17:51,010 I can basically rent the cloud instead of building my own data 367 00:17:51,010 --> 00:17:52,030 warehouse. 368 00:17:52,030 --> 00:17:54,190 That was a big change about 15 years ago. 369 00:17:54,190 --> 00:17:58,240 And it's made startups more viable in the 2020s. 370 00:17:58,240 --> 00:18:02,320 Also, the ability to raise money in the capital markets, what's 371 00:18:02,320 --> 00:18:06,670 called securitizations, which started decades ago, really 372 00:18:06,670 --> 00:18:10,870 took off by the 1990s, also allows a startup like Lending 373 00:18:10,870 --> 00:18:13,540 Club and others to say I'll raise my money elsewhere. 374 00:18:13,540 --> 00:18:16,240 I don't have to have a balance sheet. 375 00:18:16,240 --> 00:18:19,390 And then there's the various risks that you have to manage. 376 00:18:19,390 --> 00:18:22,060 And most importantly, we're going to spend a lot of time 377 00:18:22,060 --> 00:18:24,130 doing this class on this fourth point-- user 378 00:18:24,130 --> 00:18:26,200 experience, user interface. 379 00:18:26,200 --> 00:18:29,860 Much of what's happened with mobile phones, 380 00:18:29,860 --> 00:18:35,110 with apps that you can download for free have given all of us 381 00:18:35,110 --> 00:18:39,070 a better user experience than online banking. 382 00:18:39,070 --> 00:18:41,900 Online banking has been around for over 20 years. 383 00:18:41,900 --> 00:18:43,870 Most of the major banks around the globe 384 00:18:43,870 --> 00:18:47,920 had a viable platform for online banking by the naughts, 385 00:18:47,920 --> 00:18:52,380 whether it was 2005 for some or 2008 or others or so forth. 386 00:18:52,380 --> 00:18:56,020 But by the naughts, most had online banking. 387 00:18:56,020 --> 00:18:58,300 And yet, their user experience wasn't what 388 00:18:58,300 --> 00:19:01,300 we all wanted, and certainly wasn't what-- maybe 389 00:19:01,300 --> 00:19:04,300 if the newer users, as millennials, 390 00:19:04,300 --> 00:19:06,490 were coming into the marketplace, 391 00:19:06,490 --> 00:19:09,890 to do it on your mobile phone, to do it conveniently. 392 00:19:09,890 --> 00:19:12,890 So the user experience and the user interface 393 00:19:12,890 --> 00:19:16,990 is the critical competitive place. 394 00:19:16,990 --> 00:19:18,520 So what do we mean by FinTech? 395 00:19:18,520 --> 00:19:22,210 I like going back to that 2017 Financial Stability Board 396 00:19:22,210 --> 00:19:22,950 report. 397 00:19:22,950 --> 00:19:26,020 That doesn't mean that they have it quite right. 398 00:19:26,020 --> 00:19:27,880 But they basically talk about that it's 399 00:19:27,880 --> 00:19:34,170 technology-enabled innovation in the financial world that's 400 00:19:34,170 --> 00:19:39,650 going to some new business and it's material. 401 00:19:39,650 --> 00:19:43,060 So, as I mentioned earlier, the telegraph in the 1830s 402 00:19:43,060 --> 00:19:43,750 comes along. 403 00:19:43,750 --> 00:19:46,180 And by the 1860s and 1870s, we're 404 00:19:46,180 --> 00:19:49,090 starting to see digital money. 405 00:19:49,090 --> 00:19:56,130 The first form of digital money is already about 140 years old. 406 00:19:56,130 --> 00:20:00,300 I mean, we think we're living in this digital age, and we are. 407 00:20:00,300 --> 00:20:03,300 And I would note this about the corona crisis 408 00:20:03,300 --> 00:20:04,650 that we're living in-- 409 00:20:04,650 --> 00:20:06,820 the corona crisis, in my opinion, 410 00:20:06,820 --> 00:20:09,600 will accelerate this digitization. 411 00:20:09,600 --> 00:20:11,730 We've already, in the last 30 years, 412 00:20:11,730 --> 00:20:15,960 significantly digitized our world. 413 00:20:15,960 --> 00:20:18,280 And if we were all together in a classroom, 414 00:20:18,280 --> 00:20:20,470 I would ask by a show of hands how many of you 415 00:20:20,470 --> 00:20:24,240 have used paper money or currency in the last two 416 00:20:24,240 --> 00:20:25,770 or three days. 417 00:20:25,770 --> 00:20:27,630 And maybe a quarter of you might have 418 00:20:27,630 --> 00:20:31,230 said you had used paper money in the last two or three days. 419 00:20:31,230 --> 00:20:33,910 But let me ask you in the middle of the corona crisis-- 420 00:20:33,910 --> 00:20:37,240 and Romain, you can tell me if any hands go up-- 421 00:20:37,240 --> 00:20:39,690 we'll you use the blue hand, anybody 422 00:20:39,690 --> 00:20:44,905 that's used paper, physical money in the last two days. 423 00:20:46,492 --> 00:20:48,950 ROMAIN DE SAINT PERIER: Thank you, Alida, for volunteering. 424 00:20:48,950 --> 00:20:49,760 The floor is yours. 425 00:20:54,220 --> 00:20:57,138 AUDIENCE: I have for grocery shopping and so forth. 426 00:20:57,138 --> 00:20:58,430 GARY GENSLER: So you actually-- 427 00:20:58,430 --> 00:21:03,030 and they'll still take the paper money behind the counter 428 00:21:03,030 --> 00:21:06,920 without wiping it down with some cleaner? 429 00:21:06,920 --> 00:21:07,570 AUDIENCE: Yes. 430 00:21:07,570 --> 00:21:10,420 And I took out a lot of cash before everything 431 00:21:10,420 --> 00:21:11,542 happened, so-- 432 00:21:11,542 --> 00:21:13,310 GARY GENSLER: Ah, ah. 433 00:21:13,310 --> 00:21:16,090 Alida, may I ask, was that just sort of insurance, 434 00:21:16,090 --> 00:21:18,837 managing risk, as we say, as part of finance, where you're-- 435 00:21:18,837 --> 00:21:19,420 AUDIENCE: Yes. 436 00:21:19,420 --> 00:21:20,795 GARY GENSLER: --managing the risk 437 00:21:20,795 --> 00:21:23,830 that the banking sector and the ATMs and things might not work? 438 00:21:23,830 --> 00:21:25,480 AUDIENCE: Yes. 439 00:21:25,480 --> 00:21:27,693 GARY GENSLER: And do I see Devin's hand? 440 00:21:27,693 --> 00:21:29,360 ROMAIN DE SAINT PERIER: That is correct. 441 00:21:29,360 --> 00:21:33,022 AUDIENCE: I can only do laundry using cash. 442 00:21:33,022 --> 00:21:36,490 So I use it for now, but-- 443 00:21:36,490 --> 00:21:39,560 GARY GENSLER: But see how few of us, of 80 plus people, 444 00:21:39,560 --> 00:21:40,250 two people. 445 00:21:40,250 --> 00:21:42,740 Now, we're in the middle of a corona crisis. 446 00:21:42,740 --> 00:21:47,180 I would predict that as we come out of this, whether this is-- 447 00:21:47,180 --> 00:21:49,670 whether this is a matter of a couple of few months 448 00:21:49,670 --> 00:21:51,410 or whether this is, unfortunately, 449 00:21:51,410 --> 00:21:55,670 as long as couple of years, wherever we come out, 450 00:21:55,670 --> 00:21:58,310 that the corona crisis will accelerate 451 00:21:58,310 --> 00:22:00,470 an already existing trend. 452 00:22:00,470 --> 00:22:04,370 And that already existing trend distorts further and further 453 00:22:04,370 --> 00:22:07,910 digitalization of commerce, that that laundromat 454 00:22:07,910 --> 00:22:14,000 that Devin goes to will take a QR code or a swipe of a phone 455 00:22:14,000 --> 00:22:15,410 more readily. 456 00:22:15,410 --> 00:22:20,600 And yes, I think it was Anita said that she took out cash 457 00:22:20,600 --> 00:22:21,720 before the crisis. 458 00:22:21,720 --> 00:22:22,850 And that's true. 459 00:22:22,850 --> 00:22:25,310 There is still going to be some people that probably 460 00:22:25,310 --> 00:22:27,530 also went and bought gold. 461 00:22:27,530 --> 00:22:29,960 And we should respect that, that that's 462 00:22:29,960 --> 00:22:33,260 sort of an insurance policy against the digital world 463 00:22:33,260 --> 00:22:34,467 collapsing. 464 00:22:34,467 --> 00:22:36,050 ROMAIN DE SAINT PERIER: Gary, it seems 465 00:22:36,050 --> 00:22:37,520 like Devin has a follow-up. 466 00:22:37,520 --> 00:22:39,974 GARY GENSLER: Yes, please. 467 00:22:39,974 --> 00:22:42,150 No, and maybe Devin just left his hand up. 468 00:22:42,150 --> 00:22:42,900 AUDIENCE: I apol-- 469 00:22:42,900 --> 00:22:44,540 GARY GENSLER: Maybe after you speak, 470 00:22:44,540 --> 00:22:46,515 you actually then take your hand down. 471 00:22:46,515 --> 00:22:46,800 AUDIENCE: All right. 472 00:22:46,800 --> 00:22:49,133 GARY GENSLER: So what are the technologies of our times? 473 00:22:49,133 --> 00:22:52,590 What's sort of rolling with it now, no longer the telegraph 474 00:22:52,590 --> 00:22:54,240 or so forth? 475 00:22:54,240 --> 00:22:55,855 Well, I put up my favorites. 476 00:22:55,855 --> 00:22:57,480 And then I kind of think, well, maybe I 477 00:22:57,480 --> 00:22:58,938 shouldn't have put the cloud there. 478 00:22:58,938 --> 00:23:01,680 Maybe the cloud really isn't a technology that's 479 00:23:01,680 --> 00:23:03,900 pushing us forward right now. 480 00:23:03,900 --> 00:23:07,860 And yet, what's interesting is finance uses the cloud less 481 00:23:07,860 --> 00:23:09,450 than most industries. 482 00:23:09,450 --> 00:23:13,245 And this is particularly the case of incumbents. 483 00:23:13,245 --> 00:23:15,870 Country by country-- it doesn't matter whether we're in Brazil, 484 00:23:15,870 --> 00:23:19,670 whether we're in Europe, whether we're in the US-- 485 00:23:19,670 --> 00:23:22,370 big incumbents, like JPMorgan and Bank 486 00:23:22,370 --> 00:23:25,710 of America and Barclays, tend to still have their own data 487 00:23:25,710 --> 00:23:26,210 centers. 488 00:23:26,210 --> 00:23:27,140 Now, that's shifting. 489 00:23:27,140 --> 00:23:29,450 I think the 2020s will see them shift over. 490 00:23:29,450 --> 00:23:33,260 But they've tended to want to sort of control their destiny. 491 00:23:33,260 --> 00:23:36,230 I think they will shift because it's lower cost and better 492 00:23:36,230 --> 00:23:40,070 cybersecurity generally if you have it, frankly, 493 00:23:40,070 --> 00:23:45,960 at Baidu in China or Microsoft or AWS and so forth here. 494 00:23:45,960 --> 00:23:48,680 But startups dramatically use the cloud. 495 00:23:48,680 --> 00:23:51,980 This is this buy versus build sort of scenario. 496 00:23:51,980 --> 00:23:54,260 Why build my own data center? 497 00:23:54,260 --> 00:23:55,280 I can rent it. 498 00:23:55,280 --> 00:23:57,050 I can use somebody else. 499 00:23:57,050 --> 00:23:59,240 But all the others we're going to talk about-- 500 00:23:59,240 --> 00:24:02,480 and I should say that there's a little-- 501 00:24:02,480 --> 00:24:06,320 a little sleight of hand by my list, because many of these 502 00:24:06,320 --> 00:24:09,440 are actually artificial intelligence and machine 503 00:24:09,440 --> 00:24:11,600 learning, which is part of artificial intelligence. 504 00:24:11,600 --> 00:24:14,030 Natural language processing, in essence, 505 00:24:14,030 --> 00:24:16,040 is artificial intelligence. 506 00:24:16,040 --> 00:24:20,870 Chatbots are sort of that as well, and robotic process 507 00:24:20,870 --> 00:24:21,520 automation. 508 00:24:21,520 --> 00:24:23,870 But I split them out because I think 509 00:24:23,870 --> 00:24:26,300 it's relevant that we kind of have a chance to talk 510 00:24:26,300 --> 00:24:30,370 about each of these as we go. 511 00:24:30,370 --> 00:24:32,950 I'm pausing every once in a while, Romain, 512 00:24:32,950 --> 00:24:34,000 but you can let me now. 513 00:24:36,600 --> 00:24:39,210 I like to think of this in the context of history. 514 00:24:39,210 --> 00:24:42,290 Maybe that's just because I've always loved studying history. 515 00:24:42,290 --> 00:24:44,940 But what's the customer interface 516 00:24:44,940 --> 00:24:49,530 that we've seen all the way back from sort of prehistory, 517 00:24:49,530 --> 00:24:53,940 that the customer interface was in tents or bricks and mortar 518 00:24:53,940 --> 00:24:57,360 later, all the way up through where 519 00:24:57,360 --> 00:25:00,960 we got credit cards invented in the 1940s and '50s? 520 00:25:00,960 --> 00:25:02,580 That was a FinTech. 521 00:25:02,580 --> 00:25:05,520 That was a new financial technology. 522 00:25:05,520 --> 00:25:08,580 Visa, here in the US, was a network 523 00:25:08,580 --> 00:25:12,450 of banks started by a California bank called Bank of America. 524 00:25:12,450 --> 00:25:15,270 And they wanted to have a nationwide network. 525 00:25:15,270 --> 00:25:16,740 And other banks joined it. 526 00:25:16,740 --> 00:25:19,130 That became the Visa network. 527 00:25:19,130 --> 00:25:20,860 But where are we now? 528 00:25:20,860 --> 00:25:26,003 What are the technologies now that-- 529 00:25:26,003 --> 00:25:27,920 I want to make sure I'm clicking on the right. 530 00:25:30,440 --> 00:25:32,810 So the base that you might be thinking about-- 531 00:25:32,810 --> 00:25:37,490 mobile payments, the internet, even contactless cards-- 532 00:25:37,490 --> 00:25:40,370 I would say that was kind of a last phase. 533 00:25:40,370 --> 00:25:43,640 That was the phase that really shifted finance 534 00:25:43,640 --> 00:25:47,660 in the naughts and early teens, and that we really 535 00:25:47,660 --> 00:25:52,590 are in terms of customer interface 536 00:25:52,590 --> 00:25:58,610 is now conversational interfaces, chatbots 537 00:25:58,610 --> 00:26:00,860 and conversational interfaces. 538 00:26:00,860 --> 00:26:03,350 That's the cutting edge. 539 00:26:03,350 --> 00:26:05,990 Yes, contactless cards are important. 540 00:26:05,990 --> 00:26:08,180 Yes, of course, we need mobile payments. 541 00:26:08,180 --> 00:26:09,990 No doubt about it. 542 00:26:09,990 --> 00:26:12,740 But if you're starting a new business right now, 543 00:26:12,740 --> 00:26:14,150 you're going to start a disruptor 544 00:26:14,150 --> 00:26:18,110 and say I'm going to do online banking, that's yesterday. 545 00:26:18,110 --> 00:26:20,930 You have to find a way to do that online banking 546 00:26:20,930 --> 00:26:25,040 in a way that has such a better customer interface. 547 00:26:25,040 --> 00:26:28,430 And you might be using some form of OpenAPIs 548 00:26:28,430 --> 00:26:32,490 or robotic process automation and chatbots to do it. 549 00:26:32,490 --> 00:26:36,290 But these are all kind of build up on each other. 550 00:26:36,290 --> 00:26:38,190 Then there's risk management. 551 00:26:38,190 --> 00:26:40,220 So one is the customer side. 552 00:26:40,220 --> 00:26:42,440 One is the funding and risk management side. 553 00:26:42,440 --> 00:26:44,380 And you can look throughout history. 554 00:26:44,380 --> 00:26:47,240 And I spent a lot of my life in my career 555 00:26:47,240 --> 00:26:49,040 around the capital markets. 556 00:26:49,040 --> 00:26:51,890 I was at Goldman Sachs for 18 years. 557 00:26:51,890 --> 00:26:54,470 And I had to think a lot with hundreds 558 00:26:54,470 --> 00:26:56,572 of other people about risk management 559 00:26:56,572 --> 00:26:57,530 and what we were doing. 560 00:26:57,530 --> 00:27:01,520 And in the 1980s and the 1990s, a big thing 561 00:27:01,520 --> 00:27:05,450 was asset-backed securitization and interest rate swaps 562 00:27:05,450 --> 00:27:07,910 and even, yes, credit default swaps that helped 563 00:27:07,910 --> 00:27:11,780 bring down several economies in 2008. 564 00:27:11,780 --> 00:27:16,190 But those innovations of the 1970s, the 1990s 565 00:27:16,190 --> 00:27:19,550 were dramatically shifting, dramatically 566 00:27:19,550 --> 00:27:21,280 shifting what was going on. 567 00:27:21,280 --> 00:27:25,140 But I would say now, it's really about machine learning. 568 00:27:25,140 --> 00:27:29,210 Machine learning is an ability to extract correlations, 569 00:27:29,210 --> 00:27:35,190 extract patterns from data in a way that we couldn't before. 570 00:27:35,190 --> 00:27:39,950 And many of you have studied linear algebra. 571 00:27:39,950 --> 00:27:43,970 We're blessed in this class to have remarkable science 572 00:27:43,970 --> 00:27:47,300 technology and math folks. 573 00:27:47,300 --> 00:27:50,690 Not all of you-- we accept all the English majors and history 574 00:27:50,690 --> 00:27:52,050 majors as well. 575 00:27:52,050 --> 00:27:56,780 But I'm saying that you know that from linear algebra 576 00:27:56,780 --> 00:28:01,470 and basic statistics, you can do a lot of regression analyses. 577 00:28:01,470 --> 00:28:03,890 Machine learning is going a little beyond that 578 00:28:03,890 --> 00:28:06,020 and extracting patterns from that 579 00:28:06,020 --> 00:28:08,810 which was more difficult to extract when you were just 580 00:28:08,810 --> 00:28:10,940 doing regression analysis. 581 00:28:10,940 --> 00:28:14,500 And so there's patterns that we might 582 00:28:14,500 --> 00:28:19,810 see in the data that says you're a good risk or a less good risk 583 00:28:19,810 --> 00:28:23,740 that you wouldn't quite say in a traditional credit scoring 584 00:28:23,740 --> 00:28:24,430 systems. 585 00:28:24,430 --> 00:28:27,710 I think that's where we're going right now. 586 00:28:27,710 --> 00:28:29,860 Romain, anything in the chat rooms? 587 00:28:29,860 --> 00:28:31,690 Or I'm going to pause for a second. 588 00:28:35,545 --> 00:28:37,420 ROMAIN DE SAINT PERIER: Nothing so far, Gary. 589 00:28:37,420 --> 00:28:38,600 GARY GENSLER: All right. 590 00:28:38,600 --> 00:28:42,010 So it's fertile ground. 591 00:28:42,010 --> 00:28:44,310 And we talked a little bit about this before. 592 00:28:44,310 --> 00:28:46,750 But the fertile ground of finance 593 00:28:46,750 --> 00:28:50,920 is we've basically digitized money securities and credit 594 00:28:50,920 --> 00:28:52,270 over the last 30 years. 595 00:28:52,270 --> 00:28:56,950 The corona crisis is just going to push that further faster, 596 00:28:56,950 --> 00:28:58,570 in my opinion. 597 00:28:58,570 --> 00:29:00,850 We have this vast amount of data. 598 00:29:00,850 --> 00:29:03,310 And we're starting to see patterns in that data 599 00:29:03,310 --> 00:29:06,370 that we didn't recognize before. 600 00:29:06,370 --> 00:29:12,070 I mentioned this in the consumer finance course that some of you 601 00:29:12,070 --> 00:29:16,480 were with me on, but somebody has actually studied something 602 00:29:16,480 --> 00:29:18,430 like this to say that if you charge 603 00:29:18,430 --> 00:29:22,480 your phone overnight every night, if you charge 604 00:29:22,480 --> 00:29:24,730 your phone every night, you apparently 605 00:29:24,730 --> 00:29:26,410 are a better credit than if you don't 606 00:29:26,410 --> 00:29:28,480 charge your phone every night. 607 00:29:28,480 --> 00:29:30,400 You might think, oh my gosh, I'd better 608 00:29:30,400 --> 00:29:36,040 be charging my phone because Apple is watching. 609 00:29:36,040 --> 00:29:39,490 They're going to be watching whether I charge my phone. 610 00:29:39,490 --> 00:29:42,300 And in China, there's a whole social credit 611 00:29:42,300 --> 00:29:47,140 system that takes data from many of your online apps. 612 00:29:47,140 --> 00:29:49,380 It's not just taking your payment information 613 00:29:49,380 --> 00:29:53,220 from Alipay or WeChat Pay, but that social credit system 614 00:29:53,220 --> 00:29:56,520 is checking even your participation 615 00:29:56,520 --> 00:29:58,590 in dating websites. 616 00:29:58,590 --> 00:29:59,610 I kid you not. 617 00:29:59,610 --> 00:30:00,930 I kid you not. 618 00:30:00,930 --> 00:30:04,200 So that social credit system in China 619 00:30:04,200 --> 00:30:09,000 or the private sector's approach to collecting data in the West 620 00:30:09,000 --> 00:30:13,110 have sort of the same goal in mind-- 621 00:30:13,110 --> 00:30:19,320 to understand the customers more and to basically 622 00:30:19,320 --> 00:30:23,290 provide more marketing, but also to assess risk. 623 00:30:23,290 --> 00:30:24,810 And, by the way, the corona crisis 624 00:30:24,810 --> 00:30:28,710 might change even the West's view of data sharing, 625 00:30:28,710 --> 00:30:32,640 because when we have Google and the US partnering up to say 626 00:30:32,640 --> 00:30:36,480 we can track everybody to see how this disease, how 627 00:30:36,480 --> 00:30:40,320 this disease is propagating through society, that we 628 00:30:40,320 --> 00:30:46,740 need the modern technology of our position location trackers. 629 00:30:46,740 --> 00:30:50,100 A position location tracker is called a cell phone. 630 00:30:50,100 --> 00:30:52,110 A smartphone is tracking where we 631 00:30:52,110 --> 00:30:56,920 are if you go running with it, if you go driving, hiking, 632 00:30:56,920 --> 00:30:58,750 all of that data. 633 00:30:58,750 --> 00:31:01,900 But now here, even in the US and in Europe, 634 00:31:01,900 --> 00:31:03,640 we're starting to say do we partner, 635 00:31:03,640 --> 00:31:07,390 as the official sector, with that data 636 00:31:07,390 --> 00:31:12,990 to keep our societies maybe a little safer, and then, 637 00:31:12,990 --> 00:31:16,050 of course, the rapid expansion of computation power 638 00:31:16,050 --> 00:31:16,857 and so forth. 639 00:31:16,857 --> 00:31:18,440 Now, I think it will change something. 640 00:31:18,440 --> 00:31:21,780 This disruptive potential, I think, has a dramatic change. 641 00:31:21,780 --> 00:31:25,500 We'll be talking about this slide the whole half semester. 642 00:31:25,500 --> 00:31:30,210 But managing risks, updating the customer user interfaces, 643 00:31:30,210 --> 00:31:31,980 and financial inclusion are the three 644 00:31:31,980 --> 00:31:36,180 that I want to focus a lot on, managing risk. 645 00:31:36,180 --> 00:31:40,200 And AI is also about targeting products as well. 646 00:31:40,200 --> 00:31:43,440 I think the interesting challenge is, near the bottom, 647 00:31:43,440 --> 00:31:46,260 will some revenue models shift? 648 00:31:46,260 --> 00:31:51,120 We saw a company called Credit Karma start here in the US. 649 00:31:51,120 --> 00:31:56,250 Credit Karma started basically with the idea to get a free-- 650 00:31:56,250 --> 00:31:59,980 free is the operative word-- a free credit score. 651 00:31:59,980 --> 00:32:03,420 The founders of Credit Karma couldn't get their credit score 652 00:32:03,420 --> 00:32:04,770 from the traditional companies-- 653 00:32:04,770 --> 00:32:08,270 TransUnion, Experian, and the like. 654 00:32:08,270 --> 00:32:13,350 And so they started an app just in the last 10 years. 655 00:32:13,350 --> 00:32:18,934 And in January, they sold Credit Karma for $7 billion. 656 00:32:18,934 --> 00:32:21,920 Now, Credit Karma is still a free app. 657 00:32:21,920 --> 00:32:26,930 And you might say how can Credit Karma commercialize a free app? 658 00:32:26,930 --> 00:32:30,320 In 2019, there was $1 billion in revenue for Credit Karma 659 00:32:30,320 --> 00:32:35,300 because they basically had built files on 106 million Americans. 660 00:32:35,300 --> 00:32:39,530 They built data files on 106 million Americans providing 661 00:32:39,530 --> 00:32:40,220 a free app. 662 00:32:40,220 --> 00:32:43,730 And, by the way, they do not have 106 million customers. 663 00:32:43,730 --> 00:32:46,100 They even built credit files on people 664 00:32:46,100 --> 00:32:47,920 that weren't their customers. 665 00:32:47,920 --> 00:32:50,120 And they were able to commercialize and build 666 00:32:50,120 --> 00:32:54,950 revenues around that data stream and, in essence, referencing. 667 00:32:54,950 --> 00:32:59,090 They get referral fees when somebody then does buy a loan 668 00:32:59,090 --> 00:33:03,740 or takes out a loan through the Credit Karma platform. 669 00:33:03,740 --> 00:33:05,740 ROMAIN DE SAINT PERIER: Gary, we have a question 670 00:33:05,740 --> 00:33:07,000 from [INAUDIBLE]. 671 00:33:07,000 --> 00:33:07,875 GARY GENSLER: Please. 672 00:33:11,000 --> 00:33:13,970 You might need to unmute yourself. 673 00:33:13,970 --> 00:33:14,890 We-- 674 00:33:14,890 --> 00:33:16,160 ROMAIN DE SAINT PERIER: So I am normally unmuting? 675 00:33:16,160 --> 00:33:16,993 AUDIENCE: I'm just-- 676 00:33:16,993 --> 00:33:18,500 ROMAIN DE SAINT PERIER: Yeah, OK. 677 00:33:18,500 --> 00:33:19,917 AUDIENCE: I'm just wondering, what 678 00:33:19,917 --> 00:33:23,150 is the difference between big tech and FinTech, 679 00:33:23,150 --> 00:33:26,810 and why it is important preparing and learning 680 00:33:26,810 --> 00:33:29,330 the FinTech now? 681 00:33:29,330 --> 00:33:31,273 GARY GENSLER: So I understand the second part 682 00:33:31,273 --> 00:33:31,940 of the question. 683 00:33:31,940 --> 00:33:34,250 You said the difference between what and FinTech 684 00:33:34,250 --> 00:33:35,300 at the beginning? 685 00:33:35,300 --> 00:33:36,305 AUDIENCE: Big tech. 686 00:33:36,305 --> 00:33:37,430 GARY GENSLER: OK, big tech. 687 00:33:37,430 --> 00:33:41,300 So by that, I think of-- 688 00:33:41,300 --> 00:33:45,260 I use the word FinTech in the broad way the Financial 689 00:33:45,260 --> 00:33:46,700 Stability Board does. 690 00:33:46,700 --> 00:33:50,660 I use it as is the intersection of finance and technology 691 00:33:50,660 --> 00:33:52,780 where the technology is new-- 692 00:33:52,780 --> 00:33:56,180 so not the telephone, not the internet, 693 00:33:56,180 --> 00:33:59,960 but it's something new, like AI and machine learning, 694 00:33:59,960 --> 00:34:04,860 that may materially change the provision of finance. 695 00:34:04,860 --> 00:34:09,170 So I use it to capture the whole field. 696 00:34:09,170 --> 00:34:11,989 Why it's important is I think it's important every decade. 697 00:34:11,989 --> 00:34:14,600 I don't think it's just important now, 698 00:34:14,600 --> 00:34:17,989 because I think technologies will come along each half 699 00:34:17,989 --> 00:34:20,360 decade, each decade that will materially 700 00:34:20,360 --> 00:34:25,100 change finance and provide the entrepreneurs in this class 701 00:34:25,100 --> 00:34:30,500 an opportunity to break into the wide margins. 702 00:34:30,500 --> 00:34:35,060 In the US, 7 and 1/2% of our economy or $1 and 1/2 trillion 703 00:34:35,060 --> 00:34:39,139 is the revenues of finance. 704 00:34:39,139 --> 00:34:41,389 So if you're an entrepreneur, you want part of that $1 705 00:34:41,389 --> 00:34:42,350 and 1/2 trillion. 706 00:34:42,350 --> 00:34:44,120 You want an opportunity. 707 00:34:44,120 --> 00:34:46,010 And usually, it's technology that's 708 00:34:46,010 --> 00:34:47,420 changing business models. 709 00:34:47,420 --> 00:34:50,870 When the internet came along in the 1990s, that 710 00:34:50,870 --> 00:34:54,350 provided an opportunity for PayPal to start and say maybe 711 00:34:54,350 --> 00:34:57,170 we can provide a better payment solution. 712 00:34:57,170 --> 00:34:59,420 And subsequent payment solutions, 713 00:34:59,420 --> 00:35:03,320 like Venmo and TransferWise and Square, 714 00:35:03,320 --> 00:35:07,310 have all been opportunities to chip away a little bit. 715 00:35:07,310 --> 00:35:10,790 I would say only in the opportunity-- 716 00:35:10,790 --> 00:35:13,100 the opportunities came because technology 717 00:35:13,100 --> 00:35:15,950 was changing the field. 718 00:35:15,950 --> 00:35:18,320 What's the difference between big tech and FinTech? 719 00:35:18,320 --> 00:35:22,010 Big tech, to me, are big platform companies-- 720 00:35:22,010 --> 00:35:27,020 in the US, the Facebooks and the Amazons and the Apples, 721 00:35:27,020 --> 00:35:31,730 in China, Baidu, Alibaba, Tencent, in Africa, 722 00:35:31,730 --> 00:35:33,920 even, Safaricom got in. 723 00:35:33,920 --> 00:35:35,230 We could go country by country. 724 00:35:35,230 --> 00:35:39,590 India, the big tech has gone into payments as well. 725 00:35:39,590 --> 00:35:44,630 I think that big tech companies have dramatic advantages. 726 00:35:44,630 --> 00:35:47,870 And then I separate what I would call FinTech startups 727 00:35:47,870 --> 00:35:50,030 or FinTech disruptors. 728 00:35:50,030 --> 00:35:53,630 I always put the second noun in there, disruptor or startup, 729 00:35:53,630 --> 00:35:57,630 because to me, the word FinTech is the whole field. 730 00:35:57,630 --> 00:35:58,533 I hope that helps. 731 00:35:58,533 --> 00:36:00,700 ROMAIN DE SAINT PERIER: And we have another question 732 00:36:00,700 --> 00:36:01,685 from [INAUDIBLE]. 733 00:36:01,685 --> 00:36:02,560 GARY GENSLER: Please. 734 00:36:05,420 --> 00:36:06,870 AUDIENCE: Yeah, hi, Professor. 735 00:36:06,870 --> 00:36:08,520 One question that I have is actually 736 00:36:08,520 --> 00:36:11,055 one concerned with privacy. 737 00:36:14,010 --> 00:36:16,650 At some point, do you think that we 738 00:36:16,650 --> 00:36:20,310 would have up give up to some extent our privacy in order 739 00:36:20,310 --> 00:36:21,240 to-- 740 00:36:21,240 --> 00:36:24,600 for me to get a credit from a startup 741 00:36:24,600 --> 00:36:25,883 or get a credit from somebody? 742 00:36:25,883 --> 00:36:27,300 GARY GENSLER: Well, I think you've 743 00:36:27,300 --> 00:36:31,140 raised a dramatic issue for society at large. 744 00:36:31,140 --> 00:36:33,670 And finance is one example of it. 745 00:36:33,670 --> 00:36:35,070 But yes, we have-- 746 00:36:35,070 --> 00:36:39,030 we have shared our personal data much more broadly 747 00:36:39,030 --> 00:36:42,120 in the last 10 years, and certainly in the last 30 years, 748 00:36:42,120 --> 00:36:44,810 than we did in societies before that. 749 00:36:44,810 --> 00:36:49,410 And in terms of getting credit card, yes, we've 750 00:36:49,410 --> 00:36:56,220 been sharing data for 50 years through various credit-- 751 00:36:56,220 --> 00:36:58,020 consumer credit companies. 752 00:36:58,020 --> 00:37:05,370 The Fair Isaac Company was founded almost 60 years ago 753 00:37:05,370 --> 00:37:07,980 by two people out of Stanford, actually, 754 00:37:07,980 --> 00:37:10,680 one named Fair and one named Isaac. 755 00:37:10,680 --> 00:37:13,080 And that led to something called FICO scores, 756 00:37:13,080 --> 00:37:16,230 which are used in over 30 countries around the globe. 757 00:37:16,230 --> 00:37:19,500 These FICO scores took some personal data, 758 00:37:19,500 --> 00:37:22,860 as to whether you were paying your bills on time. 759 00:37:22,860 --> 00:37:27,600 But now, we can go beyond that and capture alternative data. 760 00:37:27,600 --> 00:37:31,130 We can capture somebody's digital footprint. 761 00:37:31,130 --> 00:37:34,470 And in China, they are doing that with social credit scoring 762 00:37:34,470 --> 00:37:38,580 and Alibaba is with Alipay and WeChat Pay. 763 00:37:38,580 --> 00:37:41,040 But Amazon is capturing our data as well. 764 00:37:41,040 --> 00:37:44,330 Amazon captures any Amazon Prime customer. 765 00:37:44,330 --> 00:37:47,280 And I'm sure amongst at of us, there are many Amazon Prime 766 00:37:47,280 --> 00:37:48,090 customers. 767 00:37:48,090 --> 00:37:51,490 That data is being captured in some way. 768 00:37:51,490 --> 00:37:54,660 Now, it leads to more financial inclusion. 769 00:37:54,660 --> 00:37:57,810 But it also raises all sorts of issues around privacy 770 00:37:57,810 --> 00:38:00,300 that we're grappling with as societies. 771 00:38:00,300 --> 00:38:02,490 In Europe, they passed something called 772 00:38:02,490 --> 00:38:08,760 the GDPR, Global Directive on Privacy Regulation. 773 00:38:08,760 --> 00:38:12,390 Here in the US, only California's stepped into this 774 00:38:12,390 --> 00:38:15,450 and passed something that went into place last year, 775 00:38:15,450 --> 00:38:18,540 the California Consumer Protection Act. 776 00:38:18,540 --> 00:38:21,240 So these are things that society will grapple with. 777 00:38:21,240 --> 00:38:25,560 Technology can enable privacy as well as take away privacy. 778 00:38:25,560 --> 00:38:31,360 Technology actually can enable us to keep our privacy. 779 00:38:31,360 --> 00:38:34,180 But the technology companies and the financial companies 780 00:38:34,180 --> 00:38:35,260 want our data. 781 00:38:35,260 --> 00:38:38,050 So they're not going to necessarily want 782 00:38:38,050 --> 00:38:39,550 us to keep our privacy. 783 00:38:39,550 --> 00:38:41,980 So it's an interesting-- 784 00:38:41,980 --> 00:38:44,350 technology can enable it, but technology 785 00:38:44,350 --> 00:38:47,070 can take it away as well. 786 00:38:47,070 --> 00:38:51,115 So just moving on a little bit, this three big areas-- 787 00:38:51,115 --> 00:38:52,740 we're not going to spend a lot of time, 788 00:38:52,740 --> 00:38:55,020 but artificial intelligence, machine learning. 789 00:38:55,020 --> 00:38:57,840 I love to give a shout out to another 790 00:38:57,840 --> 00:39:01,440 MITer, or Lex Fridman, who has a wonderful set 791 00:39:01,440 --> 00:39:04,950 of online courses, if you wanted to take Lex's courses. 792 00:39:04,950 --> 00:39:08,260 This was online before online became such the vogue now 793 00:39:08,260 --> 00:39:09,690 that we're all doing it. 794 00:39:09,690 --> 00:39:12,240 But Lex has this wonderful course. 795 00:39:12,240 --> 00:39:15,000 And I captured his one slide. 796 00:39:15,000 --> 00:39:17,940 What is AI and machine learning? 797 00:39:17,940 --> 00:39:21,210 It's extracting useful patterns from data. 798 00:39:21,210 --> 00:39:23,460 You don't have to be a computer scientist. 799 00:39:23,460 --> 00:39:26,640 But it's basically that's the key thing, and something 800 00:39:26,640 --> 00:39:28,620 that might not be linear, something 801 00:39:28,620 --> 00:39:32,430 that might not fit into that old statistics class and regression 802 00:39:32,430 --> 00:39:35,850 analysis or linear algebra that we think about. 803 00:39:35,850 --> 00:39:38,400 It all relies on good data, cleaning up 804 00:39:38,400 --> 00:39:41,598 data, and good questions. 805 00:39:41,598 --> 00:39:42,390 Where do we see it? 806 00:39:42,390 --> 00:39:44,970 We see it in facial recognition, image classification, 807 00:39:44,970 --> 00:39:48,210 speech recognition, et cetera, this list from Lex's list 808 00:39:48,210 --> 00:39:52,680 that you can see online, medical diagnoses-- 809 00:39:52,680 --> 00:39:54,360 in the midst of the corona crisis, 810 00:39:54,360 --> 00:39:57,360 a lot are turning to AI and machine learning to see 811 00:39:57,360 --> 00:40:00,420 what patterns can we see beyond the patterns you 812 00:40:00,420 --> 00:40:03,150 can see by classic statistics? 813 00:40:03,150 --> 00:40:05,260 It's going beyond that. 814 00:40:05,260 --> 00:40:08,010 But in this field, in this field, 815 00:40:08,010 --> 00:40:11,490 in finance, we're seeing it in every one of these areas. 816 00:40:11,490 --> 00:40:15,540 And we have two classes, so I won't dive into it now. 817 00:40:15,540 --> 00:40:17,410 But whether it's asset management, where 818 00:40:17,410 --> 00:40:20,760 BlackRock is literally taking-- 819 00:40:20,760 --> 00:40:27,880 and all the news items for the top companies, every quarter 820 00:40:27,880 --> 00:40:30,270 that a company reports its earnings, 821 00:40:30,270 --> 00:40:35,320 BlackRock is listening digitally to their shareholder meetings 822 00:40:35,320 --> 00:40:37,780 and their shareholder conference calls 823 00:40:37,780 --> 00:40:42,880 and seeing which words in there, which words relate to stock 824 00:40:42,880 --> 00:40:44,680 markets going up or stock markets 825 00:40:44,680 --> 00:40:47,950 going down, using machine learning and asset management. 826 00:40:47,950 --> 00:40:53,380 We talked about call centers and chatbots and so forth. 827 00:40:53,380 --> 00:40:57,220 I don't know how many of you are Bank of America customers. 828 00:40:57,220 --> 00:41:01,900 But bank of America has millions of its customers using Erica. 829 00:41:01,900 --> 00:41:05,120 Think the Siri of banking. 830 00:41:05,120 --> 00:41:08,560 Think the Alexa of banking, a virtual assistant 831 00:41:08,560 --> 00:41:10,740 called Erica-- 832 00:41:10,740 --> 00:41:13,230 Bank of America, get it? 833 00:41:13,230 --> 00:41:17,480 All right, that was their marketing thing, I guess, now. 834 00:41:17,480 --> 00:41:21,000 So AI and machine learning is dramatically shifting. 835 00:41:21,000 --> 00:41:23,940 The question in FinTech for the big incumbents, 836 00:41:23,940 --> 00:41:28,140 how do I do it to raise my revenues, 837 00:41:28,140 --> 00:41:31,710 lower the amount of capital that I have to use, 838 00:41:31,710 --> 00:41:33,570 raise my profits? 839 00:41:33,570 --> 00:41:36,870 The question for big tech is how do I do this maybe 840 00:41:36,870 --> 00:41:40,560 to get into the business, to leapfrog, as Ivy said, 841 00:41:40,560 --> 00:41:44,520 that Alibaba and WeChat Pay leapfrogged the Chinese banks 842 00:41:44,520 --> 00:41:45,660 and payments? 843 00:41:45,660 --> 00:41:49,200 If I'm big tech, can I leapfrog big finance? 844 00:41:49,200 --> 00:41:53,130 Because, frankly, if I'm Google, I'm better at it right now. 845 00:41:53,130 --> 00:41:55,740 Google has a comparative advantage. 846 00:41:55,740 --> 00:41:59,610 Can I maybe use my comparative advantage and AI and leapfrog? 847 00:41:59,610 --> 00:42:03,090 If I'm a startup, maybe I can give a better customer 848 00:42:03,090 --> 00:42:03,940 interface. 849 00:42:03,940 --> 00:42:07,620 I can do something with this to better manage risk 850 00:42:07,620 --> 00:42:10,260 with alternative data. 851 00:42:10,260 --> 00:42:14,800 So that's how I think of it. 852 00:42:14,800 --> 00:42:16,870 OpenAPI-- I should pause. 853 00:42:16,870 --> 00:42:18,873 Romain, any questions or hands up? 854 00:42:18,873 --> 00:42:21,040 ROMAIN DE SAINT PERIER: Yes, we have one from Laira. 855 00:42:21,040 --> 00:42:21,915 GARY GENSLER: Please. 856 00:42:24,860 --> 00:42:27,370 AUDIENCE: Yeah, so I was just curious to know how, 857 00:42:27,370 --> 00:42:31,510 internationally speaking, regulation 858 00:42:31,510 --> 00:42:36,640 hampers the capacity of FinTech companies to expand, just 859 00:42:36,640 --> 00:42:37,910 on an international level. 860 00:42:37,910 --> 00:42:43,340 So for the US, is it more regulated and, hence, more 861 00:42:43,340 --> 00:42:47,782 harder for FinTech companies to expand than for in China? 862 00:42:47,782 --> 00:42:48,990 GARY GENSLER: Great question. 863 00:42:48,990 --> 00:42:50,230 And again, who asked? 864 00:42:50,230 --> 00:42:53,090 I just didn't rem-- is Leia? 865 00:42:53,090 --> 00:42:54,130 AUDIENCE: Laira. 866 00:42:54,130 --> 00:42:57,620 GARY GENSLER: Laira, all right, Laira, good to see you again. 867 00:42:57,620 --> 00:43:00,950 I'm sorry that I don't physically see you. 868 00:43:00,950 --> 00:43:04,420 But good to see you remotely. 869 00:43:04,420 --> 00:43:07,330 Every country is taking a little bit different approach. 870 00:43:07,330 --> 00:43:12,790 And the range of approaches here could be from you're a-- 871 00:43:12,790 --> 00:43:15,250 let's call them a startup or a disruptor. 872 00:43:15,250 --> 00:43:17,080 You're starting something. 873 00:43:17,080 --> 00:43:19,020 You'd better just come into whatever 874 00:43:19,020 --> 00:43:21,160 our traditional regulatory framework. 875 00:43:21,160 --> 00:43:24,730 If you're taking deposits and offering loans, 876 00:43:24,730 --> 00:43:26,170 you've got to be a bank. 877 00:43:26,170 --> 00:43:28,000 If you're just doing payments, you 878 00:43:28,000 --> 00:43:33,520 might come under a European, US African e-money law 879 00:43:33,520 --> 00:43:37,270 and just have to do the things around money laundering 880 00:43:37,270 --> 00:43:39,010 and anti-money laundering. 881 00:43:39,010 --> 00:43:43,100 If you're like Robinhood here in the US, 882 00:43:43,100 --> 00:43:45,790 you would need to register as a broker-dealer. 883 00:43:45,790 --> 00:43:48,640 And there's been this big debate around cryptocurrencies. 884 00:43:48,640 --> 00:43:50,320 Are they securities or not? 885 00:43:50,320 --> 00:43:52,110 And in some countries, like the US, 886 00:43:52,110 --> 00:43:56,860 they generally are, unless you're 887 00:43:56,860 --> 00:43:59,860 like Bitcoin and Ethereum. 888 00:43:59,860 --> 00:44:03,610 But the debate, Lyra, is really country by country, 889 00:44:03,610 --> 00:44:11,000 is are these startups and these technologies, 890 00:44:11,000 --> 00:44:14,240 do they fit into the current regulatory regimes? 891 00:44:14,240 --> 00:44:17,210 By and large, if you take deposits and you make loans, 892 00:44:17,210 --> 00:44:20,220 you're a bank pretty much anywhere around the globe. 893 00:44:20,220 --> 00:44:22,370 If you facilitate the movement of money, 894 00:44:22,370 --> 00:44:26,390 you're probably in some e-money laws around the globe. 895 00:44:26,390 --> 00:44:29,780 Securities, if you're actually facilitating 896 00:44:29,780 --> 00:44:32,510 the raising of money and the selling of securities, 897 00:44:32,510 --> 00:44:35,480 you usually have to register as a securities broker-dealer 898 00:44:35,480 --> 00:44:37,460 somewhere around the globe. 899 00:44:37,460 --> 00:44:40,240 But a lot of places also have this concept 900 00:44:40,240 --> 00:44:44,900 of some form of regulatory forbearance called sandboxes. 901 00:44:44,900 --> 00:44:48,620 The idea is let's promote some innovation, whether it's 902 00:44:48,620 --> 00:44:51,590 in Hong Kong, whether it's in Asia, whether it's in the US, 903 00:44:51,590 --> 00:44:56,330 promote some motivation by saying if it stays small enough 904 00:44:56,330 --> 00:44:58,490 and it's new enough, you might not 905 00:44:58,490 --> 00:45:03,540 have to comply with all of the regulatory regimes. 906 00:45:03,540 --> 00:45:06,330 The other interesting challenge is sometimes things come along 907 00:45:06,330 --> 00:45:07,710 that don't fit in a box. 908 00:45:07,710 --> 00:45:10,200 They don't quite fit in to something. 909 00:45:10,200 --> 00:45:12,950 So the internet came along in the 1990s. 910 00:45:12,950 --> 00:45:16,160 Internet in the 1990s was facilitating 911 00:45:16,160 --> 00:45:18,215 a rapid change in finance. 912 00:45:18,215 --> 00:45:19,590 The internet comes along and then 913 00:45:19,590 --> 00:45:21,720 the question is literally a question 914 00:45:21,720 --> 00:45:24,150 that the securities regulators around the globe 915 00:45:24,150 --> 00:45:25,290 had to deal with-- 916 00:45:25,290 --> 00:45:26,970 what if I put a bulletin board up 917 00:45:26,970 --> 00:45:29,760 on the internet that offers people to buy and sell 918 00:45:29,760 --> 00:45:31,560 securities on the internet? 919 00:45:31,560 --> 00:45:33,150 Now, it wasn't a traditional exchange. 920 00:45:33,150 --> 00:45:37,260 It didn't look like the Tokyo or Shanghai or London or New York 921 00:45:37,260 --> 00:45:40,470 exchanges of old, where there were humans yelling 922 00:45:40,470 --> 00:45:42,750 and screaming on the floors of stock exchanges 923 00:45:42,750 --> 00:45:43,920 around the globe. 924 00:45:43,920 --> 00:45:45,990 It was just a bulletin board where 925 00:45:45,990 --> 00:45:48,060 buyers and sellers could meet. 926 00:45:48,060 --> 00:45:51,300 And usually they were insurance companies and various asset 927 00:45:51,300 --> 00:45:52,800 managers. 928 00:45:52,800 --> 00:45:55,890 That question was a ripe question in the 1990s. 929 00:45:55,890 --> 00:45:59,880 And over time, we ended up with two tiers of regulation 930 00:45:59,880 --> 00:46:00,810 for exchanges. 931 00:46:00,810 --> 00:46:03,260 We had fully-regulated exchanges, 932 00:46:03,260 --> 00:46:06,060 and this was true in Europe and the US at the time. 933 00:46:06,060 --> 00:46:09,760 China sort of got there a little later. 934 00:46:09,760 --> 00:46:11,860 But in Europe and the US, it was like all right, 935 00:46:11,860 --> 00:46:14,068 there's going to be these fully-regulated traditional 936 00:46:14,068 --> 00:46:15,010 exchanges. 937 00:46:15,010 --> 00:46:17,160 And then there would be another tier. 938 00:46:17,160 --> 00:46:19,170 In the US, we called them broker dealers, 939 00:46:19,170 --> 00:46:22,590 alternative trading systems, ATS's. 940 00:46:22,590 --> 00:46:24,630 In Europe, there were various rules 941 00:46:24,630 --> 00:46:29,220 that became known as MiFID, which now I can't remember 942 00:46:29,220 --> 00:46:30,540 what it all stands for. 943 00:46:30,540 --> 00:46:33,360 But electronic trading platforms were regulated a little 944 00:46:33,360 --> 00:46:34,317 differently. 945 00:46:34,317 --> 00:46:36,150 So I hope that gives you a sense and puts it 946 00:46:36,150 --> 00:46:38,010 in a historic concept. 947 00:46:38,010 --> 00:46:40,920 I think over the 2020s, AI and machine 948 00:46:40,920 --> 00:46:44,640 learning will lead to tremendous challenges 949 00:46:44,640 --> 00:46:50,940 around regulation, about if you see a pattern in the data 950 00:46:50,940 --> 00:46:54,360 but you can't explain why the pattern is there, 951 00:46:54,360 --> 00:46:57,360 you fall into some challenges of explainability. 952 00:46:57,360 --> 00:47:00,780 And for the last 50 years, or in many countries, 953 00:47:00,780 --> 00:47:02,520 if you deny somebody credit, you're 954 00:47:02,520 --> 00:47:06,240 supposed to be able to explain why you deny them credit. 955 00:47:06,240 --> 00:47:08,280 We talked about privacy earlier. 956 00:47:08,280 --> 00:47:11,060 It bumps up against privacy issues. 957 00:47:11,060 --> 00:47:14,380 And a third area it bumps up against is biases. 958 00:47:14,380 --> 00:47:16,050 What if there is a bias in the data, 959 00:47:16,050 --> 00:47:19,110 like when Apple Credit Card just rolled out 960 00:47:19,110 --> 00:47:22,980 and it seemed like husbands were getting more credit than wives 961 00:47:22,980 --> 00:47:25,730 in the same household? 962 00:47:25,730 --> 00:47:28,990 So biases, privacy, explainability 963 00:47:28,990 --> 00:47:32,260 are the three sort of cutting edge, when I call the big three 964 00:47:32,260 --> 00:47:36,790 public policy issues around AI and finance, though there 965 00:47:36,790 --> 00:47:38,420 are other issues as well. 966 00:47:38,420 --> 00:47:39,855 Romain, did I see you-- were you-- 967 00:47:39,855 --> 00:47:41,230 ROMAIN DE SAINT PERIER: Yes, sir. 968 00:47:41,230 --> 00:47:43,335 We have Carlos, who has his hand up. 969 00:47:43,335 --> 00:47:44,710 GARY GENSLER: All right, and then 970 00:47:44,710 --> 00:47:46,180 I'm going to keep going, because I 971 00:47:46,180 --> 00:47:49,540 want to talk about where we're going in this class as well. 972 00:47:49,540 --> 00:47:50,580 Carlos? 973 00:47:50,580 --> 00:47:52,270 AUDIENCE: Hi, how are you? 974 00:47:52,270 --> 00:47:55,330 Just a comment on regulations, sort of to build up on that. 975 00:47:55,330 --> 00:47:57,490 So I think ironically, for example, 976 00:47:57,490 --> 00:47:59,950 in Latin America, a lot of countries 977 00:47:59,950 --> 00:48:01,750 have an issue where the big banks have 978 00:48:01,750 --> 00:48:03,907 a massive concentration of deposits. 979 00:48:03,907 --> 00:48:06,490 But, for example, if you look at the Mexico FinTech law, which 980 00:48:06,490 --> 00:48:09,640 was passed end of 2018, it actually raised the barriers 981 00:48:09,640 --> 00:48:11,320 to entry for other FinTechs. 982 00:48:11,320 --> 00:48:12,808 So sort of ironically-- 983 00:48:12,808 --> 00:48:15,350 GARY GENSLER: I'm sorry, Carlos, it raised what for FinTechs? 984 00:48:15,350 --> 00:48:16,550 AUDIENCE: The barriers to entry. 985 00:48:16,550 --> 00:48:18,050 GARY GENSLER: Barriers to entry, OK. 986 00:48:18,050 --> 00:48:19,030 AUDIENCE: And so-- 987 00:48:19,030 --> 00:48:22,030 GARY GENSLER: I think our faculty member, Luis Videgaray, 988 00:48:22,030 --> 00:48:25,360 who helped work on that when he was finance minister of Mexico, 989 00:48:25,360 --> 00:48:27,430 we should ask him. 990 00:48:27,430 --> 00:48:30,010 And I'll see if I can get his answer for the next Wednesday 991 00:48:30,010 --> 00:48:31,180 or next Monday's class. 992 00:48:31,180 --> 00:48:32,570 But keep going. 993 00:48:32,570 --> 00:48:34,330 AUDIENCE: OK, that would be great. 994 00:48:34,330 --> 00:48:36,080 But the question is do you think there 995 00:48:36,080 --> 00:48:38,740 is a risk that new regulations in the FinTech scope 996 00:48:38,740 --> 00:48:41,080 are actually going to perpetuate some of the problems 997 00:48:41,080 --> 00:48:44,710 that we saw before with the more traditional banking sector? 998 00:48:44,710 --> 00:48:46,400 GARY GENSLER: Great question. 999 00:48:46,400 --> 00:48:47,980 Carlos, can I hold that for a minute, 1000 00:48:47,980 --> 00:48:50,140 because I'm going to do that when I do the actors? 1001 00:48:50,140 --> 00:48:54,540 But I think yes, incumbents in every field-- 1002 00:48:54,540 --> 00:48:57,330 and these would be incumbents in the pharmaceutical field, 1003 00:48:57,330 --> 00:49:01,470 in the tech fields, in airlines, whatever-- incumbents 1004 00:49:01,470 --> 00:49:07,050 tend to be able to deal with regulation a little easier. 1005 00:49:07,050 --> 00:49:08,700 They're big. 1006 00:49:08,700 --> 00:49:10,200 They've got great resources. 1007 00:49:10,200 --> 00:49:14,880 They can build systems to comply with those regulations. 1008 00:49:14,880 --> 00:49:17,880 Now, they don't necessarily embrace new regulation. 1009 00:49:17,880 --> 00:49:20,310 But once those regulations are put in place 1010 00:49:20,310 --> 00:49:22,770 by an official sector, they tend to have 1011 00:49:22,770 --> 00:49:24,810 the resources to embrace them. 1012 00:49:24,810 --> 00:49:27,510 And startups sometimes have more challenges. 1013 00:49:27,510 --> 00:49:30,060 And thus, you may be seeing that regulations 1014 00:49:30,060 --> 00:49:31,820 become a barrier to entry. 1015 00:49:31,820 --> 00:49:36,150 They're kind of grains in the sand of innovation at times. 1016 00:49:36,150 --> 00:49:38,310 So there's always a public policy tradeoff-- 1017 00:49:38,310 --> 00:49:40,800 protecting the public, whether it's 1018 00:49:40,800 --> 00:49:46,620 protecting the public against consumer fraud or investor 1019 00:49:46,620 --> 00:49:50,390 protection or protecting the public against systemic risk, 1020 00:49:50,390 --> 00:49:53,970 that big banks will fail and hurt the rest of the economy, 1021 00:49:53,970 --> 00:49:57,420 also comes with some tradeoffs, that it could 1022 00:49:57,420 --> 00:49:59,250 raise the barriers to entry. 1023 00:49:59,250 --> 00:50:02,625 You're absolutely right there. 1024 00:50:02,625 --> 00:50:05,610 We're going to talk a lot about OpenAPI and open banking. 1025 00:50:05,610 --> 00:50:08,040 We have a whole class on that. 1026 00:50:08,040 --> 00:50:10,980 So I'm going to keep moving on just so that we finish 1027 00:50:10,980 --> 00:50:13,230 by our 10 o'clock deadline. 1028 00:50:13,230 --> 00:50:16,110 Blockchain technology-- you've heard about it. 1029 00:50:16,110 --> 00:50:17,640 We're going to have a class on this, 1030 00:50:17,640 --> 00:50:20,040 about cryptocurrencies and blockchains. 1031 00:50:20,040 --> 00:50:24,160 Some of you actually were in our fall blockchain and money 1032 00:50:24,160 --> 00:50:24,660 class. 1033 00:50:24,660 --> 00:50:27,360 Some of you are in the crypto finance class that 1034 00:50:27,360 --> 00:50:30,170 starts in about 30 minutes. 1035 00:50:30,170 --> 00:50:32,580 But we will talk about blockchain technology. 1036 00:50:32,580 --> 00:50:34,650 I want to say and lay it out right 1037 00:50:34,650 --> 00:50:38,250 from the beginning, these two issues, AI 1038 00:50:38,250 --> 00:50:42,420 and machine learning, in these eight areas are dramatically 1039 00:50:42,420 --> 00:50:44,040 more relevant. 1040 00:50:44,040 --> 00:50:48,360 And OpenAPI and open banking, dramatically more relevant 1041 00:50:48,360 --> 00:50:53,340 than blockchain technology potential use cases as of 2020. 1042 00:50:53,340 --> 00:50:56,820 The interesting question is will that shift? 1043 00:50:56,820 --> 00:51:01,350 Is there an overabundance of investment in AI and machine 1044 00:51:01,350 --> 00:51:03,940 learning and not enough in blockchain technology? 1045 00:51:03,940 --> 00:51:06,180 You get to decide for yourselves. 1046 00:51:06,180 --> 00:51:11,610 But I'm saying as of 2020, sort of the real potential 1047 00:51:11,610 --> 00:51:14,790 that we're seeing and the dramatic changes around user 1048 00:51:14,790 --> 00:51:19,320 interface an OpenAPI and machine learning and natural language 1049 00:51:19,320 --> 00:51:22,690 processing are more dramatic in this space. 1050 00:51:22,690 --> 00:51:24,630 And yet cryptocurrencies have dramatically 1051 00:51:24,630 --> 00:51:26,830 changed what central banks are doing. 1052 00:51:26,830 --> 00:51:29,970 And we saw Facebook trying to stand up a worldwide currency 1053 00:51:29,970 --> 00:51:31,150 last year. 1054 00:51:31,150 --> 00:51:33,060 So I don't think you can adequately 1055 00:51:33,060 --> 00:51:35,910 discuss and have a course on financial technology 1056 00:51:35,910 --> 00:51:40,680 without really having some slice of blockchain technology. 1057 00:51:40,680 --> 00:51:41,760 And it is shifting. 1058 00:51:41,760 --> 00:51:45,820 Everything that's on this list, it is a catalyst for change. 1059 00:51:45,820 --> 00:51:49,080 I would say my takeaway on blockchain technology, 1060 00:51:49,080 --> 00:51:52,620 it is definitely pushing the financial sector 1061 00:51:52,620 --> 00:51:57,580 in places that wouldn't be pushed otherwise. 1062 00:51:57,580 --> 00:51:59,580 I guess that's really saying OpenAPI 1063 00:51:59,580 --> 00:52:01,780 and artificial intelligence, machine learning 1064 00:52:01,780 --> 00:52:07,353 are so much bigger in terms of what's happening in 2020. 1065 00:52:07,353 --> 00:52:08,770 Romain, unless there's a question, 1066 00:52:08,770 --> 00:52:10,572 I'm going to do the actors quickly. 1067 00:52:10,572 --> 00:52:11,072 Anything? 1068 00:52:11,072 --> 00:52:13,280 ROMAIN DE SAINT PERIER: We have one specific question 1069 00:52:13,280 --> 00:52:16,930 from [INAUDIBLE] on whether machine learning and AI can 1070 00:52:16,930 --> 00:52:20,710 cause a dark box, or creditors could deny and approve credits 1071 00:52:20,710 --> 00:52:22,115 based on unreasonable grounds. 1072 00:52:22,115 --> 00:52:22,948 How do you see that? 1073 00:52:22,948 --> 00:52:26,830 GARY GENSLER: That absolutely is a risk. 1074 00:52:26,830 --> 00:52:30,130 Our first big data revolution in the US, 1075 00:52:30,130 --> 00:52:32,310 and then it was about a decade later elsewhere, 1076 00:52:32,310 --> 00:52:35,410 was in the late '50s and early '60s, 1077 00:52:35,410 --> 00:52:38,810 credit cards came along, invented really 1078 00:52:38,810 --> 00:52:44,330 in the late 1940s and then popularized by the mid 1960s, 1079 00:52:44,330 --> 00:52:44,990 came along. 1080 00:52:44,990 --> 00:52:47,090 And then the official sector passed laws. 1081 00:52:47,090 --> 00:52:50,570 And two of the first laws we passed in the US 1082 00:52:50,570 --> 00:52:52,700 was something called the Fair Credit Reporting 1083 00:52:52,700 --> 00:52:55,070 Act and the Equal Credit Opportunity Act. 1084 00:52:55,070 --> 00:52:58,010 And why I speak about those two and this question 1085 00:52:58,010 --> 00:53:00,560 50 years later is this was a question 1086 00:53:00,560 --> 00:53:03,930 with the first big data analytics at that time. 1087 00:53:03,930 --> 00:53:07,070 And the idea was you couldn't-- you couldn't use data analytics 1088 00:53:07,070 --> 00:53:11,240 to deny somebody credit because of their race, 1089 00:53:11,240 --> 00:53:13,580 because of their color, because their ethnicity, 1090 00:53:13,580 --> 00:53:18,812 because of their gender and other protected attributes. 1091 00:53:18,812 --> 00:53:21,020 And that was called the Equal Credit Opportunity Act. 1092 00:53:21,020 --> 00:53:24,320 That same act is important now as we move into machine 1093 00:53:24,320 --> 00:53:27,230 learning credit decisions. 1094 00:53:27,230 --> 00:53:30,260 Fair Credit Reporting Act also said in the US, 1095 00:53:30,260 --> 00:53:33,230 and Europe did some similar things elsewhere later, 1096 00:53:33,230 --> 00:53:37,810 said if you deny credit, you have to be able to explain why. 1097 00:53:37,810 --> 00:53:42,000 So to this question, just because you have a black box, 1098 00:53:42,000 --> 00:53:46,890 you still need to have those basic tenets of explainability 1099 00:53:46,890 --> 00:53:49,890 and fairness or lack of bias. 1100 00:53:49,890 --> 00:53:52,470 And that's why I say the three big challenges are 1101 00:53:52,470 --> 00:53:56,350 explainability, bias, and then privacy. 1102 00:53:56,350 --> 00:53:59,605 There's also challenges of robustness and so forth, 1103 00:53:59,605 --> 00:54:02,280 but great question. 1104 00:54:02,280 --> 00:54:04,590 The actors-- I think of the actors-- 1105 00:54:04,590 --> 00:54:09,180 we've talked about this a little bit in several buckets. 1106 00:54:09,180 --> 00:54:11,460 I think of big finance-- 1107 00:54:11,460 --> 00:54:13,890 I apologize, I sort of borrowed this a little bit 1108 00:54:13,890 --> 00:54:15,950 from the central bank governor of Brazil. 1109 00:54:15,950 --> 00:54:17,670 I met with him last year. 1110 00:54:17,670 --> 00:54:20,220 And he and I were talking about Brazilian banking. 1111 00:54:20,220 --> 00:54:22,170 And he said they're like fortresses. 1112 00:54:22,170 --> 00:54:26,680 So this was actually his kind of articulation of Itaú 1113 00:54:26,680 --> 00:54:28,185 and the others in Brazil. 1114 00:54:28,185 --> 00:54:30,060 And I said how do you see them as fortresses? 1115 00:54:30,060 --> 00:54:33,780 And he says they all have their moats, towers, 1116 00:54:33,780 --> 00:54:35,930 and they have sovereign protection. 1117 00:54:35,930 --> 00:54:39,420 And to him-- and I liked it so much I repeat it-- 1118 00:54:39,420 --> 00:54:42,390 their towers, their sort of basic tenets 1119 00:54:42,390 --> 00:54:46,320 of sort of financial strength is around payments. 1120 00:54:46,320 --> 00:54:48,310 They usually control payments. 1121 00:54:48,310 --> 00:54:50,820 They have big balance sheets that they can use. 1122 00:54:50,820 --> 00:54:55,860 And balance sheets allow you to lower your risk, frankly, 1123 00:54:55,860 --> 00:54:56,910 and leverage. 1124 00:54:56,910 --> 00:54:58,380 They have a lot of data. 1125 00:54:58,380 --> 00:55:00,430 And yes, they have hundreds of legal entities. 1126 00:55:00,430 --> 00:55:05,220 Their corporate structure is one of their both complexities 1127 00:55:05,220 --> 00:55:06,810 but benefits. 1128 00:55:06,810 --> 00:55:09,780 A company like a JPMorgan or a Goldman Sachs 1129 00:55:09,780 --> 00:55:14,040 or Barclays at a minimum has probably 1,000 legal entities 1130 00:55:14,040 --> 00:55:16,260 and might have 3,000 or 5,000 legal entities. 1131 00:55:16,260 --> 00:55:19,380 When I left Goldman Sachs, which was already 22 years ago, 1132 00:55:19,380 --> 00:55:23,310 I was the co-head of finance with David Viniar, who 1133 00:55:23,310 --> 00:55:28,380 went on to be the CFO, we had 700 legal entities. 1134 00:55:28,380 --> 00:55:31,710 But if I recall, in that quarter of a trillion balance sheet 1135 00:55:31,710 --> 00:55:34,140 that we had to sort of help manage and fund, 1136 00:55:34,140 --> 00:55:38,040 only about 70 or 80 of those were regulated companies. 1137 00:55:38,040 --> 00:55:39,840 So we had a lot-- 1138 00:55:39,840 --> 00:55:42,190 we did everything legal, I just want to say-- 1139 00:55:42,190 --> 00:55:45,570 but we had a lot going on amongst those 700 1140 00:55:45,570 --> 00:55:46,920 legal entities. 1141 00:55:46,920 --> 00:55:50,970 That's kind of big finance from a sort of central bank 1142 00:55:50,970 --> 00:55:54,150 governor of Brazil's point of view, like fortresses. 1143 00:55:54,150 --> 00:55:56,420 But then big tech-- 1144 00:55:56,420 --> 00:55:57,980 the Bank of International Settlements 1145 00:55:57,980 --> 00:55:59,740 did a wonderful report last year. 1146 00:55:59,740 --> 00:56:01,680 And they said it's like DNA-- 1147 00:56:01,680 --> 00:56:04,700 data, networks, activities. 1148 00:56:04,700 --> 00:56:09,920 And if you're Alibaba or you're Facebook, 1149 00:56:09,920 --> 00:56:13,580 you want to layer another activity on your network. 1150 00:56:13,580 --> 00:56:15,860 Facebook already has 2 plus billion people 1151 00:56:15,860 --> 00:56:17,130 in their network. 1152 00:56:17,130 --> 00:56:19,430 They have a lot of data already. 1153 00:56:19,430 --> 00:56:22,530 They are supposedly a free app. 1154 00:56:22,530 --> 00:56:25,110 They are free if you download it. 1155 00:56:25,110 --> 00:56:27,200 But it's data for services. 1156 00:56:27,200 --> 00:56:29,580 And if they can put another activity on top of it, 1157 00:56:29,580 --> 00:56:32,190 that means they get more data. 1158 00:56:32,190 --> 00:56:35,210 So every time they add an activity, more data. 1159 00:56:35,210 --> 00:56:38,010 And data they can commercialize. 1160 00:56:38,010 --> 00:56:42,870 And so that DNA network is why you see big tech trying 1161 00:56:42,870 --> 00:56:45,240 to get in payments around the globe, 1162 00:56:45,240 --> 00:56:48,790 and then adding credit products on top of it. 1163 00:56:48,790 --> 00:56:51,360 Startups-- startups have advantages. 1164 00:56:51,360 --> 00:56:53,250 Don't count them out. 1165 00:56:53,250 --> 00:56:55,290 Some people would just call that the FinTech. 1166 00:56:55,290 --> 00:56:56,790 But they're flexible. 1167 00:56:56,790 --> 00:56:58,530 They're disruptive innovators. 1168 00:56:58,530 --> 00:57:02,670 They can sort of rent their data storage on the cloud. 1169 00:57:02,670 --> 00:57:05,250 In some circumstances, they can rent their balance sheets 1170 00:57:05,250 --> 00:57:07,530 by doing securitizations. 1171 00:57:07,530 --> 00:57:10,240 They also have some asymmetric risks. 1172 00:57:10,240 --> 00:57:13,930 And that asymmetric risk we'll talk about all half semester. 1173 00:57:13,930 --> 00:57:18,390 The important asymmetries they have is one, 1174 00:57:18,390 --> 00:57:21,100 they're not protecting a business model. 1175 00:57:21,100 --> 00:57:23,100 So let's just talk about payments and credit 1176 00:57:23,100 --> 00:57:25,000 cards for a minute. 1177 00:57:25,000 --> 00:57:28,820 The big banks are protecting a very profitable credit card 1178 00:57:28,820 --> 00:57:29,320 business. 1179 00:57:29,320 --> 00:57:32,040 And there's seven big banks in the US. 1180 00:57:32,040 --> 00:57:34,780 There's seven big actors in the credit card space-- 1181 00:57:34,780 --> 00:57:38,230 Bank of America and Chase and Citi, of course, 1182 00:57:38,230 --> 00:57:43,750 but also American Express and Discover and the like, Cap One. 1183 00:57:43,750 --> 00:57:45,980 They're protecting that business model. 1184 00:57:45,980 --> 00:57:47,950 But then somebody comes along. 1185 00:57:47,950 --> 00:57:51,490 Maybe it's a small company like Toast in the payment system 1186 00:57:51,490 --> 00:57:54,520 space for restaurant payments. 1187 00:57:54,520 --> 00:57:57,580 And before corona crisis, Toast was doing pretty well. 1188 00:57:57,580 --> 00:58:00,340 And they did a C or series C or D round 1189 00:58:00,340 --> 00:58:03,100 at $4.9 billion valuation. 1190 00:58:03,100 --> 00:58:05,980 Well, Toast can come along and provide a payment product 1191 00:58:05,980 --> 00:58:09,250 for the restaurant business. 1192 00:58:09,250 --> 00:58:11,860 They're not protecting any business model. 1193 00:58:11,860 --> 00:58:14,170 Or even Lending Club that came along 10 1194 00:58:14,170 --> 00:58:18,490 or 11 years ago can come into the personal loan space 1195 00:58:18,490 --> 00:58:22,000 and say we're not protecting wider profit 1196 00:58:22,000 --> 00:58:25,030 margins and interest rate margins in the credit card 1197 00:58:25,030 --> 00:58:26,320 space. 1198 00:58:26,320 --> 00:58:28,840 You can come into the personal lending space, 1199 00:58:28,840 --> 00:58:31,660 which is growing dramatically. 1200 00:58:31,660 --> 00:58:36,460 Personal lending in the US is about $170 billion asset class. 1201 00:58:36,460 --> 00:58:38,890 Credit cards is $1 trillion. 1202 00:58:38,890 --> 00:58:41,760 So it's only one sixth the size, but the personal loan space 1203 00:58:41,760 --> 00:58:45,540 is growing rapidly, in part because those actors 1204 00:58:45,540 --> 00:58:47,460 in the disruptive startup space are not 1205 00:58:47,460 --> 00:58:51,040 protecting the trillion-dollar asset class, which 1206 00:58:51,040 --> 00:58:53,700 is called credit cards. 1207 00:58:53,700 --> 00:58:55,750 And then there's the official sector. 1208 00:58:55,750 --> 00:58:59,520 So I think of these actors as, importantly, all of them. 1209 00:58:59,520 --> 00:59:01,350 And just some pictures, just for fun-- 1210 00:59:01,350 --> 00:59:03,550 we don't need to stop, but big finance. 1211 00:59:03,550 --> 00:59:06,150 And, of course, I left companies out. 1212 00:59:06,150 --> 00:59:08,580 But to give you a sense, it's an asset management, 1213 00:59:08,580 --> 00:59:11,720 like BlackRock and Fidelity and Vanguard. 1214 00:59:11,720 --> 00:59:13,170 It's in banking. 1215 00:59:13,170 --> 00:59:14,460 It's in investment banking. 1216 00:59:14,460 --> 00:59:15,660 It's global. 1217 00:59:15,660 --> 00:59:18,780 If I left your country or your favorite company out, 1218 00:59:18,780 --> 00:59:19,680 I apologize. 1219 00:59:19,680 --> 00:59:23,460 But I could have put 200, 500 companies on this page. 1220 00:59:23,460 --> 00:59:26,820 But then there's also big tech, which I only 1221 00:59:26,820 --> 00:59:28,530 picked six or seven at the top. 1222 00:59:28,530 --> 00:59:30,720 And then the startups-- and we're 1223 00:59:30,720 --> 00:59:34,380 going to talk about startups in every one of our classes. 1224 00:59:34,380 --> 00:59:37,050 But I think you've got to sort of bear with me 1225 00:59:37,050 --> 00:59:41,750 and think about it more broadly as well. 1226 00:59:41,750 --> 00:59:43,500 And then I'm just going to close before we 1227 00:59:43,500 --> 00:59:47,990 talk about our actual course and so forth, is 1228 00:59:47,990 --> 00:59:49,140 where's the investments? 1229 00:59:49,140 --> 00:59:51,810 Accenture puts out this wonderful report, 1230 00:59:51,810 --> 00:59:55,140 I think on a quarterly basis, as to the number 1231 00:59:55,140 --> 00:59:59,200 of deals in different sectors and then the funding. 1232 00:59:59,200 --> 01:00:01,920 And I don't ask you to study this on your screen now. 1233 01:00:01,920 --> 01:00:02,760 But think about it. 1234 01:00:02,760 --> 01:00:06,390 Maybe pull up the Accenture report itself. 1235 01:00:06,390 --> 01:00:10,350 But the big bulk of it is in payments and credit. 1236 01:00:10,350 --> 01:00:14,760 If you look at the kind of purplish blue boxes, nearly 50% 1237 01:00:14,760 --> 01:00:17,280 of the funding is in those boxes. 1238 01:00:17,280 --> 01:00:20,010 Insurance, pretty good size, too. 1239 01:00:20,010 --> 01:00:24,270 But it gives you the sectors that actual funding is going on 1240 01:00:24,270 --> 01:00:25,920 in this marketplace. 1241 01:00:25,920 --> 01:00:28,662 Romain, questions? 1242 01:00:28,662 --> 01:00:29,620 AUDIENCE: No questions. 1243 01:00:29,620 --> 01:00:31,930 GARY GENSLER: My god, Romain, where did you 1244 01:00:31,930 --> 01:00:34,630 get this picture taken anyway that I grabbed off 1245 01:00:34,630 --> 01:00:36,645 the internet? 1246 01:00:36,645 --> 01:00:38,770 ROMAIN DE SAINT PERIER: That was when I was working 1247 01:00:38,770 --> 01:00:41,020 in the Middle East for BCG. 1248 01:00:41,020 --> 01:00:42,970 GARY GENSLER: I see, I see. 1249 01:00:42,970 --> 01:00:45,670 All right, so you've met Romain. 1250 01:00:45,670 --> 01:00:46,650 You've met myself. 1251 01:00:46,650 --> 01:00:49,630 Lena is the course administrator. 1252 01:00:49,630 --> 01:00:52,060 If you want to set up office hours-- and, yes, 1253 01:00:52,060 --> 01:00:54,760 I am committed to office hours-- 1254 01:00:54,760 --> 01:00:56,890 it's great to also copy Lena. 1255 01:00:56,890 --> 01:00:58,740 You can probably do it with me, too. 1256 01:00:58,740 --> 01:01:05,320 But Lena is going to be better to sign it up as well. 1257 01:01:05,320 --> 01:01:07,600 The course-- so after this intro, 1258 01:01:07,600 --> 01:01:09,420 we're going to take the technologies. 1259 01:01:09,420 --> 01:01:12,670 We're going to take two classes on artificial intelligence, 1260 01:01:12,670 --> 01:01:14,980 machine learning, natural language processing 1261 01:01:14,980 --> 01:01:19,000 and the like, and then talk about the customer interface 1262 01:01:19,000 --> 01:01:22,270 on April 8 and then blockchain and technology. 1263 01:01:22,270 --> 01:01:24,730 These three slices-- now, there are other slices 1264 01:01:24,730 --> 01:01:27,050 we could address as well. 1265 01:01:27,050 --> 01:01:29,290 So then we're going to go through the sectors. 1266 01:01:29,290 --> 01:01:31,090 We're going to talk about these sectors. 1267 01:01:31,090 --> 01:01:35,830 And what I could see is payment and credit, trading, a little 1268 01:01:35,830 --> 01:01:37,570 less on the risk management. 1269 01:01:37,570 --> 01:01:42,830 So maybe on May 4, we'll squeeze that down a little bit as well. 1270 01:01:42,830 --> 01:01:44,470 And then, of course, we have the intro. 1271 01:01:44,470 --> 01:01:47,530 I've just, [INAUDIBLE] and I, revised the syllabus 1272 01:01:47,530 --> 01:01:48,700 this past week. 1273 01:01:48,700 --> 01:01:51,430 I want to take the next-to-last class 1274 01:01:51,430 --> 01:01:53,540 and just talk about corona crisis 1275 01:01:53,540 --> 01:01:56,500 and how it might be shifting the landscape. 1276 01:01:56,500 --> 01:01:59,340 I already said, I really do think this shifting, 1277 01:01:59,340 --> 01:02:05,110 this deep trend towards online from bricks and mortar, 1278 01:02:05,110 --> 01:02:07,190 that was already happening. 1279 01:02:07,190 --> 01:02:09,910 But we've even seen in the last three weeks, 1280 01:02:09,910 --> 01:02:11,900 we've seen winners and losers. 1281 01:02:11,900 --> 01:02:14,110 I asked each of you to think about this. 1282 01:02:14,110 --> 01:02:19,410 Who are the winners and losers within the financial sector, 1283 01:02:19,410 --> 01:02:22,200 big tech, and the financial startups? 1284 01:02:22,200 --> 01:02:23,670 I talked a little bit about Toast. 1285 01:02:23,670 --> 01:02:27,120 Toast is a very successful Boston company 1286 01:02:27,120 --> 01:02:29,340 that is providing payment services 1287 01:02:29,340 --> 01:02:31,163 and credit to restaurants. 1288 01:02:31,163 --> 01:02:33,330 For a moment, that would have to-- you'd have to say 1289 01:02:33,330 --> 01:02:35,670 that's a company that's taking-- 1290 01:02:35,670 --> 01:02:38,940 taking it on the chin, so to speak, not as badly 1291 01:02:38,940 --> 01:02:41,520 as all those individuals that have health care worries, 1292 01:02:41,520 --> 01:02:43,710 that are ending up in the hospitals and the families 1293 01:02:43,710 --> 01:02:45,160 losing loved ones. 1294 01:02:45,160 --> 01:02:47,940 But I'm saying from an economic perspective, 1295 01:02:47,940 --> 01:02:49,470 there are some winners and losers. 1296 01:02:49,470 --> 01:02:55,440 Robinhood, an online app, mobile app for trading, 1297 01:02:55,440 --> 01:02:58,860 has crashed several times in the last three weeks. 1298 01:02:58,860 --> 01:03:00,510 And there's some data out of Europe 1299 01:03:00,510 --> 01:03:06,810 already that online FinTech apps as a sector have 1300 01:03:06,810 --> 01:03:12,450 seen usage up 70% and 100%, and some apps up 700%. 1301 01:03:12,450 --> 01:03:14,130 But not all will be winners. 1302 01:03:14,130 --> 01:03:16,020 Not all will be losers. 1303 01:03:16,020 --> 01:03:19,800 And so I thought we'll take one class towards the end 1304 01:03:19,800 --> 01:03:23,850 and just discuss it and get your feelings and thoughts 1305 01:03:23,850 --> 01:03:29,080 as well going forward how this might play out. 1306 01:03:29,080 --> 01:03:32,130 So MIT chose this as pass-fail. 1307 01:03:32,130 --> 01:03:36,060 So welcome to not only remote learning MIT, 1308 01:03:36,060 --> 01:03:39,420 but technically pass emergency, no-credit emergency, 1309 01:03:39,420 --> 01:03:41,100 incomplete emergency. 1310 01:03:41,100 --> 01:03:44,940 Just so that you understand what this is, it's almost pass-fail. 1311 01:03:44,940 --> 01:03:47,940 Pass emergency, PE, will be on your transcript, 1312 01:03:47,940 --> 01:03:49,990 hopefully for all of you. 1313 01:03:49,990 --> 01:03:52,530 I can't commit to it, because it's up to you 1314 01:03:52,530 --> 01:03:54,060 whether you pass. 1315 01:03:54,060 --> 01:03:56,160 There are assignments. 1316 01:03:56,160 --> 01:04:00,660 And no credit emergency, you can think of is an F, 1317 01:04:00,660 --> 01:04:03,700 but it's not going to be on your transcript. 1318 01:04:03,700 --> 01:04:07,430 So this is an emergency circumstance right now. 1319 01:04:07,430 --> 01:04:09,440 There's still assignments because we 1320 01:04:09,440 --> 01:04:12,380 want to give you the most learning 1321 01:04:12,380 --> 01:04:13,380 experience you can have. 1322 01:04:13,380 --> 01:04:15,672 You might say wait a minute, wait a minute, I thought-- 1323 01:04:15,672 --> 01:04:18,020 I thought assignments were just about so that a faculty 1324 01:04:18,020 --> 01:04:22,370 member can decide who gets A's and who gets B's and the like. 1325 01:04:22,370 --> 01:04:24,830 I look at assignments in a different way than that. 1326 01:04:24,830 --> 01:04:27,080 I look at assignments really as a way 1327 01:04:27,080 --> 01:04:29,710 to help you engage in this subject. 1328 01:04:29,710 --> 01:04:31,040 And so in this class-- 1329 01:04:31,040 --> 01:04:32,870 and those of you who know me, I do 1330 01:04:32,870 --> 01:04:36,050 this in a couple of classes-- is one individual paper, one group 1331 01:04:36,050 --> 01:04:36,750 paper. 1332 01:04:36,750 --> 01:04:38,870 And it's all geared to writing a group 1333 01:04:38,870 --> 01:04:43,560 paper for either a big incumbent, Bank of America, 1334 01:04:43,560 --> 01:04:49,170 a big tech company, Jeff Bezos and Amazon, or kind 1335 01:04:49,170 --> 01:04:53,870 of a startup company, a big VC company, Andreesen Horowitz, 1336 01:04:53,870 --> 01:04:56,550 that you form groups and you decide on a sector. 1337 01:04:56,550 --> 01:05:00,360 You can decide on credit or payment or trading. 1338 01:05:00,360 --> 01:05:04,230 You choose, and it helps you engage in the subject. 1339 01:05:04,230 --> 01:05:07,170 And then I ask you to split, if it's a three or four-person 1340 01:05:07,170 --> 01:05:09,300 team, or even if it's a one-person team, 1341 01:05:09,300 --> 01:05:14,100 if you choose to do that, because we're all so separated, 1342 01:05:14,100 --> 01:05:18,030 then split up and write a three or four page paper, 1343 01:05:18,030 --> 01:05:24,080 900 words or so, on one of the topics that I lay out here-- 1344 01:05:24,080 --> 01:05:28,620 the traditional competitors, the startup competitors 1345 01:05:28,620 --> 01:05:33,450 and so forth, the technology that you're interested in. 1346 01:05:33,450 --> 01:05:36,810 Why do I still do assignments when it's PE NE? 1347 01:05:36,810 --> 01:05:38,970 It's to help you engage in this subject. 1348 01:05:38,970 --> 01:05:42,900 And Romain and I are committed, even if we slap a PE on most 1349 01:05:42,900 --> 01:05:45,090 of these-- hopefully all of them-- 1350 01:05:45,090 --> 01:05:46,890 we're trying to give you feedback 1351 01:05:46,890 --> 01:05:48,390 so you engage in a subject. 1352 01:05:48,390 --> 01:05:53,460 And yes, I'm willing to do Zoom group meetings, Zoom 1353 01:05:53,460 --> 01:05:55,390 individual meetings. 1354 01:05:55,390 --> 01:05:58,610 Look, this is not an easy time for any of us. 1355 01:05:58,610 --> 01:06:01,410 But I want to make sure that I deliver as much as I can 1356 01:06:01,410 --> 01:06:03,750 and that MIT continues to deliver 1357 01:06:03,750 --> 01:06:08,460 for you as we're going through this sort of challenging time. 1358 01:06:08,460 --> 01:06:11,800 Class participation is still important. 1359 01:06:11,800 --> 01:06:15,330 If you can't sign up, if it's the time zone doesn't work 1360 01:06:15,330 --> 01:06:18,740 or there's something going on in your family, it doesn't-- 1361 01:06:18,740 --> 01:06:22,200 if you're interviewing for a job, God bless, 1362 01:06:22,200 --> 01:06:23,970 then listen to the recording. 1363 01:06:23,970 --> 01:06:29,470 We'll put the recordings up on Canvas as well. 1364 01:06:29,470 --> 01:06:32,400 And so professionalism, I just want to say something. 1365 01:06:32,400 --> 01:06:32,550 ROMAIN DE SAINT PERIER: Gary? 1366 01:06:32,550 --> 01:06:33,240 GARY GENSLER: Yeah? 1367 01:06:33,240 --> 01:06:35,698 ROMAIN DE SAINT PERIER: Excuse me, just on the assignments, 1368 01:06:35,698 --> 01:06:37,470 I'm being asked whether listeners also 1369 01:06:37,470 --> 01:06:39,210 have to comply with these assignments. 1370 01:06:39,210 --> 01:06:40,933 GARY GENSLER: No. 1371 01:06:40,933 --> 01:06:42,350 ROMAIN DE SAINT PERIER: Thank you. 1372 01:06:45,278 --> 01:06:48,220 GARY GENSLER: I'm trying to get rid of the poll here. 1373 01:06:48,220 --> 01:06:48,720 OK. 1374 01:06:52,140 --> 01:06:55,230 I've never been asked that question. 1375 01:06:55,230 --> 01:06:57,000 A little word on professionalism, 1376 01:06:57,000 --> 01:07:01,680 just for my, God knows, 30 plus years in business. 1377 01:07:01,680 --> 01:07:04,050 And this is just sort of my closing thing, 1378 01:07:04,050 --> 01:07:08,160 is my advice for all of us is success 1379 01:07:08,160 --> 01:07:11,890 goes for those prepared, curious, and self-starters. 1380 01:07:11,890 --> 01:07:13,950 If you read the assignments for this, 1381 01:07:13,950 --> 01:07:15,970 you'll do better in this class. 1382 01:07:15,970 --> 01:07:18,720 But if you go into a meeting, if you go into a pitch, 1383 01:07:18,720 --> 01:07:23,190 you go into an interview, if you read about the person, 1384 01:07:23,190 --> 01:07:25,122 you're going to learn more about them. 1385 01:07:25,122 --> 01:07:26,580 And, by the way, if you're curious, 1386 01:07:26,580 --> 01:07:28,270 when you walk into somebody's office, 1387 01:07:28,270 --> 01:07:34,190 whether it's a video office or a real office, look at the walls. 1388 01:07:34,190 --> 01:07:36,440 What does it mean that Gary Gensler has 1389 01:07:36,440 --> 01:07:38,570 this stuff behind him or not? 1390 01:07:38,570 --> 01:07:41,360 I'm not asking you to analyze me right now. 1391 01:07:41,360 --> 01:07:44,570 But I'm telling you, if you walk into somebody's office, 1392 01:07:44,570 --> 01:07:48,020 whether they're a US senator, the president of the United 1393 01:07:48,020 --> 01:07:52,220 States, some job interview, a colleague, and you look 1394 01:07:52,220 --> 01:07:54,440 at their walls and you ask them about their families, 1395 01:07:54,440 --> 01:07:55,640 show some curiosity. 1396 01:07:55,640 --> 01:07:58,040 You'll do better off. 1397 01:07:58,040 --> 01:08:01,460 As I said, respect and courtesy builds reputation and networks 1398 01:08:01,460 --> 01:08:04,380 and so forth. 1399 01:08:04,380 --> 01:08:05,250 Engage. 1400 01:08:05,250 --> 01:08:08,580 It's going to be hard with this many students online, 1401 01:08:08,580 --> 01:08:10,270 but engage in this class. 1402 01:08:10,270 --> 01:08:12,600 You'll learn more if you engage with me 1403 01:08:12,600 --> 01:08:19,140 also offline, Gensler@MIT.edu, but also engage 1404 01:08:19,140 --> 01:08:21,840 by setting up office time. 1405 01:08:21,840 --> 01:08:25,210 I also think understanding both strategy and detail matter. 1406 01:08:25,210 --> 01:08:27,779 Some people are really good tactical people, 1407 01:08:27,779 --> 01:08:30,500 really good detail folks. 1408 01:08:30,500 --> 01:08:32,000 They'll do fine in their careers. 1409 01:08:32,000 --> 01:08:34,609 But if you step back and understand the trends as well, 1410 01:08:34,609 --> 01:08:35,850 you'll do better. 1411 01:08:35,850 --> 01:08:37,760 Some people are really global strategists 1412 01:08:37,760 --> 01:08:39,500 and not really good at the details. 1413 01:08:39,500 --> 01:08:41,270 They'll probably do OK. 1414 01:08:41,270 --> 01:08:42,870 But I'll tell you, my experience, 1415 01:08:42,870 --> 01:08:46,430 whether it's in finance, whether it's watching people 1416 01:08:46,430 --> 01:08:51,040 at MIT and the faculty, or my time in public service, 1417 01:08:51,040 --> 01:08:54,319 the people that can merge both broad strategy 1418 01:08:54,319 --> 01:08:56,870 and can execute on the details, those folks 1419 01:08:56,870 --> 01:08:58,538 are unstoppable often. 1420 01:08:58,538 --> 01:09:00,080 Those are folks that you really-- you 1421 01:09:00,080 --> 01:09:03,770 want them on your team, that they can do a bit of both. 1422 01:09:03,770 --> 01:09:06,859 So we're going to talk a lot about strategy and the trends. 1423 01:09:06,859 --> 01:09:10,231 But we're going to get into the granular details as well. 1424 01:09:10,231 --> 01:09:11,689 And then lastly, it's always better 1425 01:09:11,689 --> 01:09:13,520 to stay true to your values. 1426 01:09:13,520 --> 01:09:15,350 Now, I do say this-- 1427 01:09:15,350 --> 01:09:16,630 it's going to be a little-- 1428 01:09:16,630 --> 01:09:18,130 we're in this pass-fail thing. 1429 01:09:18,130 --> 01:09:21,320 But if you want to know one way that Romain and I, 1430 01:09:21,320 --> 01:09:23,569 it will drive us a little nutty, we're 1431 01:09:23,569 --> 01:09:26,660 going to try to put your papers through some plagiarism 1432 01:09:26,660 --> 01:09:27,680 software. 1433 01:09:27,680 --> 01:09:29,420 I use Grammarly. 1434 01:09:29,420 --> 01:09:32,750 I actually check, yes. 1435 01:09:32,750 --> 01:09:36,859 And if you have one like eight or ten-word section 1436 01:09:36,859 --> 01:09:38,660 that you pulled off of Wikipedia, 1437 01:09:38,660 --> 01:09:42,410 and I've had students grab the first 15 words off of Wikipedia 1438 01:09:42,410 --> 01:09:45,080 and put it right in their paper, that's 1439 01:09:45,080 --> 01:09:46,880 kind of a sloppy thing to do. 1440 01:09:46,880 --> 01:09:49,279 And usually, if I was grading, that paper 1441 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. 1442 01:09:52,279 --> 01:09:56,390 And I'm not going to be a hard-nosed guy about 10 words. 1443 01:09:56,390 --> 01:09:59,870 But if you extensively plagiarize a couple hundred 1444 01:09:59,870 --> 01:10:02,930 words in a 900-word paper, you're 1445 01:10:02,930 --> 01:10:05,300 going to get an F on the paper. 1446 01:10:05,300 --> 01:10:07,370 Now, you can recover on the group paper. 1447 01:10:07,370 --> 01:10:09,417 But don't plagiarize on your group paper 1448 01:10:09,417 --> 01:10:11,750 because you're also going to bring down your colleagues. 1449 01:10:11,750 --> 01:10:15,080 I don't know in pass-fail land what I will do. 1450 01:10:15,080 --> 01:10:18,770 But if somebody really is trying to test the limit, 1451 01:10:18,770 --> 01:10:22,100 you would test it by extensive plagiarism. 1452 01:10:22,100 --> 01:10:22,820 Enough on that. 1453 01:10:22,820 --> 01:10:26,330 I've had it in the past occasionally. 1454 01:10:26,330 --> 01:10:30,380 So I got to say it. 1455 01:10:30,380 --> 01:10:32,750 I would say speak up in class if you can. 1456 01:10:32,750 --> 01:10:34,940 Keep your videos on, as most of you have. 1457 01:10:34,940 --> 01:10:36,770 Keep your audios muted. 1458 01:10:36,770 --> 01:10:38,360 But please speak up. 1459 01:10:38,360 --> 01:10:44,440 I mean, don't be hesitant at all. 1460 01:10:44,440 --> 01:10:47,050 And then I do have office hours. 1461 01:10:47,050 --> 01:10:50,800 And the crazy thing was I set up office lunches. 1462 01:10:50,800 --> 01:10:54,860 So on those following Tuesdays and Thursdays, I had-- 1463 01:10:54,860 --> 01:10:56,590 there's a spreadsheet. 1464 01:10:56,590 --> 01:11:00,160 Romain, maybe we should send it around to this group. 1465 01:11:00,160 --> 01:11:03,520 There's a Google Spreadsheet if you wanted to sign up. 1466 01:11:03,520 --> 01:11:08,390 And right before we left, before SIP week, a student said hey, 1467 01:11:08,390 --> 01:11:10,430 I'm signed up for the 31. 1468 01:11:10,430 --> 01:11:12,020 Will you still do the lunches? 1469 01:11:12,020 --> 01:11:12,770 I laughed. 1470 01:11:12,770 --> 01:11:15,870 I said listen, I'm willing to do it. 1471 01:11:15,870 --> 01:11:17,750 I'm willing to do remote lunches. 1472 01:11:17,750 --> 01:11:20,480 It's crazy. 1473 01:11:20,480 --> 01:11:23,540 So if you want it-- if nobody signs up, I'm fine. 1474 01:11:23,540 --> 01:11:25,310 I'll go for a run. 1475 01:11:25,310 --> 01:11:28,190 I live in Baltimore and I'm still able to run here. 1476 01:11:28,190 --> 01:11:30,510 They might shut that down, too, at some point in time. 1477 01:11:30,510 --> 01:11:33,210 But for now, I'm able to do my runs. 1478 01:11:33,210 --> 01:11:36,410 So I think that's it on the slides. 1479 01:11:36,410 --> 01:11:42,570 And then I think I'm supposed to finish this class right now. 1480 01:11:42,570 --> 01:11:43,288 I went over. 1481 01:11:43,288 --> 01:11:44,330 I want to thank you all-- 1482 01:11:44,330 --> 01:11:45,460 ROMAIN DE SAINT PERIER: Maybe just one-- 1483 01:11:45,460 --> 01:11:46,470 GARY GENSLER: What's that? 1484 01:11:46,470 --> 01:11:47,580 ROMAIN DE SAINT PERIER: Just one clarification, 1485 01:11:47,580 --> 01:11:49,230 because I'm getting a lot of questions on the group 1486 01:11:49,230 --> 01:11:49,860 formation. 1487 01:11:49,860 --> 01:11:52,940 So groups will be from three to four students, 1488 01:11:52,940 --> 01:11:55,650 and you are supposed to group yourselves, 1489 01:11:55,650 --> 01:11:57,255 right, through the Canvas function. 1490 01:11:57,255 --> 01:11:58,630 If that doesn't work out for you, 1491 01:11:58,630 --> 01:12:00,210 we're going to send out a link where 1492 01:12:00,210 --> 01:12:02,700 you can find other team members who also 1493 01:12:02,700 --> 01:12:04,390 are looking for team members. 1494 01:12:04,390 --> 01:12:06,533 So do not worry about the group formation. 1495 01:12:06,533 --> 01:12:07,950 GARY GENSLER: And I thank you all. 1496 01:12:07,950 --> 01:12:09,960 I know this is unusual. 1497 01:12:09,960 --> 01:12:13,690 I see you on Wednesday morning, 8:30 AM. 1498 01:12:13,690 --> 01:12:17,480 And please stay safe and be well.