1 00:00:11,610 --> 00:00:14,040 GARY GENSLER: Welcome back to Fintech: 2 00:00:14,040 --> 00:00:17,160 Shaping the Financial World. 3 00:00:17,160 --> 00:00:20,180 This is our last time together. 4 00:00:20,180 --> 00:00:23,020 And so we're going to try to summarize up 5 00:00:23,020 --> 00:00:26,800 what this semester's been and some thoughts going forward 6 00:00:26,800 --> 00:00:31,580 as all of you go forth, and try to think 7 00:00:31,580 --> 00:00:34,610 about this intersection of technology and finance. 8 00:00:34,610 --> 00:00:39,140 And again, we've taught it from that point of view, 9 00:00:39,140 --> 00:00:41,270 not necessarily just about startups, but how 10 00:00:41,270 --> 00:00:44,240 technology and finance come together 11 00:00:44,240 --> 00:00:46,040 in the world of finance. 12 00:00:46,040 --> 00:00:48,920 And it's been shaping it, really, for centuries. 13 00:00:48,920 --> 00:00:51,710 But the dominant sort of themes that we've been talking about 14 00:00:51,710 --> 00:00:56,120 is the new technologies that, in the last five to 15 years, 15 00:00:56,120 --> 00:00:59,720 have really been transforming finance 16 00:00:59,720 --> 00:01:02,540 around artificial intelligence and machine 17 00:01:02,540 --> 00:01:06,080 learning, natural language processing, 18 00:01:06,080 --> 00:01:11,420 having dramatic changes, but also open API and the access 19 00:01:11,420 --> 00:01:18,050 to data through technological connections, sometimes done 20 00:01:18,050 --> 00:01:20,990 by commerce, sometimes the official sector has sort of 21 00:01:20,990 --> 00:01:24,720 moved and pressed against it. 22 00:01:24,720 --> 00:01:28,070 And then, of course, data, and all the different forms 23 00:01:28,070 --> 00:01:31,970 of alternative data, some off of just traditional means, 24 00:01:31,970 --> 00:01:36,740 like whether we pay our rent on time, some from social media. 25 00:01:36,740 --> 00:01:38,420 And then when we looked at insurtech, 26 00:01:38,420 --> 00:01:41,160 that I know really lifted the class up, 27 00:01:41,160 --> 00:01:44,840 this look at insurance and finance, 28 00:01:44,840 --> 00:01:49,550 but then also telematics, meaning all the data 29 00:01:49,550 --> 00:01:52,430 that a car brings on us, or wearables, 30 00:01:52,430 --> 00:01:56,090 if we wear a Fitbit or a watch or something, 31 00:01:56,090 --> 00:01:58,170 or the Internet of Things. 32 00:01:58,170 --> 00:02:00,590 And then we've talked even about some technologies that 33 00:02:00,590 --> 00:02:03,140 haven't yet come into the technology stack, 34 00:02:03,140 --> 00:02:05,720 like blockchain technology, and will it? 35 00:02:05,720 --> 00:02:07,460 It's already been a catalyst for change, 36 00:02:07,460 --> 00:02:12,140 but will it sort of come in and dramatically change finance. 37 00:02:12,140 --> 00:02:17,270 So as we normally do, if we can try to be part of the community 38 00:02:17,270 --> 00:02:21,110 and have our videos on, I still see some with it off. 39 00:02:21,110 --> 00:02:25,160 I know at 12 lectures in, and it's early morning. 40 00:02:25,160 --> 00:02:29,090 But I think if we can just try to close out together 41 00:02:29,090 --> 00:02:32,030 with our videos on, and if you have questions, 42 00:02:32,030 --> 00:02:35,210 raising the little Zoom blue hand 43 00:02:35,210 --> 00:02:36,800 or going into the chat room. 44 00:02:36,800 --> 00:02:40,130 And Romain is going to take a close look 45 00:02:40,130 --> 00:02:42,680 and try to have a dialogue here. 46 00:02:42,680 --> 00:02:45,890 I do have some slides to put up. 47 00:02:45,890 --> 00:02:50,930 I'm still the same faculty member of the other 11 classes, 48 00:02:50,930 --> 00:02:54,340 but let me share some slides and we'll get going. 49 00:03:05,060 --> 00:03:09,020 So we're going to talk a little bit about this finance 50 00:03:09,020 --> 00:03:15,650 technology stack and how I see these technologies fitting in, 51 00:03:15,650 --> 00:03:20,090 the actors again, and then a review of the key areas, 52 00:03:20,090 --> 00:03:22,970 machine learning, open API, credit scoring, 53 00:03:22,970 --> 00:03:24,740 alternative data, and the question 54 00:03:24,740 --> 00:03:27,530 marks around blockchain technology. 55 00:03:27,530 --> 00:03:32,360 And then a quick sector review, and bringing those all together 56 00:03:32,360 --> 00:03:36,180 and wrapping it up 57 00:03:36,180 --> 00:03:40,060 The study questions was really about what is fintech? 58 00:03:40,060 --> 00:03:43,000 And so I don't know if everybody just wants to have an easy, 59 00:03:43,000 --> 00:03:46,680 hit the ball out of the park moment. 60 00:03:46,680 --> 00:03:49,570 But to you all, what is fintech and how might it 61 00:03:49,570 --> 00:03:50,490 shape the future? 62 00:03:53,250 --> 00:03:55,640 ROMAIN: This is the last class and your last opportunity 63 00:03:55,640 --> 00:03:56,460 to shine, guys. 64 00:03:59,043 --> 00:04:01,710 GARY GENSLER: Don't you love how Romain sort of pulls it all out 65 00:04:01,710 --> 00:04:05,060 of you so readily, easily? 66 00:04:05,060 --> 00:04:06,390 ROMAIN: This is the last class. 67 00:04:06,390 --> 00:04:09,000 Danielle, go ahead. 68 00:04:09,000 --> 00:04:10,830 AUDIENCE: The application of technology 69 00:04:10,830 --> 00:04:14,610 to financial services, and how might 70 00:04:14,610 --> 00:04:19,737 it shape the future of money and finance in unlimited ways. 71 00:04:19,737 --> 00:04:20,820 GARY GENSLER: An optimist. 72 00:04:20,820 --> 00:04:24,110 I like that about you, Dani. 73 00:04:24,110 --> 00:04:25,350 Unlimited ways. 74 00:04:25,350 --> 00:04:28,453 So do you think we'll have banks in the future? 75 00:04:28,453 --> 00:04:29,870 Let's go right to the heart of it. 76 00:04:29,870 --> 00:04:33,883 Will we have commercial banks in the future? 77 00:04:33,883 --> 00:04:34,800 AUDIENCE: I think yes. 78 00:04:34,800 --> 00:04:36,960 I think it's hard to dislodge institutions 79 00:04:36,960 --> 00:04:40,800 with that much inertia. 80 00:04:40,800 --> 00:04:42,810 But they may not look the way they look. 81 00:04:42,810 --> 00:04:46,260 They may get slightly more agile, 82 00:04:46,260 --> 00:04:48,750 adapt, and have digital currency. 83 00:04:48,750 --> 00:04:52,710 They might pull in some of the fintech disruptors. 84 00:04:52,710 --> 00:04:54,430 So yes, we'll have commercial banks, 85 00:04:54,430 --> 00:04:58,188 but they may not look precisely like they do today. 86 00:04:58,188 --> 00:05:00,730 GARY GENSLER: I, too, think that we'll have commercial banks. 87 00:05:00,730 --> 00:05:03,240 And I think it's more than just inertia. 88 00:05:03,240 --> 00:05:07,530 I think that there's certain network effects that we've 89 00:05:07,530 --> 00:05:11,010 seen for centuries about bringing depositors 90 00:05:11,010 --> 00:05:15,760 and borrowers together and forming that intersection, sort 91 00:05:15,760 --> 00:05:18,730 of like a neck of an hourglass, but forming 92 00:05:18,730 --> 00:05:22,930 that intersection where the grains of sand, money and risk, 93 00:05:22,930 --> 00:05:27,670 flow between depositors who have money but want to save it, 94 00:05:27,670 --> 00:05:29,960 and borrowers who want to borrow it, 95 00:05:29,960 --> 00:05:32,380 and the commercial banks bring that together. 96 00:05:32,380 --> 00:05:34,600 Peer to peer lending has been around now 97 00:05:34,600 --> 00:05:38,500 for some 15 years, this concept that we don't need 98 00:05:38,500 --> 00:05:40,840 an intermediary in the middle. 99 00:05:40,840 --> 00:05:42,820 And then maybe borrowers and lenders 100 00:05:42,820 --> 00:05:46,140 can come together on a technology platform. 101 00:05:46,140 --> 00:05:49,210 And it's had some success, not just here in the US, 102 00:05:49,210 --> 00:05:51,970 but it started in Europe. 103 00:05:51,970 --> 00:05:53,290 It's been here in the US. 104 00:05:53,290 --> 00:05:58,390 It's been in China with great numbers and some challenges 105 00:05:58,390 --> 00:05:59,600 as well. 106 00:05:59,600 --> 00:06:02,500 But I think what we've seen is, by and large, we still 107 00:06:02,500 --> 00:06:05,200 have some centralized marketplaces that 108 00:06:05,200 --> 00:06:07,327 come together. 109 00:06:07,327 --> 00:06:09,910 Now that's not the only reason I think commercial banking will 110 00:06:09,910 --> 00:06:12,400 be around, but I think there are significant network 111 00:06:12,400 --> 00:06:17,210 effects pulling at the center. 112 00:06:17,210 --> 00:06:21,400 How do you best assess the viability of a fintech project? 113 00:06:21,400 --> 00:06:23,400 ROMAIN: Carlos, you like to take a stab at that? 114 00:06:27,040 --> 00:06:29,330 AUDIENCE: Sure. 115 00:06:29,330 --> 00:06:33,570 So I think there's a couple elements. 116 00:06:33,570 --> 00:06:39,200 One is to what degree is it an improvement 117 00:06:39,200 --> 00:06:42,920 relative to a traditional product? 118 00:06:42,920 --> 00:06:45,472 How much more convenient is it for users? 119 00:06:45,472 --> 00:06:46,930 And how much more accessible is it? 120 00:06:46,930 --> 00:06:49,790 I think those are important factors. 121 00:06:49,790 --> 00:06:51,540 Of course, there's a financial liability. 122 00:06:51,540 --> 00:06:54,150 You have to look at the actual economics of the product 123 00:06:54,150 --> 00:06:56,290 to see whether or not it's scalable, above all, 124 00:06:56,290 --> 00:06:59,990 and whether it makes sense to have it at a large degree. 125 00:06:59,990 --> 00:07:03,140 And then ultimately, I think we've 126 00:07:03,140 --> 00:07:06,800 seen, especially the last five years, 127 00:07:06,800 --> 00:07:09,412 whether fintech companies can actually 128 00:07:09,412 --> 00:07:11,870 become publicly owned companies, how they handle themselves 129 00:07:11,870 --> 00:07:13,140 in the stock market, et cetera. 130 00:07:13,140 --> 00:07:15,473 So I think there's been several phases to the evolution, 131 00:07:15,473 --> 00:07:17,040 and it's continuing. 132 00:07:17,040 --> 00:07:20,780 GARY GENSLER: So very helpful, in part. 133 00:07:20,780 --> 00:07:24,380 So first Carlos said fundamentally, you have 134 00:07:24,380 --> 00:07:26,390 to have a value proposition. 135 00:07:26,390 --> 00:07:28,880 I'm using my words, but sort of translating it, 136 00:07:28,880 --> 00:07:30,380 if that's all right, Carlos. 137 00:07:30,380 --> 00:07:32,540 So what pain points are you addressing? 138 00:07:32,540 --> 00:07:36,500 What gap in the current system of finance? 139 00:07:36,500 --> 00:07:39,830 And it might be about user interface and user experience. 140 00:07:39,830 --> 00:07:42,320 And as mobile phones came along, as we 141 00:07:42,320 --> 00:07:45,920 moved from online banking and online trading 142 00:07:45,920 --> 00:07:48,680 to mobile banking and mobile trading that, 143 00:07:48,680 --> 00:07:50,730 creates opportunities. 144 00:07:50,730 --> 00:07:53,870 Many of those opportunities have already happened. 145 00:07:53,870 --> 00:07:57,410 They're not all done, but many of those opportunities 146 00:07:57,410 --> 00:08:01,610 of that transition from bricks and mortar to the internet, 147 00:08:01,610 --> 00:08:03,510 to mobile phones, have happened. 148 00:08:03,510 --> 00:08:06,650 But they create opportunities of user experience and user 149 00:08:06,650 --> 00:08:07,550 interface. 150 00:08:07,550 --> 00:08:11,450 But it has to address some pain point, do something 151 00:08:11,450 --> 00:08:13,650 quicker, cheaper, better. 152 00:08:13,650 --> 00:08:17,630 It's an old saw in terms of strategy. 153 00:08:17,630 --> 00:08:20,480 But then Carlos is also saying we need 154 00:08:20,480 --> 00:08:23,990 to have financial viability. 155 00:08:23,990 --> 00:08:25,850 At some point in time, you have to be 156 00:08:25,850 --> 00:08:29,240 able to build a model where revenues are more than costs. 157 00:08:29,240 --> 00:08:31,910 A lot of these startups are looking for that sweet spot, 158 00:08:31,910 --> 00:08:34,820 that sort of nirvana moment when you 159 00:08:34,820 --> 00:08:38,030 build a platform, where platform economics and the network 160 00:08:38,030 --> 00:08:39,630 effects take off. 161 00:08:39,630 --> 00:08:41,390 But Carlos said you still have to have 162 00:08:41,390 --> 00:08:43,842 some financial viability at some point in time. 163 00:08:43,842 --> 00:08:45,592 I think there was another hand up, Romain. 164 00:08:48,162 --> 00:08:50,620 AUDIENCE: Yeah, I think to kind of built on Carlos's point, 165 00:08:50,620 --> 00:08:54,710 another thing that might make or break a product 166 00:08:54,710 --> 00:08:57,260 is potential for fraud or the risk profile 167 00:08:57,260 --> 00:09:01,317 increase on a specific area. 168 00:09:01,317 --> 00:09:03,400 One thing we were kind of talking about as a group 169 00:09:03,400 --> 00:09:06,580 was alternative scoring, and that's something 170 00:09:06,580 --> 00:09:08,582 that we're just not sure if we're 171 00:09:08,582 --> 00:09:12,760 hot on or not just because is taking 172 00:09:12,760 --> 00:09:16,940 alternative data actually going to increase or reduce risks 173 00:09:16,940 --> 00:09:18,175 looking forward. 174 00:09:18,175 --> 00:09:19,550 GARY GENSLER: So Michael, I think 175 00:09:19,550 --> 00:09:21,590 what you're saying is you still have 176 00:09:21,590 --> 00:09:24,470 to live within regulatory constraints 177 00:09:24,470 --> 00:09:29,120 and social constraints in terms of there's normative behavior, 178 00:09:29,120 --> 00:09:33,350 and this is a highly regulated field, and for good reason. 179 00:09:33,350 --> 00:09:35,840 We're dealing with trust, and trust 180 00:09:35,840 --> 00:09:37,820 is at the center of finance. 181 00:09:37,820 --> 00:09:40,750 We're also dealing with other people's money and livelihood 182 00:09:40,750 --> 00:09:42,830 and savings and investments. 183 00:09:42,830 --> 00:09:44,600 And so there's investor protection, 184 00:09:44,600 --> 00:09:49,220 there's consumer protection, and in terms of alternative data, 185 00:09:49,220 --> 00:09:51,800 the basic tenets around the globe 186 00:09:51,800 --> 00:09:57,050 that when you extend credit or offer somebody insurance, 187 00:09:57,050 --> 00:09:59,600 it should be done fairly and unbiased. 188 00:09:59,600 --> 00:10:03,770 And so how do we ensure that the use of alternative data 189 00:10:03,770 --> 00:10:08,750 still stays within those social and legal norms about offering 190 00:10:08,750 --> 00:10:14,090 credit and insurance in an unbiased and fair way. 191 00:10:14,090 --> 00:10:17,990 I think that's what you were chatting about. 192 00:10:17,990 --> 00:10:18,990 AUDIENCE: Yeah, kind of. 193 00:10:18,990 --> 00:10:22,380 I was just thinking about, also, the lack of adoption crypto. 194 00:10:22,380 --> 00:10:26,070 It's like, if it's just going to sort of attract 195 00:10:26,070 --> 00:10:27,943 scammers and fraudulent activity, 196 00:10:27,943 --> 00:10:30,360 the technology is going to have a hard time being adopted. 197 00:10:30,360 --> 00:10:30,750 GARY GENSLER: Right. 198 00:10:30,750 --> 00:10:31,410 Right. 199 00:10:31,410 --> 00:10:34,810 So of course we want to protect against fraud in the capital 200 00:10:34,810 --> 00:10:35,310 markets. 201 00:10:35,310 --> 00:10:37,410 We also want to protect against manipulation 202 00:10:37,410 --> 00:10:39,480 and ensure investment protection. 203 00:10:39,480 --> 00:10:41,850 And we've seen, particularly in some fields 204 00:10:41,850 --> 00:10:43,710 like cryptocurrencies, that they're 205 00:10:43,710 --> 00:10:48,940 rife with fraud and manipulation and sort of quick buck artists, 206 00:10:48,940 --> 00:10:50,910 frankly, around the globe. 207 00:10:50,910 --> 00:10:54,840 And so to ensure that any long-term viability 208 00:10:54,840 --> 00:10:57,420 of a project, real viability comes 209 00:10:57,420 --> 00:11:01,800 with investor protection, consumer protection norms, 210 00:11:01,800 --> 00:11:07,890 as well as norms about fairness, inclusiveness, explainability, 211 00:11:07,890 --> 00:11:08,770 and the like. 212 00:11:08,770 --> 00:11:11,730 In most nations, these are embedded in various regulations 213 00:11:11,730 --> 00:11:12,570 and laws. 214 00:11:12,570 --> 00:11:18,030 So standing up a new effort, whether it's inside 215 00:11:18,030 --> 00:11:21,780 of an incumbent, inside a big tech, or a startup, 216 00:11:21,780 --> 00:11:24,450 you've got to look at the regulatory context. 217 00:11:24,450 --> 00:11:25,875 But Michael's pointing out, also, 218 00:11:25,875 --> 00:11:27,250 you've got to go a little further 219 00:11:27,250 --> 00:11:28,917 and make sure that you're really serving 220 00:11:28,917 --> 00:11:33,150 your public and your community. 221 00:11:33,150 --> 00:11:35,570 And then the last question is, I hope that we've 222 00:11:35,570 --> 00:11:37,560 met the goals of the class. 223 00:11:37,560 --> 00:11:43,940 But I do ask you today and over the next few weeks, write me. 224 00:11:43,940 --> 00:11:48,470 Write me at gensler@mit.edu and tell me 225 00:11:48,470 --> 00:11:50,870 what we could do better. 226 00:11:50,870 --> 00:11:52,610 This the first year we've stood this up. 227 00:11:52,610 --> 00:11:56,090 This class is also going to be shown publicly 228 00:11:56,090 --> 00:11:58,370 in MIT'S OpenCourseWare. 229 00:11:58,370 --> 00:12:03,470 So those of you that might be watching this publicly, 230 00:12:03,470 --> 00:12:06,920 write me and tell me what we can do in this class going forward 231 00:12:06,920 --> 00:12:10,340 and how we can make it a better product. 232 00:12:10,340 --> 00:12:12,110 I'm kind of curious whether it's made 233 00:12:12,110 --> 00:12:15,390 some folks more bullish or more bearish on fintech, 234 00:12:15,390 --> 00:12:19,770 and I'm going to ask this at the end of class as well, 235 00:12:19,770 --> 00:12:23,000 but if there's anybody who wants to chat now, Romain? 236 00:12:26,630 --> 00:12:29,630 Anybody want to take the bullish side or the bearish side? 237 00:12:29,630 --> 00:12:33,370 ROMAIN: Danielle has her hand up. 238 00:12:33,370 --> 00:12:36,280 AUDIENCE: I think it depends on which hat I have on, 239 00:12:36,280 --> 00:12:39,160 if I'm an investor or entrepreneur. 240 00:12:39,160 --> 00:12:40,840 The class has made me more bullish. 241 00:12:40,840 --> 00:12:43,690 Lots of opportunities out there. 242 00:12:43,690 --> 00:12:48,970 If I have my citizen hat on, I think I net out at bearish, 243 00:12:48,970 --> 00:12:52,660 not so much on the industry, but more on its impact for me. 244 00:12:52,660 --> 00:12:54,310 I'm certainly thrilled at the ability 245 00:12:54,310 --> 00:12:58,150 to access different forms of payment and lending. 246 00:12:58,150 --> 00:13:00,250 But ultimately, I think the long-term costs 247 00:13:00,250 --> 00:13:03,400 in terms of the data these companies will 248 00:13:03,400 --> 00:13:07,310 have on me, and the just immense role 249 00:13:07,310 --> 00:13:10,690 and control over my life, I net out at bearish. 250 00:13:10,690 --> 00:13:12,190 And then if I'm the government, I'm 251 00:13:12,190 --> 00:13:14,357 probably netting out at bearish as well because this 252 00:13:14,357 --> 00:13:19,030 is going to mean a gnarly set of regulations and laws 253 00:13:19,030 --> 00:13:22,360 to navigate that means a lot more work for me. 254 00:13:22,360 --> 00:13:24,620 GARY GENSLER: Very interesting, very nuanced answer. 255 00:13:24,620 --> 00:13:26,590 I see some other hands up. 256 00:13:26,590 --> 00:13:27,593 ROMAIN: Celi? 257 00:13:33,327 --> 00:13:35,410 GARY GENSLER: Maybe you've got your mute on still. 258 00:13:35,410 --> 00:13:36,702 ROMAIN: Yes, we can't hear you. 259 00:13:39,290 --> 00:13:39,790 OK. 260 00:13:39,790 --> 00:13:43,675 Then let's move on to Lamide. 261 00:13:43,675 --> 00:13:44,300 AUDIENCE: Yeah. 262 00:13:44,300 --> 00:13:46,880 I'm more bullish. 263 00:13:46,880 --> 00:13:49,310 For me one of the interesting trends 264 00:13:49,310 --> 00:13:51,280 I see is serving the underserved, 265 00:13:51,280 --> 00:13:55,460 whether that's in crypto trying to serve the population that 266 00:13:55,460 --> 00:13:58,437 doesn't have to be VC, or whether it's in insurance. 267 00:13:58,437 --> 00:14:00,020 you have all these trends where people 268 00:14:00,020 --> 00:14:03,680 are getting more nuanced products to really target 269 00:14:03,680 --> 00:14:05,740 populations that are underserved. 270 00:14:05,740 --> 00:14:09,520 And all these different kind of new innovations around that 271 00:14:09,520 --> 00:14:12,000 inspire me. 272 00:14:12,000 --> 00:14:17,330 GARY GENSLER: So we've got bullish as an investor 273 00:14:17,330 --> 00:14:18,470 from Dani. 274 00:14:18,470 --> 00:14:23,630 We've got bullish for economic inclusion and product inclusion 275 00:14:23,630 --> 00:14:25,880 just now from Lamide. 276 00:14:25,880 --> 00:14:28,310 But there were some bearishness from Dani 277 00:14:28,310 --> 00:14:32,930 on data and the industry knowing so much more about us. 278 00:14:32,930 --> 00:14:37,760 Was there anybody else, Romain, that wanted to add in? 279 00:14:37,760 --> 00:14:40,677 ROMAIN: Maybe Celi, if her microphone works, and Luke. 280 00:14:40,677 --> 00:14:41,510 GARY GENSLER: Right. 281 00:14:41,510 --> 00:14:42,468 And then we'll move on. 282 00:14:42,468 --> 00:14:44,909 We'll take Luke and Celi. 283 00:14:44,909 --> 00:14:46,240 AUDIENCE: Yeah. 284 00:14:46,240 --> 00:14:48,190 My internet connection's poor. 285 00:14:48,190 --> 00:14:51,070 But I think I'd like to echo-- 286 00:14:51,070 --> 00:14:57,760 I missed part of Lamide's but my thing was financial inclusion. 287 00:14:57,760 --> 00:15:01,760 So really entering, I think, also, it's 288 00:15:01,760 --> 00:15:05,610 in the benefit of the government, too. 289 00:15:05,610 --> 00:15:09,220 I know that there are some concerns there, 290 00:15:09,220 --> 00:15:11,650 but I think if you're thinking about it 291 00:15:11,650 --> 00:15:13,660 from an economic growth perspective, 292 00:15:13,660 --> 00:15:16,480 there's a lot of opportunity through fintech 293 00:15:16,480 --> 00:15:20,420 that would end up really benefit the entire economy. 294 00:15:20,420 --> 00:15:24,160 So I think that there's the argument for the government 295 00:15:24,160 --> 00:15:27,220 to have interest and to be a more bullish approach as 296 00:15:27,220 --> 00:15:31,490 well, even though there are risks involved. 297 00:15:31,490 --> 00:15:33,440 GARY GENSLER: So you accept a little bit 298 00:15:33,440 --> 00:15:36,020 what Dani was saying that for the government, 299 00:15:36,020 --> 00:15:40,490 the official sector, some of the challenges, to use her words, 300 00:15:40,490 --> 00:15:42,560 were gnarly. 301 00:15:42,560 --> 00:15:45,320 But though there's challenges, you think to the extent 302 00:15:45,320 --> 00:15:48,050 that it promotes inclusion and economic activity, 303 00:15:48,050 --> 00:15:51,750 that innovation is a net good, and competition, 304 00:15:51,750 --> 00:15:53,600 if I can insert something. 305 00:15:53,600 --> 00:15:56,930 What fintech does is it provides an opportunity 306 00:15:56,930 --> 00:16:01,960 both for incumbents and startups and big competitors 307 00:16:01,960 --> 00:16:05,170 like big tech to start to bring competitive challenges. 308 00:16:05,170 --> 00:16:08,140 We saw this in China when Ally Pay and WeChat 309 00:16:08,140 --> 00:16:14,230 Pay bring economic challenges to the banking sector 310 00:16:14,230 --> 00:16:17,300 and now dominate the retail payment space. 311 00:16:17,300 --> 00:16:20,440 But for a while, all of that inclusion 312 00:16:20,440 --> 00:16:22,990 creates economic opportunity. 313 00:16:22,990 --> 00:16:25,360 I say for a while because at some point in time, 314 00:16:25,360 --> 00:16:30,040 then they too become a bit of an oligopoly. 315 00:16:30,040 --> 00:16:34,180 And they can charge, over time, economic rents. 316 00:16:34,180 --> 00:16:36,760 But that has certainly brought a lot of inclusion, 317 00:16:36,760 --> 00:16:40,360 a lot of growth, a lot of economic opportunity 318 00:16:40,360 --> 00:16:42,970 to underserved populations, in that case, 319 00:16:42,970 --> 00:16:45,130 in China and elsewhere in Asia. 320 00:16:45,130 --> 00:16:48,940 We've also seen that across the US and in Europe 321 00:16:48,940 --> 00:16:54,400 with competition from challenger banks and neo banks and payment 322 00:16:54,400 --> 00:16:59,080 companies, long ago, like PayPal started 20 years ago, or more 323 00:16:59,080 --> 00:17:02,710 recently, by many of the payment innovators in this space, 324 00:17:02,710 --> 00:17:05,950 whether they're data aggregators or direct payment 325 00:17:05,950 --> 00:17:09,160 solutions like Toast in the restaurant business. 326 00:17:09,160 --> 00:17:11,780 But as Dani said also, the Plaids, 327 00:17:11,780 --> 00:17:15,220 which are the data aggregators, or even the Credit Karmas 328 00:17:15,220 --> 00:17:19,270 giving us free access to our credit reports, 329 00:17:19,270 --> 00:17:22,210 or Robin Hood giving us free access 330 00:17:22,210 --> 00:17:24,880 to trading online brokerage, also 331 00:17:24,880 --> 00:17:27,589 then are collecting significant amount of data. 332 00:17:27,589 --> 00:17:29,770 And whether they're commercializing that data 333 00:17:29,770 --> 00:17:34,180 in ways we understand, appreciate, and want, 334 00:17:34,180 --> 00:17:36,280 Dani is sort of raising that makes her 335 00:17:36,280 --> 00:17:38,250 a little uncomfortable as well. 336 00:17:38,250 --> 00:17:41,720 And I think we were going to close it out with Luke. 337 00:17:41,720 --> 00:17:44,770 AUDIENCE: So I'm bullish in the long term 338 00:17:44,770 --> 00:17:48,020 in the sense that two conditions are met, 339 00:17:48,020 --> 00:17:51,530 the convenience and then technology acceptance in terms 340 00:17:51,530 --> 00:17:53,250 of blockchain. 341 00:17:53,250 --> 00:17:54,800 You teach blockchain and money. 342 00:17:54,800 --> 00:17:59,000 And if people accept blockchain as technology as core 343 00:17:59,000 --> 00:18:02,510 to fintech, it has a lot of potentials. 344 00:18:02,510 --> 00:18:08,120 If not, this is nothing more than making any status quo 345 00:18:08,120 --> 00:18:11,790 industry more convenient and more accessible. 346 00:18:11,790 --> 00:18:13,113 So little bearish. 347 00:18:13,113 --> 00:18:14,780 GARY GENSLER: So you're bullish, and one 348 00:18:14,780 --> 00:18:18,440 of the reasons you're bullish is you feel the blockchain 349 00:18:18,440 --> 00:18:23,870 technology is a positive. 350 00:18:23,870 --> 00:18:25,490 And is that positive because you feel 351 00:18:25,490 --> 00:18:27,980 it's pushing at the side of central banks, 352 00:18:27,980 --> 00:18:31,700 pushing at the side of central payment processors 353 00:18:31,700 --> 00:18:35,210 and the rest of finance, which I would share that view. 354 00:18:35,210 --> 00:18:36,920 I think it's a catalyst. 355 00:18:36,920 --> 00:18:40,430 And it's part of why we've seen a lot of innovation. 356 00:18:40,430 --> 00:18:44,120 Even our US Federal Reserve, who was thinking 357 00:18:44,120 --> 00:18:48,050 about doing 24 hour a day, seven days a week payment systems, 358 00:18:48,050 --> 00:18:50,120 is rolling out something called Fed Now. 359 00:18:50,120 --> 00:18:53,990 I think that's in part because of this catalyst for change. 360 00:18:53,990 --> 00:18:58,220 But do you also think that it will be a mainstream product 361 00:18:58,220 --> 00:19:00,770 offering, and that blockchain technology will 362 00:19:00,770 --> 00:19:07,400 roll into what I call the technology stack in finance? 363 00:19:07,400 --> 00:19:09,130 AUDIENCE: Well, I believe in evolution, 364 00:19:09,130 --> 00:19:12,950 so in 10 years or whatever years that may take, 365 00:19:12,950 --> 00:19:14,960 this is indeed the best technology 366 00:19:14,960 --> 00:19:16,610 if everyone accepts it. 367 00:19:16,610 --> 00:19:17,570 So I don't see why not. 368 00:19:17,570 --> 00:19:19,910 However, it will take a tremendous amount 369 00:19:19,910 --> 00:19:23,120 of time and energy and effort for everyone 370 00:19:23,120 --> 00:19:25,080 to accept it and understand it. 371 00:19:25,080 --> 00:19:26,960 Either it takes a crisis or it takes 372 00:19:26,960 --> 00:19:28,698 governmental intervention. 373 00:19:28,698 --> 00:19:29,990 GARY GENSLER: You may be right. 374 00:19:29,990 --> 00:19:32,960 I still haven't been convinced, but you may be right. 375 00:19:36,265 --> 00:19:38,930 I'm sort of a middle of the road. 376 00:19:38,930 --> 00:19:41,190 I'm neither a minimalist or a maximalist 377 00:19:41,190 --> 00:19:42,650 on blockchain technology. 378 00:19:42,650 --> 00:19:46,250 But it hasn't yet because of the dominant challenges 379 00:19:46,250 --> 00:19:47,070 of governance. 380 00:19:47,070 --> 00:19:50,630 How do you have multiple parties sharing a ledger? 381 00:19:50,630 --> 00:19:53,070 And these issues of governance are not new. 382 00:19:53,070 --> 00:19:57,260 They're just in this context of blockchain technology. 383 00:19:57,260 --> 00:20:01,850 But the opportunity to have a new data structure, a shared 384 00:20:01,850 --> 00:20:04,970 data structure amongst multiple parties, 385 00:20:04,970 --> 00:20:09,140 I think you're probably right, will come into use somewhere. 386 00:20:09,140 --> 00:20:11,660 The question is how broadly because you 387 00:20:11,660 --> 00:20:15,140 have at the center a really-- 388 00:20:15,140 --> 00:20:17,290 Dani used the word gnarly, but maybe 389 00:20:17,290 --> 00:20:20,270 if I use that here, gnarly set of governance issues, 390 00:20:20,270 --> 00:20:23,840 how you have a shared data structure, a shared ledger. 391 00:20:23,840 --> 00:20:26,210 And then, of course, in cryptocurrencies 392 00:20:26,210 --> 00:20:28,270 is another set of challenges. 393 00:20:28,270 --> 00:20:31,620 Is a private sector currency needed? 394 00:20:31,620 --> 00:20:34,000 Well, if fiat currencies are well managed, 395 00:20:34,000 --> 00:20:36,380 if monetary policy and fiscal policies 396 00:20:36,380 --> 00:20:38,990 are well managed, probably not. 397 00:20:38,990 --> 00:20:42,560 But then again, the history of nations, the history of finance 398 00:20:42,560 --> 00:20:48,140 and money of 200 countries, there tends to be one to three 399 00:20:48,140 --> 00:20:50,780 at any given time that aren't managing their fiat 400 00:20:50,780 --> 00:20:52,040 currencies well. 401 00:20:52,040 --> 00:20:53,930 So there you have it. 402 00:20:53,930 --> 00:20:55,070 AUDIENCE: One last notice. 403 00:20:55,070 --> 00:20:58,880 Only when governments' interest aligns with the blockchain, 404 00:20:58,880 --> 00:21:02,110 because fiat currency's centralized and blockchain is 405 00:21:02,110 --> 00:21:07,260 decentralized, would we have a wide acceptance of blockchain, 406 00:21:07,260 --> 00:21:07,760 correct? 407 00:21:07,760 --> 00:21:09,177 GARY GENSLER: That's a good point, 408 00:21:09,177 --> 00:21:13,820 that the sovereign currency is probably here 409 00:21:13,820 --> 00:21:17,180 unless there's some collaboration 410 00:21:17,180 --> 00:21:20,780 between the central authorities and the use of that technology 411 00:21:20,780 --> 00:21:22,770 is your point. 412 00:21:22,770 --> 00:21:25,250 So we're going to dive in a little bit. 413 00:21:25,250 --> 00:21:27,140 And just sort of summarizing, I talk 414 00:21:27,140 --> 00:21:30,260 about the technology stack. 415 00:21:30,260 --> 00:21:32,300 And the cloud, mobile, the internet 416 00:21:32,300 --> 00:21:36,140 were important parts that were added to the finance technology 417 00:21:36,140 --> 00:21:40,250 stack, really, from the 1990s all the way till now. 418 00:21:40,250 --> 00:21:44,560 And if you look at big finance, all 419 00:21:44,560 --> 00:21:47,110 using the internet, of course, from the late '90s, 420 00:21:47,110 --> 00:21:49,480 and into the naughts, all pretty much 421 00:21:49,480 --> 00:21:53,530 rolling out mobile applications, particularly in the last 10 422 00:21:53,530 --> 00:21:58,420 years, often because of the pressures from startups. 423 00:21:58,420 --> 00:22:02,230 Not so much all have rolled in the cloud. 424 00:22:02,230 --> 00:22:05,290 And many big finance companies are still having 425 00:22:05,290 --> 00:22:07,810 their centralized data structures. 426 00:22:07,810 --> 00:22:09,640 But more and more are using the cloud. 427 00:22:09,640 --> 00:22:11,200 What's important about the cloud, 428 00:22:11,200 --> 00:22:15,310 though, is that it gives startups a real chance 429 00:22:15,310 --> 00:22:18,610 to sort of rent versus bill. 430 00:22:18,610 --> 00:22:21,580 So you don't have to build your own data structure, 431 00:22:21,580 --> 00:22:26,110 put it in Microsoft Azure, put it in an Amazon AWS, 432 00:22:26,110 --> 00:22:29,870 put it in [INAUDIBLE] equivalents and so forth. 433 00:22:29,870 --> 00:22:32,860 So those were all earlier technologies fully 434 00:22:32,860 --> 00:22:35,240 in the finance technology stack. 435 00:22:35,240 --> 00:22:37,840 But we've talked a lot about in this class machine 436 00:22:37,840 --> 00:22:40,050 learning, natural language processing, 437 00:22:40,050 --> 00:22:44,090 the critical pieces, really, in the last call it 438 00:22:44,090 --> 00:22:49,330 seven to 10 years, that computational power 439 00:22:49,330 --> 00:22:55,390 and the ability to analyze data and extract correlations. 440 00:22:55,390 --> 00:22:59,440 And what machine learning at its core does best, 441 00:22:59,440 --> 00:23:03,100 it's remarkable at extracting correlations, pattern 442 00:23:03,100 --> 00:23:05,000 recognition, if you wish. 443 00:23:05,000 --> 00:23:08,440 And that pattern recognition on data 444 00:23:08,440 --> 00:23:11,570 has started to really be important in this field. 445 00:23:11,570 --> 00:23:13,320 And we'll talk a little bit more about it. 446 00:23:13,320 --> 00:23:17,620 But it's also access to data and digital interfaces 447 00:23:17,620 --> 00:23:23,010 through open application program interfaces, or API. 448 00:23:23,010 --> 00:23:25,680 And you could have machine learning and natural language 449 00:23:25,680 --> 00:23:28,890 processing without open API, but open API 450 00:23:28,890 --> 00:23:30,790 is a really important piece of this, 451 00:23:30,790 --> 00:23:34,660 particularly as startups try to get into this space. 452 00:23:34,660 --> 00:23:36,750 And as we talked about, some of that's 453 00:23:36,750 --> 00:23:40,440 because the official sector says, thou shall do this. 454 00:23:40,440 --> 00:23:44,310 Some of it is technologically facilitated by companies 455 00:23:44,310 --> 00:23:46,530 that provided that access. 456 00:23:46,530 --> 00:23:52,050 And then many of the incumbents wanted to provide it as well. 457 00:23:52,050 --> 00:23:53,940 And then, of course, there's a whole bunch 458 00:23:53,940 --> 00:23:55,940 of things that have been in the technology stack 459 00:23:55,940 --> 00:23:57,710 for decades or centuries. 460 00:23:57,710 --> 00:23:59,840 We took those as givens. 461 00:23:59,840 --> 00:24:02,480 And I think at some point, when this course is 462 00:24:02,480 --> 00:24:05,480 taught in the 2030s, machine learning 463 00:24:05,480 --> 00:24:07,460 will just be taken as a given. 464 00:24:07,460 --> 00:24:09,320 Open API will be a given, and there'll 465 00:24:09,320 --> 00:24:11,720 be new technologies beyond that. 466 00:24:11,720 --> 00:24:15,320 Now we talked about one that's really in insurance. 467 00:24:15,320 --> 00:24:17,360 And I kid the class a little bit because I 468 00:24:17,360 --> 00:24:20,840 know the interest has been more in payments and in challenger 469 00:24:20,840 --> 00:24:22,810 banks and neo banks. 470 00:24:22,810 --> 00:24:26,540 But why I find insurance so interesting is it's 471 00:24:26,540 --> 00:24:29,820 starting to layer a new technology into the technology 472 00:24:29,820 --> 00:24:30,320 stack. 473 00:24:30,320 --> 00:24:35,150 We don't see this as much in the challenger banks and in credit. 474 00:24:35,150 --> 00:24:40,210 But sensor data, telematics in the automobiles, 475 00:24:40,210 --> 00:24:42,640 wearables and the like. 476 00:24:42,640 --> 00:24:46,840 All that remarkable amount of data to assess and underwrite 477 00:24:46,840 --> 00:24:49,870 insurance risk, whether it's on an automobile, 478 00:24:49,870 --> 00:24:52,750 whether it's on a home, will that 479 00:24:52,750 --> 00:24:56,650 come into the technology stack of banks and credit card 480 00:24:56,650 --> 00:24:57,580 companies? 481 00:24:57,580 --> 00:25:00,630 We haven't yet seen it. 482 00:25:00,630 --> 00:25:05,390 But other than sort of social norms and regulatory reasons, 483 00:25:05,390 --> 00:25:09,350 why wouldn't we see that wealth of data from your automobile, 484 00:25:09,350 --> 00:25:14,330 from your wearables, from Internet of Things sensors 485 00:25:14,330 --> 00:25:18,310 wouldn't start to influence, and one 486 00:25:18,310 --> 00:25:21,910 might say infiltrate, into how credit decisions are 487 00:25:21,910 --> 00:25:26,390 made, again, within the regulatory construct 488 00:25:26,390 --> 00:25:27,290 and so forth. 489 00:25:27,290 --> 00:25:30,550 So one of the reasons why I find insurance so fascinating, 490 00:25:30,550 --> 00:25:33,370 because there's sort of another technology 491 00:25:33,370 --> 00:25:35,680 that they're starting to use to collect data. 492 00:25:35,680 --> 00:25:37,280 Really important. 493 00:25:37,280 --> 00:25:39,340 And then lastly, we've talked a lot 494 00:25:39,340 --> 00:25:41,050 about blockchain technology. 495 00:25:41,050 --> 00:25:42,830 I teach it as well. 496 00:25:42,830 --> 00:25:44,470 I find it a fascinating area. 497 00:25:44,470 --> 00:25:47,290 But just as that back and forth with Luke, 498 00:25:47,290 --> 00:25:48,550 I put question marks. 499 00:25:48,550 --> 00:25:51,490 Will it come into that full technology stack? 500 00:25:51,490 --> 00:25:53,830 It's most definitely been a catalyst for change. 501 00:25:53,830 --> 00:25:57,370 It's pushing at the edges of finance. 502 00:25:57,370 --> 00:26:00,880 It's starting to make each institution, whether it's 503 00:26:00,880 --> 00:26:03,700 JP Morgan thinking about a new way 504 00:26:03,700 --> 00:26:06,640 to do payments with something called JP Morgan 505 00:26:06,640 --> 00:26:10,300 Coin, or a whole collection of incumbent banks 506 00:26:10,300 --> 00:26:17,250 around a project called Finality or utility settlement coin. 507 00:26:17,250 --> 00:26:20,410 We have, of course, seen how Facebook 508 00:26:20,410 --> 00:26:24,130 caught a great deal of attention in 2019 and 2020 509 00:26:24,130 --> 00:26:26,320 with their Facebook Libra project, 510 00:26:26,320 --> 00:26:30,370 a consortium to roll out a new global currency, 511 00:26:30,370 --> 00:26:33,680 both multi-basket and single currency. 512 00:26:33,680 --> 00:26:37,810 So incumbents in big tech are taking very close look at this. 513 00:26:37,810 --> 00:26:39,700 And then you have the Central Bank of China, 514 00:26:39,700 --> 00:26:42,070 People's Bank of China doing something 515 00:26:42,070 --> 00:26:46,900 called a digital currency electronic payments program. 516 00:26:46,900 --> 00:26:48,820 It's not using blockchain technology. 517 00:26:48,820 --> 00:26:52,060 But for sure it's inspired by cryptocurrencies. 518 00:26:52,060 --> 00:26:58,090 It's for sure put a higher emphasis for all central banks 519 00:26:58,090 --> 00:27:00,970 around the globe to consider what's called central bank 520 00:27:00,970 --> 00:27:02,660 digital currencies. 521 00:27:02,660 --> 00:27:05,170 So I put some question marks because we know it's already 522 00:27:05,170 --> 00:27:07,960 been a catalyst for change, but will it actually 523 00:27:07,960 --> 00:27:11,130 be incorporated in the technology stack? 524 00:27:11,130 --> 00:27:12,850 Romain, any questions? 525 00:27:16,430 --> 00:27:18,270 ROMAIN: Let's wait just another second. 526 00:27:18,270 --> 00:27:18,830 Yes, Hassan. 527 00:27:23,005 --> 00:27:23,630 AUDIENCE: Yeah. 528 00:27:23,630 --> 00:27:24,588 I just have a question. 529 00:27:24,588 --> 00:27:27,400 Now at the beginning of the slide, 530 00:27:27,400 --> 00:27:31,060 you said internet, mobile, and the cloud. 531 00:27:31,060 --> 00:27:32,380 What do you mean exactly? 532 00:27:32,380 --> 00:27:34,600 I mean, did it move from internet to cloud, 533 00:27:34,600 --> 00:27:38,470 or is is just like a new technology? 534 00:27:38,470 --> 00:27:39,520 What do you mean? 535 00:27:39,520 --> 00:27:42,280 GARY GENSLER: I'm sure-- because your voice is fading out-- 536 00:27:42,280 --> 00:27:45,160 are you asking whether I think these are new technologies? 537 00:27:45,160 --> 00:27:48,130 I think they were new when they rolled out 538 00:27:48,130 --> 00:27:51,640 the internet fundamentally in the mid- to late '90s 539 00:27:51,640 --> 00:27:54,760 for most of finance had to take notice. 540 00:27:54,760 --> 00:27:58,390 But I assure you, having lived it, in the early '90s, 541 00:27:58,390 --> 00:28:01,840 the internet was not in the technology stack, 542 00:28:01,840 --> 00:28:05,830 meaning most of big finance and startups of the time 543 00:28:05,830 --> 00:28:07,120 didn't use it. 544 00:28:07,120 --> 00:28:10,990 But many of the startups by the late '90s had to. 545 00:28:10,990 --> 00:28:16,450 Amazon started in 1995, and they embedded the internet in it. 546 00:28:16,450 --> 00:28:21,010 Of course, it was what made Amazon so successful. 547 00:28:21,010 --> 00:28:27,370 PayPal started in 1997 or 1998, for instance. 548 00:28:27,370 --> 00:28:31,310 Now you can't survive if you don't use the internet. 549 00:28:31,310 --> 00:28:32,870 So that's what I'm saying there. 550 00:28:32,870 --> 00:28:35,510 And mobile phones similarly. 551 00:28:35,510 --> 00:28:41,570 By and large, finance, at first, was providing things online, 552 00:28:41,570 --> 00:28:44,270 or shall I say, on the internet, but not 553 00:28:44,270 --> 00:28:46,280 necessarily on a mobile app. 554 00:28:46,280 --> 00:28:50,720 And the first mobile apps were fundamentally just the same 555 00:28:50,720 --> 00:28:53,360 that you could do online, and then you 556 00:28:53,360 --> 00:28:56,960 had to give a better user interface, more adaptable, 557 00:28:56,960 --> 00:28:59,990 in essence, something for the small screen 558 00:28:59,990 --> 00:29:03,380 that our fingers can do easily and readily on this rather than 559 00:29:03,380 --> 00:29:04,830 on a laptop. 560 00:29:04,830 --> 00:29:08,390 So I'm saying that today, whether you're 561 00:29:08,390 --> 00:29:12,220 big tech, big finance, or a startup, 562 00:29:12,220 --> 00:29:13,870 you have to do these things. 563 00:29:13,870 --> 00:29:18,370 You have to do them well or you can't compete fundamentally. 564 00:29:18,370 --> 00:29:22,292 But maybe I didn't understand the question, Hassan. 565 00:29:22,292 --> 00:29:24,000 AUDIENCE: I mean, when you move to cloud, 566 00:29:24,000 --> 00:29:29,110 is it like supporting the mobile application, 567 00:29:29,110 --> 00:29:32,018 or does it have a different use? 568 00:29:32,018 --> 00:29:34,060 GARY GENSLER: I think it does a number of things. 569 00:29:34,060 --> 00:29:34,850 Great question. 570 00:29:34,850 --> 00:29:39,850 The cloud, I think, facilitates greater competition 571 00:29:39,850 --> 00:29:45,580 both as a support function and also a cost function. 572 00:29:45,580 --> 00:29:47,530 A lot of finance is about trust. 573 00:29:47,530 --> 00:29:50,650 Embedded in finance is trust, and we give data 574 00:29:50,650 --> 00:29:52,540 to financial firms. 575 00:29:52,540 --> 00:29:54,590 We want that data to be protected. 576 00:29:54,590 --> 00:29:57,520 Now earlier Dani raised the underbelly 577 00:29:57,520 --> 00:29:59,350 about how they use the data. 578 00:29:59,350 --> 00:30:00,980 But we want to protect it. 579 00:30:00,980 --> 00:30:05,050 So by using the cloud, any financial competitor 580 00:30:05,050 --> 00:30:09,250 can better insure for cyber security. 581 00:30:09,250 --> 00:30:13,990 One of the dominant things about the cloud is it can lower cost. 582 00:30:13,990 --> 00:30:17,530 It can provide a standardized form of data security or cyber 583 00:30:17,530 --> 00:30:19,140 security. 584 00:30:19,140 --> 00:30:23,830 And I think it somewhat levels the field of competition. 585 00:30:23,830 --> 00:30:25,300 But it's a supporting role. 586 00:30:25,300 --> 00:30:25,930 You're right. 587 00:30:25,930 --> 00:30:30,490 It's not something, necessarily, that's there for the users, 588 00:30:30,490 --> 00:30:33,070 for you and me to see. 589 00:30:33,070 --> 00:30:36,370 But we couldn't use Credit Karma, 590 00:30:36,370 --> 00:30:38,830 we couldn't use Robin Hood, we couldn't 591 00:30:38,830 --> 00:30:43,660 use many of the insurance apps if those venture capitalists 592 00:30:43,660 --> 00:30:46,930 and entrepreneurs behind it had decided to build their own data 593 00:30:46,930 --> 00:30:47,573 centers. 594 00:30:47,573 --> 00:30:49,240 They wouldn't have been able to stand up 595 00:30:49,240 --> 00:30:52,730 with the same scalability and same time frames 596 00:30:52,730 --> 00:30:54,560 at the same costs. 597 00:30:54,560 --> 00:30:58,610 So it's facilitated a tremendous amount of competition. 598 00:30:58,610 --> 00:31:00,560 AUDIENCE: Thank you. 599 00:31:00,560 --> 00:31:02,960 GARY GENSLER: So sometimes in infrastructure technology, 600 00:31:02,960 --> 00:31:06,110 I might consider the cloud an infrastructure technology 601 00:31:06,110 --> 00:31:08,960 rather than a user facing technology. 602 00:31:08,960 --> 00:31:10,940 But as an infrastructure technology, 603 00:31:10,940 --> 00:31:12,502 it's darn important. 604 00:31:16,360 --> 00:31:19,780 And so in that context, I've also sort of said, 605 00:31:19,780 --> 00:31:23,820 well, there's the customer interface and chat 606 00:31:23,820 --> 00:31:26,550 bots and open API or what we've been 607 00:31:26,550 --> 00:31:29,340 talking about in this class. 608 00:31:29,340 --> 00:31:31,650 But it all builds on that which came right 609 00:31:31,650 --> 00:31:35,720 before it, mobile phones, the internet, as we just said. 610 00:31:35,720 --> 00:31:39,950 And we've dominantly focused on customer interface and risk 611 00:31:39,950 --> 00:31:40,580 management. 612 00:31:40,580 --> 00:31:42,350 Risk management, what we've focused 613 00:31:42,350 --> 00:31:46,790 on in this class, machine learning dominantly, sensors 614 00:31:46,790 --> 00:31:49,520 and telematics in the insurance field, 615 00:31:49,520 --> 00:31:53,080 peer to peer lending is in the risk management side. 616 00:31:53,080 --> 00:31:54,920 But all of this built, and could not 617 00:31:54,920 --> 00:31:57,980 have been built without the asset-backed securitization. 618 00:31:57,980 --> 00:32:00,500 And like the cloud earlier, Hassan, 619 00:32:00,500 --> 00:32:03,800 if I can use that, the cloud is an infrastructure 620 00:32:03,800 --> 00:32:07,580 technology, the technology of risk management, 621 00:32:07,580 --> 00:32:11,000 that you could take a pool of mortgages, a pool of credit 622 00:32:11,000 --> 00:32:14,510 card receivables, a pool of auto loans, 623 00:32:14,510 --> 00:32:19,670 and take those and sell them into the securities markets, 624 00:32:19,670 --> 00:32:22,040 has facilitated a number of startups 625 00:32:22,040 --> 00:32:24,980 to say, look, I don't need to use balance sheet. 626 00:32:24,980 --> 00:32:31,120 I can maybe sell these pool of receivables and loans 627 00:32:31,120 --> 00:32:32,350 into the marketplace. 628 00:32:32,350 --> 00:32:36,070 I could be more like a broker, more on the front end, 629 00:32:36,070 --> 00:32:38,380 and give great customer interface, 630 00:32:38,380 --> 00:32:40,250 and then sell into the market. 631 00:32:40,250 --> 00:32:43,630 So some risk management tools that 632 00:32:43,630 --> 00:32:46,550 were inventions and new technologies of the 1970s 633 00:32:46,550 --> 00:32:52,640 to the 1990s allows, then, this build here. 634 00:32:52,640 --> 00:32:57,010 And so one of the central themes of this class that I want 635 00:32:57,010 --> 00:32:58,840 you to take away is not necessarily 636 00:32:58,840 --> 00:33:03,160 how machine learning works, not necessarily how open API works, 637 00:33:03,160 --> 00:33:07,930 but how new technologies come along in the past, 638 00:33:07,930 --> 00:33:09,400 and they will in the future-- 639 00:33:09,400 --> 00:33:11,600 every five to 15 years there's going 640 00:33:11,600 --> 00:33:14,650 to be some new technology. 641 00:33:14,650 --> 00:33:16,780 We seven billion humans haven't stopped 642 00:33:16,780 --> 00:33:19,420 coming up with innovations that are 643 00:33:19,420 --> 00:33:21,370 relevant to the financial world-- 644 00:33:21,370 --> 00:33:27,380 and as they do, think about how it opens a crack, how 645 00:33:27,380 --> 00:33:30,450 it creates an opportunity. 646 00:33:30,450 --> 00:33:32,060 And if you're at a big incumbent, 647 00:33:32,060 --> 00:33:35,620 if you're a Bank of America or Mitsui Bank, 648 00:33:35,620 --> 00:33:38,120 think about how it's going to create an opportunity not only 649 00:33:38,120 --> 00:33:40,880 for you, but your competitors. 650 00:33:40,880 --> 00:33:44,300 And if you don't act upon that new technology, somebody will. 651 00:33:44,300 --> 00:33:51,080 There'll be a new Transferwise or Revolut or a Robin Hood 652 00:33:51,080 --> 00:33:55,700 or Ally Pay that comes along to use that technology. 653 00:33:55,700 --> 00:33:57,980 And I think they will be dominantly 654 00:33:57,980 --> 00:34:02,280 in these two areas, customer interface 655 00:34:02,280 --> 00:34:03,870 and in risk management. 656 00:34:03,870 --> 00:34:06,480 But just like the cloud there could be also things 657 00:34:06,480 --> 00:34:08,600 that are pure infrastructure. 658 00:34:08,600 --> 00:34:10,710 And the cloud created opportunity 659 00:34:10,710 --> 00:34:14,170 and moved finance along. 660 00:34:14,170 --> 00:34:17,429 So that's kind of the central sort of conceptual framework 661 00:34:17,429 --> 00:34:20,020 that I bring to this. 662 00:34:20,020 --> 00:34:23,730 And I think that finance has a fertile ground. 663 00:34:23,730 --> 00:34:26,940 And the important opportunities in finance, 664 00:34:26,940 --> 00:34:28,260 digitization of money. 665 00:34:28,260 --> 00:34:31,219 Money is now electronic. 666 00:34:31,219 --> 00:34:34,040 Money itself is but a social construct. 667 00:34:34,040 --> 00:34:37,130 But we used to think about it in physical ways. 668 00:34:37,130 --> 00:34:40,969 Before a few decades ago, it was all really physical paper money 669 00:34:40,969 --> 00:34:42,830 or gold or silver. 670 00:34:42,830 --> 00:34:43,639 But frankly. 671 00:34:43,639 --> 00:34:45,050 We're in the 2020s. 672 00:34:45,050 --> 00:34:47,960 We're living through this coronavirus crisis. 673 00:34:47,960 --> 00:34:50,659 We will have accentuated that. 674 00:34:50,659 --> 00:34:53,310 There's a wide public acceptance of new tech. 675 00:34:53,310 --> 00:34:57,460 And each time there's a new tech, a new way to interface, 676 00:34:57,460 --> 00:35:01,420 though finance is a world of trust, 677 00:35:01,420 --> 00:35:06,510 we tend to have a lot of trust in new technology. 678 00:35:06,510 --> 00:35:08,940 Now often it's the new generation. 679 00:35:08,940 --> 00:35:13,290 It's going to be the Gen Zs and the Millennials 680 00:35:13,290 --> 00:35:16,850 before it's going to be folks of my generation. 681 00:35:16,850 --> 00:35:19,190 But there's still a wide acceptance of new tech. 682 00:35:19,190 --> 00:35:21,950 Look at all of us in the coronavirus crisis 683 00:35:21,950 --> 00:35:23,870 using Zoom and online. 684 00:35:23,870 --> 00:35:26,240 It's not as good as being in the classroom. 685 00:35:26,240 --> 00:35:28,760 I have to tell you this is definitely not 686 00:35:28,760 --> 00:35:30,410 as good as being in the classroom, 687 00:35:30,410 --> 00:35:33,410 but we're learning how to use this new tech. 688 00:35:33,410 --> 00:35:36,080 And the interface and the processing systems 689 00:35:36,080 --> 00:35:41,450 of the legacy incumbents leaves opportunities for competition. 690 00:35:41,450 --> 00:35:44,100 And of course, we've talked about data. 691 00:35:44,100 --> 00:35:47,125 I'm pausing, Romain, in case there are questions. 692 00:35:49,652 --> 00:35:51,360 ROMAIN: Let's give it a few more seconds. 693 00:35:51,360 --> 00:35:52,568 We have a question from Luke. 694 00:35:54,648 --> 00:35:55,690 AUDIENCE: Quick question. 695 00:35:55,690 --> 00:35:58,000 What do you think about encrypting the tech to money 696 00:35:58,000 --> 00:36:03,070 via blockchain so that we can kind of see where 697 00:36:03,070 --> 00:36:09,070 the public money is spent, i.e. 698 00:36:09,070 --> 00:36:11,350 Korea is actually trying to think about doing that. 699 00:36:11,350 --> 00:36:13,540 You mentioned China is thinking about doing that. 700 00:36:13,540 --> 00:36:17,410 This is more possible in more of the East Asian countries 701 00:36:17,410 --> 00:36:20,260 versus very, very high individual rights 702 00:36:20,260 --> 00:36:24,643 countries such as the US where they might be allergic to it. 703 00:36:24,643 --> 00:36:26,560 GARY GENSLER: So you've embedded a whole bunch 704 00:36:26,560 --> 00:36:27,730 of parts in there. 705 00:36:27,730 --> 00:36:34,420 But let me just say blockchain technology has 706 00:36:34,420 --> 00:36:37,780 a positive attribute that it's a shared public ledger. 707 00:36:37,780 --> 00:36:42,250 On some level, you can make data on that ledger public. 708 00:36:42,250 --> 00:36:46,570 You can also encrypt it and make it not necessarily public. 709 00:36:46,570 --> 00:36:49,510 So it depends on the architecture. 710 00:36:49,510 --> 00:36:54,850 But a blockchain technology with a shared ledger 711 00:36:54,850 --> 00:36:57,700 is what often is called tamper resistant. 712 00:36:57,700 --> 00:36:59,260 Tamper resistant, some people say 713 00:36:59,260 --> 00:37:02,470 immutable, which isn't quite technically correct. 714 00:37:02,470 --> 00:37:06,640 And so a government, or even a private sector firm, 715 00:37:06,640 --> 00:37:12,700 could use it to say that's a record of truth. 716 00:37:12,700 --> 00:37:17,440 That's a record that is tamper resistant. 717 00:37:17,440 --> 00:37:21,580 I haven't seen any government doing that yet. 718 00:37:21,580 --> 00:37:23,420 But it certainly can be done. 719 00:37:23,420 --> 00:37:25,750 It's been done more in the private sector. 720 00:37:25,750 --> 00:37:27,580 You'd still have to trust in the protocol. 721 00:37:27,580 --> 00:37:31,570 You'd have to trust in whatever nodes or network maintain 722 00:37:31,570 --> 00:37:33,130 that ledger. 723 00:37:33,130 --> 00:37:34,930 But it's a possibility going forward. 724 00:37:37,490 --> 00:37:39,250 So who are the actors in fintech? 725 00:37:39,250 --> 00:37:41,600 Who are the broad actors? 726 00:37:41,600 --> 00:37:42,850 Well, we've talked about them. 727 00:37:42,850 --> 00:37:45,700 Big finance, I like to quote the Central Bank 728 00:37:45,700 --> 00:37:48,190 governor of Brazil, who gave me this little 729 00:37:48,190 --> 00:37:51,160 saying where he said, big finance is like fortresses. 730 00:37:51,160 --> 00:37:55,390 They have moats and towers and sovereign affiliations. 731 00:37:55,390 --> 00:37:58,030 And I mean, it breaks down a little bit, too. 732 00:37:58,030 --> 00:38:00,160 But it has some apt parts of it, being 733 00:38:00,160 --> 00:38:03,820 the towers are big finance are dominant in payments. 734 00:38:03,820 --> 00:38:05,230 They have their balance sheets. 735 00:38:05,230 --> 00:38:07,540 They, too, collect data, and they're 736 00:38:07,540 --> 00:38:09,940 vast corporate structures, often, 737 00:38:09,940 --> 00:38:14,390 of 1,000 to 5,000 legal entities around the globe. 738 00:38:14,390 --> 00:38:16,270 But there's challenges to big finance, 739 00:38:16,270 --> 00:38:19,090 particularly in payments, and some of them 740 00:38:19,090 --> 00:38:21,070 are now even incumbents themselves 741 00:38:21,070 --> 00:38:25,120 like Pay Pal and Square and Stripe and the like. 742 00:38:25,120 --> 00:38:27,630 Startups of just 10 and 20 years ago, 743 00:38:27,630 --> 00:38:30,530 now they feel a little bit like incumbents as well. 744 00:38:30,530 --> 00:38:33,430 But I think big finance, as we talked earlier with Dani, 745 00:38:33,430 --> 00:38:35,440 I think big finance, we will still 746 00:38:35,440 --> 00:38:38,850 have banks in 10 and 30 years because there 747 00:38:38,850 --> 00:38:44,050 is network economics around balance sheets. 748 00:38:44,050 --> 00:38:49,750 Any study of diversification would tell you 749 00:38:49,750 --> 00:38:53,120 if you have a balance sheet that has thousands of loans on it 750 00:38:53,120 --> 00:38:57,370 rather than a single loan, you can lower the risk just 751 00:38:57,370 --> 00:39:00,910 through the diversification of the underwriting and the risk 752 00:39:00,910 --> 00:39:02,290 allocation. 753 00:39:02,290 --> 00:39:05,230 Also, big finance has data. 754 00:39:05,230 --> 00:39:07,150 But there's challenges to big finance 755 00:39:07,150 --> 00:39:10,000 for sure from big tech, and the Bank of International 756 00:39:10,000 --> 00:39:15,040 Settlement talks about big tech in terms of data networks 757 00:39:15,040 --> 00:39:16,180 and activities. 758 00:39:16,180 --> 00:39:18,700 Now the data is a little different than big finance 759 00:39:18,700 --> 00:39:22,160 because it includes data outside of the financial world. 760 00:39:22,160 --> 00:39:25,300 So they get a picture on all of our activity 761 00:39:25,300 --> 00:39:27,100 in a different way. 762 00:39:27,100 --> 00:39:32,590 Google has Google Chrome, it has Google Maps, it has Gmail. 763 00:39:32,590 --> 00:39:36,580 It has so many ways to assess data. 764 00:39:36,580 --> 00:39:42,310 Phone companies like Apple have a lot of sensor data as well. 765 00:39:42,310 --> 00:39:45,700 That sensor data is currently being used in insurance. 766 00:39:45,700 --> 00:39:48,640 But will it start to be used in credit and payment 767 00:39:48,640 --> 00:39:50,690 systems and the like? 768 00:39:50,690 --> 00:39:51,760 And then the startups. 769 00:39:51,760 --> 00:39:53,950 And I don't count the startups out, 770 00:39:53,950 --> 00:39:57,070 but I think to think about fintech just about startups 771 00:39:57,070 --> 00:39:59,380 is too narrow, and it's why I think 772 00:39:59,380 --> 00:40:02,530 of fintech around these three sets of actors. 773 00:40:02,530 --> 00:40:05,250 But startups, disruptive innovators, 774 00:40:05,250 --> 00:40:12,040 if you throw up 100 finance startups or fintech startups, 775 00:40:12,040 --> 00:40:14,950 two to five of them are just going to break through. 776 00:40:14,950 --> 00:40:16,810 I mean, there's some odds to this 777 00:40:16,810 --> 00:40:18,850 that some will break through. 778 00:40:18,850 --> 00:40:20,480 And they have flexibility, and they're 779 00:40:20,480 --> 00:40:23,000 a bit of asymmetric risk takers. 780 00:40:23,000 --> 00:40:26,980 They don't have business models to support and protect. 781 00:40:26,980 --> 00:40:31,270 And often big finance is protecting a business model. 782 00:40:31,270 --> 00:40:34,060 Credit cards in the US are dominated 783 00:40:34,060 --> 00:40:35,590 by seven big companies. 784 00:40:35,590 --> 00:40:39,430 And those seven big companies that dominate in the US, 785 00:40:39,430 --> 00:40:42,010 they want to protect some of their profit and revenue 786 00:40:42,010 --> 00:40:43,150 models. 787 00:40:43,150 --> 00:40:46,420 And so that gives an opportunity for companies like Toast 788 00:40:46,420 --> 00:40:50,380 to get into the restaurant business. 789 00:40:50,380 --> 00:40:52,090 It's a little harder for a big actor 790 00:40:52,090 --> 00:40:55,750 to go into those individual sectors and the like. 791 00:40:55,750 --> 00:40:57,610 They also have a bit of asymmetric risk 792 00:40:57,610 --> 00:40:59,530 as it relates to regulation, and this is 793 00:40:59,530 --> 00:41:01,600 what Dani was saying earlier. 794 00:41:01,600 --> 00:41:03,730 It's neither good nor bad. 795 00:41:03,730 --> 00:41:08,260 It's just an observation that startups often 796 00:41:08,260 --> 00:41:12,940 have a little bit of an attitude as try it out. 797 00:41:12,940 --> 00:41:15,910 If it breaks some rules somewhere, clean it up. 798 00:41:15,910 --> 00:41:21,010 Work in good faith with the regulators and the like. 799 00:41:21,010 --> 00:41:23,110 And because they're smaller, often 800 00:41:23,110 --> 00:41:25,840 regulators around the globe have decided 801 00:41:25,840 --> 00:41:27,250 to give a little forbearance. 802 00:41:27,250 --> 00:41:29,860 There's something called regulatory sandboxes 803 00:41:29,860 --> 00:41:31,810 to promote innovation. 804 00:41:31,810 --> 00:41:34,550 If they were larger, there would be more systemic 805 00:41:34,550 --> 00:41:38,140 and customer-facing problems. 806 00:41:38,140 --> 00:41:40,720 And then fourthly, there's the official sector. 807 00:41:40,720 --> 00:41:45,190 And so while finance, tech, and startups are competing and sort 808 00:41:45,190 --> 00:41:47,860 of trying to provide better products and better 809 00:41:47,860 --> 00:41:50,140 opportunities and user interfaces, 810 00:41:50,140 --> 00:41:52,900 the official sector cares about economic growth, 811 00:41:52,900 --> 00:41:55,540 but they also care about financial stability. 812 00:41:55,540 --> 00:41:59,830 And sometimes they're a little bit in conflict. 813 00:41:59,830 --> 00:42:03,850 Also, guarding against illicit activity, promoting inclusion, 814 00:42:03,850 --> 00:42:06,850 and the tenets of investor and consumer protection. 815 00:42:09,730 --> 00:42:13,660 So what were the big technologies we talked about? 816 00:42:13,660 --> 00:42:15,780 And Romain, I'm going to pause to see 817 00:42:15,780 --> 00:42:17,070 if there are any questions. 818 00:42:19,890 --> 00:42:20,890 Still there? 819 00:42:20,890 --> 00:42:21,690 ROMAIN: Yes. 820 00:42:21,690 --> 00:42:23,610 I don't see any so far. 821 00:42:23,610 --> 00:42:25,800 GARY GENSLER: I just wanted to make sure my internet 822 00:42:25,800 --> 00:42:27,790 connection didn't go. 823 00:42:27,790 --> 00:42:31,800 So right now in finance, where we've 824 00:42:31,800 --> 00:42:34,890 seen machine learning, this extraordinary ability 825 00:42:34,890 --> 00:42:40,880 to extract patterns, extract correlations out of data. 826 00:42:40,880 --> 00:42:44,180 We've seen it primarily in the middle 827 00:42:44,180 --> 00:42:49,430 here around fraud detection and prevention, regulatory, 828 00:42:49,430 --> 00:42:52,190 that a lot of the credit card companies and big banks 829 00:42:52,190 --> 00:42:56,720 are using it to extract correlations to better do 830 00:42:56,720 --> 00:42:59,810 fraud protection and better do what's called any money 831 00:42:59,810 --> 00:43:02,160 laundering and compliance. 832 00:43:02,160 --> 00:43:04,970 So that big bucket is compliance. 833 00:43:04,970 --> 00:43:07,820 I'd say the second most developed area 834 00:43:07,820 --> 00:43:10,910 is around natural language processing or call centers, 835 00:43:10,910 --> 00:43:12,980 chat bots, robo advising. 836 00:43:12,980 --> 00:43:17,330 We all have studied together robo advising and investments 837 00:43:17,330 --> 00:43:24,470 like around firms like Betterment and Wealthfront, 838 00:43:24,470 --> 00:43:27,500 I think it's called, and others. 839 00:43:27,500 --> 00:43:30,350 But robo advising, both in the credit space, 840 00:43:30,350 --> 00:43:34,430 investing space as well. 841 00:43:34,430 --> 00:43:40,440 And Bank of America has their voice assistant called Erica. 842 00:43:40,440 --> 00:43:44,600 The Siris and Alexas of finance are going 843 00:43:44,600 --> 00:43:49,640 to be built out in the 2020s. 844 00:43:49,640 --> 00:43:52,040 But I think the most interesting area, 845 00:43:52,040 --> 00:43:54,950 which is not, frankly, used that much yet-- 846 00:43:54,950 --> 00:43:57,380 it's being used, but not a lot-- 847 00:43:57,380 --> 00:43:59,840 is around the underwriting decisions, underwriting 848 00:43:59,840 --> 00:44:02,560 and insurance, underwriting in credit. 849 00:44:02,560 --> 00:44:05,570 Basically, how do you allocate and extend 850 00:44:05,570 --> 00:44:08,450 and price credit or risk? 851 00:44:08,450 --> 00:44:11,960 This is at the core of finance because the core of finance, 852 00:44:11,960 --> 00:44:13,760 that sort of neck of the hourglass, 853 00:44:13,760 --> 00:44:17,020 is standing between savers and borrowers, 854 00:44:17,020 --> 00:44:20,360 or standing between those who have a risk and those 855 00:44:20,360 --> 00:44:22,580 who are willing to bear a risk. 856 00:44:22,580 --> 00:44:24,770 And as you're at that neck of the hourglass, 857 00:44:24,770 --> 00:44:27,920 if you can use machine learning to better allocate 858 00:44:27,920 --> 00:44:35,040 price and extend credit or insurance, 859 00:44:35,040 --> 00:44:36,870 then you're going to use that technology. 860 00:44:36,870 --> 00:44:41,440 And extracting patterns is an important part to do that. 861 00:44:41,440 --> 00:44:43,320 Now you need to do it within the law you 862 00:44:43,320 --> 00:44:47,010 need to do it within these questions of fairness 863 00:44:47,010 --> 00:44:48,990 and explainability. 864 00:44:48,990 --> 00:44:51,900 But I think we're going to see this grow dramatically 865 00:44:51,900 --> 00:44:53,020 by the mid 2020s. 866 00:44:53,020 --> 00:44:54,060 It's already there. 867 00:44:54,060 --> 00:44:55,310 I'm not saying it's not there. 868 00:44:55,310 --> 00:44:58,350 I'm just saying we will see machine learning used 869 00:44:58,350 --> 00:45:02,670 much more, especially around alternative data. 870 00:45:02,670 --> 00:45:05,280 We've seen it a bit in asset management and trading, 871 00:45:05,280 --> 00:45:10,080 but frankly, it's not dominating the trading floors yet. 872 00:45:10,080 --> 00:45:13,980 It's not dominating the hedge funds yet. 873 00:45:13,980 --> 00:45:15,570 And a lot of folks in the hedge fund 874 00:45:15,570 --> 00:45:17,880 community and high frequency trading community 875 00:45:17,880 --> 00:45:22,500 say that their algorithms, their linear sort 876 00:45:22,500 --> 00:45:26,640 of type of regressions and classic statistics, 877 00:45:26,640 --> 00:45:28,860 are doing well enough. 878 00:45:28,860 --> 00:45:31,740 But I think we'll see this dramatically shift. 879 00:45:31,740 --> 00:45:34,170 And I think at times, the real question 880 00:45:34,170 --> 00:45:36,750 is, will machine learning be better 881 00:45:36,750 --> 00:45:40,500 than a classic set of regression analyses, a classic set 882 00:45:40,500 --> 00:45:42,790 of statistics that you can do? 883 00:45:42,790 --> 00:45:44,330 The answer is if it's not, you'll 884 00:45:44,330 --> 00:45:46,710 stick with the classic statistics. 885 00:45:46,710 --> 00:45:52,500 It's not as if you have to throw away the classic data analysis. 886 00:45:52,500 --> 00:45:55,920 This is just a stronger tool in certain circumstances 887 00:45:55,920 --> 00:45:59,100 to add on top of it. 888 00:45:59,100 --> 00:46:02,030 Romain, questions? 889 00:46:02,030 --> 00:46:05,000 ROMAIN: Let's give it a few seconds. 890 00:46:05,000 --> 00:46:06,350 One, two, three. 891 00:46:06,350 --> 00:46:08,483 No questions, Gary. 892 00:46:08,483 --> 00:46:09,900 GARY GENSLER: There is this debate 893 00:46:09,900 --> 00:46:12,750 sometimes, is artificial intelligence machine learning 894 00:46:12,750 --> 00:46:14,220 a tool or a Service? 895 00:46:14,220 --> 00:46:17,310 Is it itself an industry or not? 896 00:46:17,310 --> 00:46:19,790 I find myself thinking more and more, 897 00:46:19,790 --> 00:46:24,010 it's frankly a tool dominantly. 898 00:46:24,010 --> 00:46:26,860 But it is also a service at times. 899 00:46:26,860 --> 00:46:29,710 It is a tool that is already being used 900 00:46:29,710 --> 00:46:32,740 and will be used more and more inside of big finance, 901 00:46:32,740 --> 00:46:35,950 big tech, and disruptors. 902 00:46:35,950 --> 00:46:39,070 But that's not to say it's not also a service at times. 903 00:46:39,070 --> 00:46:43,240 It can be something like I'm showing here 904 00:46:43,240 --> 00:46:44,860 as a compliance software. 905 00:46:44,860 --> 00:46:47,200 So you can stand up a company that does 906 00:46:47,200 --> 00:46:49,540 one of these things going back. 907 00:46:49,540 --> 00:46:54,840 You can stand up a company that does fraud detection better 908 00:46:54,840 --> 00:46:58,890 than the banks do right now or stands up something 909 00:46:58,890 --> 00:47:03,240 around credit or insurance, as a number of startups 910 00:47:03,240 --> 00:47:05,700 have done in the insurance field that 911 00:47:05,700 --> 00:47:10,060 can take data in, access data. 912 00:47:10,060 --> 00:47:12,640 And maybe you're a data aggregator from the payment 913 00:47:12,640 --> 00:47:16,310 space like Plaid , or a data aggregator in insurance 914 00:47:16,310 --> 00:47:18,550 that's taking in all that telematics data from 915 00:47:18,550 --> 00:47:20,350 automobiles. 916 00:47:20,350 --> 00:47:25,240 And taking that data, you can use AI as a service. 917 00:47:25,240 --> 00:47:27,940 And some of these, like anti-fraud softwares 918 00:47:27,940 --> 00:47:30,580 like Featurespace and others, or Tractable, 919 00:47:30,580 --> 00:47:33,700 which is doing insurance claims processing, 920 00:47:33,700 --> 00:47:36,190 are doing AI as a service. 921 00:47:36,190 --> 00:47:39,850 I think you'll see, though, that the dominant way we're going 922 00:47:39,850 --> 00:47:43,750 to be thinking about AI, really already now, 923 00:47:43,750 --> 00:47:45,460 but within a few years, it's a tool 924 00:47:45,460 --> 00:47:47,950 that if you don't have it in your toolkit, 925 00:47:47,950 --> 00:47:51,550 if you don't use machine learning to do better data 926 00:47:51,550 --> 00:47:55,480 analytics, you're going to fall behind somewhere, 927 00:47:55,480 --> 00:47:57,430 whether it's in your compliance side 928 00:47:57,430 --> 00:47:59,830 or whether it's in your underwriting side. 929 00:47:59,830 --> 00:48:02,290 And others who use machine learning 930 00:48:02,290 --> 00:48:08,290 will have a more nuanced, more flexible, and ultimately higher 931 00:48:08,290 --> 00:48:12,190 revenue potential, lower cost potential underwriting 932 00:48:12,190 --> 00:48:15,420 of insurance or credit. 933 00:48:15,420 --> 00:48:19,020 But I'd think about it as both ways. 934 00:48:19,020 --> 00:48:21,390 Natural language processing, a form 935 00:48:21,390 --> 00:48:24,930 of artificial intelligence, but important slipstream. 936 00:48:24,930 --> 00:48:30,540 It's really about taking human words and computer language 937 00:48:30,540 --> 00:48:31,950 and doing the interface. 938 00:48:31,950 --> 00:48:36,290 Content generation, content summarization, and the like. 939 00:48:36,290 --> 00:48:38,850 And so what are we already using it for in finance? 940 00:48:38,850 --> 00:48:40,170 A lot of ways. 941 00:48:40,170 --> 00:48:43,590 Customer service, those chat bots and the Ericas 942 00:48:43,590 --> 00:48:48,000 at Bank of America and the robo advisors and the like. 943 00:48:48,000 --> 00:48:48,990 That's a big piece. 944 00:48:48,990 --> 00:48:53,340 But that's going to continue to be facilitated and go further, 945 00:48:53,340 --> 00:48:56,700 where one day we'll get on the phone and we won't quite know, 946 00:48:56,700 --> 00:49:00,360 are we talking to a human or a chat bot on the other line? 947 00:49:00,360 --> 00:49:04,720 Chat bots right now can't deal with sarcasm too well. 948 00:49:04,720 --> 00:49:08,520 So you can kind of suss it out, still, a little bit 949 00:49:08,520 --> 00:49:11,190 with a little bit of sarcasm. 950 00:49:11,190 --> 00:49:15,360 But I don't know, in a number of years, what it will be. 951 00:49:15,360 --> 00:49:18,480 Basic process automation, and an area 952 00:49:18,480 --> 00:49:21,750 that asset managers are using more and more, still 953 00:49:21,750 --> 00:49:23,760 just a little bit, using more and more, 954 00:49:23,760 --> 00:49:29,340 sentiment analysis, analyzing vast amount of spoken words, 955 00:49:29,340 --> 00:49:31,260 written words, and seeing if they 956 00:49:31,260 --> 00:49:34,703 can assess a sentiment that might be important 957 00:49:34,703 --> 00:49:35,745 investing in the markets. 958 00:49:39,310 --> 00:49:42,520 We spent a fair amount of time on open API and open banking. 959 00:49:42,520 --> 00:49:44,830 And the critical thing here is really 960 00:49:44,830 --> 00:49:49,330 access to data and an ability to build software 961 00:49:49,330 --> 00:49:52,490 on top of platforms. 962 00:49:52,490 --> 00:49:55,900 So in this case, think of big finance as the platforms, 963 00:49:55,900 --> 00:49:59,560 just like you might think of Facebook as a platform. 964 00:49:59,560 --> 00:50:03,460 Can you build some software on top of it? 965 00:50:03,460 --> 00:50:07,960 Maybe it's software to do one small aspect of finance 966 00:50:07,960 --> 00:50:12,790 better than the big platform is doing right now, just like you 967 00:50:12,790 --> 00:50:17,620 can build one small application on top of Facebook and say, 968 00:50:17,620 --> 00:50:18,940 we can do something better. 969 00:50:18,940 --> 00:50:22,150 Now Facebook and other technology platforms 970 00:50:22,150 --> 00:50:25,420 sought to facilitate this through open API, 971 00:50:25,420 --> 00:50:28,720 saying developers can build on top of our platforms. 972 00:50:28,720 --> 00:50:31,590 Big finance was of mixed minds. 973 00:50:31,590 --> 00:50:33,550 Some of big finance wanted to promote it. 974 00:50:33,550 --> 00:50:34,720 Some didn't. 975 00:50:34,720 --> 00:50:38,050 The official sector stepped in, particularly in Europe, 976 00:50:38,050 --> 00:50:41,650 with the payment systems directive and other directives 977 00:50:41,650 --> 00:50:44,950 in the United Kingdom around open API. 978 00:50:44,950 --> 00:50:47,650 But then around the globe, Brazil and other countries, 979 00:50:47,650 --> 00:50:50,260 as we talked about, said no, we've 980 00:50:50,260 --> 00:50:52,800 got to promote some competition here 981 00:50:52,800 --> 00:50:56,080 and share data, in essence. 982 00:50:56,080 --> 00:50:58,300 The shared data is permission by us. 983 00:50:58,300 --> 00:51:02,830 That means we say, yes, you can have my data. 984 00:51:02,830 --> 00:51:07,050 It's sitting there at Bank of America, but you can have it. 985 00:51:07,050 --> 00:51:09,390 The alternatives-- and this is kind 986 00:51:09,390 --> 00:51:11,850 of an interesting technological competition-- 987 00:51:11,850 --> 00:51:16,180 the alternatives was, I could permission 988 00:51:16,180 --> 00:51:18,610 an application like Credit Karma to go 989 00:51:18,610 --> 00:51:21,320 get my data at Bank of America. 990 00:51:21,320 --> 00:51:24,400 And if Bank of America did not provide an open API 991 00:51:24,400 --> 00:51:27,610 to Credit Karma, or an open API, possibly, 992 00:51:27,610 --> 00:51:31,120 to Plaid, which is a data aggregator, 993 00:51:31,120 --> 00:51:36,590 that Plaid then created data to Credit Karma. 994 00:51:36,590 --> 00:51:40,340 If Bank of America said now, then maybe Credit Karma 995 00:51:40,340 --> 00:51:43,100 would go and do something called screen scraping, 996 00:51:43,100 --> 00:51:46,340 literally taking pictures of screens using technology 997 00:51:46,340 --> 00:51:49,670 to get that data, or reverse engineering 998 00:51:49,670 --> 00:51:52,600 of a robotic process automation. 999 00:51:52,600 --> 00:51:54,350 So there was a little bit of a competition 1000 00:51:54,350 --> 00:51:59,080 that open API is also a safer, more secure way to do it, 1001 00:51:59,080 --> 00:52:01,890 a more standardized way to do it. 1002 00:52:01,890 --> 00:52:05,810 So what we've seen is banks kind of-- 1003 00:52:05,810 --> 00:52:09,050 a little bit disappointing to their business model-- 1004 00:52:09,050 --> 00:52:11,630 banks kind of coming into this. 1005 00:52:11,630 --> 00:52:14,030 As I said, many banks thought it was a good idea 1006 00:52:14,030 --> 00:52:17,450 to be more like Facebook and let startups build apps 1007 00:52:17,450 --> 00:52:18,830 on top of them. 1008 00:52:18,830 --> 00:52:20,570 Some banks said no. 1009 00:52:20,570 --> 00:52:22,730 The official sector got involved. 1010 00:52:22,730 --> 00:52:26,330 But also, technological competition got involved, 1011 00:52:26,330 --> 00:52:32,000 this screen scraping and reverse process automation. 1012 00:52:32,000 --> 00:52:33,125 Romain, I'm going to pause. 1013 00:52:36,940 --> 00:52:41,170 ROMAIN: Let's see if the class has any questions. 1014 00:52:41,170 --> 00:52:44,070 GARY GENSLER: And I assure you that more opportunities 1015 00:52:44,070 --> 00:52:46,430 hacve been created around open API 1016 00:52:46,430 --> 00:52:50,540 and open banking than we could ever talk about in a semester. 1017 00:52:50,540 --> 00:52:54,830 And it's usually sort of thought of as a little bit 1018 00:52:54,830 --> 00:52:57,470 infrastructure, a little bit back office. 1019 00:52:57,470 --> 00:53:00,890 But there's rarely a central bank governor around the globe 1020 00:53:00,890 --> 00:53:03,080 that isn't talking about this, thinking about this, 1021 00:53:03,080 --> 00:53:06,980 how to promote competition within their banking sectors 1022 00:53:06,980 --> 00:53:09,998 and in their countries. 1023 00:53:09,998 --> 00:53:11,540 AUDIENCE: We don't have any hands up, 1024 00:53:11,540 --> 00:53:14,302 so we can continue, Gary. 1025 00:53:14,302 --> 00:53:16,510 GARY GENSLER: I mentioned robotic process automation. 1026 00:53:16,510 --> 00:53:18,550 Some of robotic process automation 1027 00:53:18,550 --> 00:53:22,480 is just the startups using it to compete and get 1028 00:53:22,480 --> 00:53:26,410 the data in a closed or more resilient 1029 00:53:26,410 --> 00:53:28,540 system that doesn't want to give them data. 1030 00:53:28,540 --> 00:53:31,570 In some countries, you can't get it easily 1031 00:53:31,570 --> 00:53:38,185 through the market structures or the official sanctioned open 1032 00:53:38,185 --> 00:53:40,120 API. 1033 00:53:40,120 --> 00:53:44,320 But it's also being used by incumbents just to accounts 1034 00:53:44,320 --> 00:53:45,070 and onboarding. 1035 00:53:45,070 --> 00:53:47,350 It's basically taking something that humans 1036 00:53:47,350 --> 00:53:49,900 were doing and automating it. 1037 00:53:49,900 --> 00:53:52,480 And it's called robotic process automation. 1038 00:53:52,480 --> 00:53:54,070 But just think of any process. 1039 00:53:54,070 --> 00:54:00,000 It can be reading documents and onboarding a client. 1040 00:54:00,000 --> 00:54:02,580 So how does a big wealth management company, 1041 00:54:02,580 --> 00:54:06,850 how does an online broker or a mobile broker or online us now, 1042 00:54:06,850 --> 00:54:11,340 and that you can literally just scan the documents. 1043 00:54:11,340 --> 00:54:14,900 It reads it, and voila, we're in, 1044 00:54:14,900 --> 00:54:17,970 that we don't have to go to some bricks and mortar place 1045 00:54:17,970 --> 00:54:22,180 and get physical documents any longer. 1046 00:54:22,180 --> 00:54:24,270 So it's an important piece of competition 1047 00:54:24,270 --> 00:54:30,600 in lowering costs, also, and enhancing user interfaces. 1048 00:54:30,600 --> 00:54:34,080 Let me talk a little bit about credit scores and alternative 1049 00:54:34,080 --> 00:54:35,280 data. 1050 00:54:35,280 --> 00:54:39,690 The Fair Isaac Company, founded about 50 to 60 years ago 1051 00:54:39,690 --> 00:54:45,680 by a couple of Stanford graduates named Fair and Isaac, 1052 00:54:45,680 --> 00:54:48,320 so thus FICO. 1053 00:54:48,320 --> 00:54:50,378 It's used in 32 countries. 1054 00:54:50,378 --> 00:54:52,170 There are many countries that don't use it, 1055 00:54:52,170 --> 00:54:54,200 but it is a dominant way to think 1056 00:54:54,200 --> 00:54:57,290 about credit scoring in the latter part 1057 00:54:57,290 --> 00:55:00,410 of the 20th century and even the early 21st century. 1058 00:55:00,410 --> 00:55:05,330 And here's the ratios, that 30% of this and 35% of that. 1059 00:55:05,330 --> 00:55:06,680 It's very static. 1060 00:55:06,680 --> 00:55:08,360 It's been updated about eight times. 1061 00:55:08,360 --> 00:55:13,700 There's a rollout of a new FICO score this summer of 2020 1062 00:55:13,700 --> 00:55:14,990 as well. 1063 00:55:14,990 --> 00:55:19,370 But alternative data might give us a better picture on somebody 1064 00:55:19,370 --> 00:55:22,580 and whether they're going to default on a loan, 1065 00:55:22,580 --> 00:55:25,680 or whether they're the right insurance risk. 1066 00:55:25,680 --> 00:55:27,890 And so the first sort of rudimentary level 1067 00:55:27,890 --> 00:55:31,100 of alternative data that goes beyond FICO 1068 00:55:31,100 --> 00:55:33,800 score is your employment or income, 1069 00:55:33,800 --> 00:55:37,610 or whether you even paid your rent on time. 1070 00:55:37,610 --> 00:55:40,310 FICO score traditionally hasn't used 1071 00:55:40,310 --> 00:55:43,040 even whether you're paying your utilities or rent 1072 00:55:43,040 --> 00:55:45,130 checks on time. 1073 00:55:45,130 --> 00:55:49,000 Or, as Ally Pay and WeChat Pay can do, 1074 00:55:49,000 --> 00:55:50,980 look at a small business and look 1075 00:55:50,980 --> 00:55:55,660 at your whole cash flow picture, your revenues and your costs. 1076 00:55:55,660 --> 00:55:58,750 Or here in the US, Intuit, which Intuit 1077 00:55:58,750 --> 00:56:02,590 is a company that has TurboTax and has more recently bought 1078 00:56:02,590 --> 00:56:06,280 some fintech startups as well, that Intuit 1079 00:56:06,280 --> 00:56:10,140 can see the whole cash flow picture around you. 1080 00:56:10,140 --> 00:56:13,210 And so why is Intuit starting to think about credit? 1081 00:56:13,210 --> 00:56:17,080 Because they can get a really good picture around you. 1082 00:56:17,080 --> 00:56:18,850 But there's also everything we do 1083 00:56:18,850 --> 00:56:22,730 online, our consumption and purchase transactional data. 1084 00:56:22,730 --> 00:56:26,200 Think Visa and Mastercard and what they know about us, 1085 00:56:26,200 --> 00:56:30,970 or what our bank knows about our consumption and purchase data, 1086 00:56:30,970 --> 00:56:35,950 using that to say if we buy one product versus another, 1087 00:56:35,950 --> 00:56:40,600 are we a better credit risk or a lesser credit risk? 1088 00:56:40,600 --> 00:56:42,760 In an earlier era, one of the things 1089 00:56:42,760 --> 00:56:46,930 that people would look at, and this is just a narrow slice, 1090 00:56:46,930 --> 00:56:49,990 is whether you replaced your automobile tires 1091 00:56:49,990 --> 00:56:52,390 on a regular basis with new tires, 1092 00:56:52,390 --> 00:56:55,810 or whether you bought retreads, literally putting rubber 1093 00:56:55,810 --> 00:56:57,760 on the old tires. 1094 00:56:57,760 --> 00:57:01,960 And it was found that new tire purchasers on a regular basis 1095 00:57:01,960 --> 00:57:04,930 were better credits than if you bought retreads. 1096 00:57:04,930 --> 00:57:06,400 Well, expand that out. 1097 00:57:06,400 --> 00:57:08,290 What purchase history would tell you 1098 00:57:08,290 --> 00:57:13,480 that somebody is a little less or a little bit more capable 1099 00:57:13,480 --> 00:57:16,960 to hold a loan going forward? 1100 00:57:16,960 --> 00:57:20,230 But app usage, browsing history, emails, et cetera, 1101 00:57:20,230 --> 00:57:22,030 your geolocation. 1102 00:57:22,030 --> 00:57:25,480 And in China, we see this being used in a social credit scoring 1103 00:57:25,480 --> 00:57:29,530 system where the government, in coordination 1104 00:57:29,530 --> 00:57:35,330 with many online services, including Ally Pay and WeChat 1105 00:57:35,330 --> 00:57:40,410 Pay, but even extended to social networking sites 1106 00:57:40,410 --> 00:57:41,970 like dating apps. 1107 00:57:41,970 --> 00:57:45,450 Literally the dating apps are feeding in some information 1108 00:57:45,450 --> 00:57:49,020 into the social credit scoring system in China 1109 00:57:49,020 --> 00:57:53,710 that then can be used about extension of credit or not. 1110 00:57:53,710 --> 00:57:57,070 And then thirdly, particularly out of the insurance field 1111 00:57:57,070 --> 00:57:59,920 and insurtech. 1112 00:57:59,920 --> 00:58:01,720 But my question is, how will this 1113 00:58:01,720 --> 00:58:05,230 be used all the way into credit extension as well? 1114 00:58:05,230 --> 00:58:07,330 Is the information being collected 1115 00:58:07,330 --> 00:58:11,290 through drones and Internet of Things and sensors 1116 00:58:11,290 --> 00:58:13,150 throughout society? 1117 00:58:13,150 --> 00:58:15,100 And we're moving into a world now 1118 00:58:15,100 --> 00:58:17,950 where we'll go from tens of billions 1119 00:58:17,950 --> 00:58:19,900 of sensors around the globe, and that's 1120 00:58:19,900 --> 00:58:23,260 the rough measurement now, to hundreds of billions 1121 00:58:23,260 --> 00:58:25,980 of sensors around the globe. 1122 00:58:25,980 --> 00:58:29,180 And whether it's in our refrigerators at home, 1123 00:58:29,180 --> 00:58:32,980 whether it's in our automobiles, whether it's wearables, 1124 00:58:32,980 --> 00:58:34,990 whether, frankly, one day it's in the paint 1125 00:58:34,990 --> 00:58:38,840 we paint our houses, literally. 1126 00:58:38,840 --> 00:58:41,720 That type of sensor technology, then 1127 00:58:41,720 --> 00:58:45,320 how does it feed back into, first, in insurance, 1128 00:58:45,320 --> 00:58:49,790 our homeowners insurance, our automobile insurance, 1129 00:58:49,790 --> 00:58:52,490 our health insurance from a wearable, 1130 00:58:52,490 --> 00:58:56,840 but how that data analytics then moves into our credit 1131 00:58:56,840 --> 00:58:58,920 scoring as well. 1132 00:58:58,920 --> 00:59:02,510 Now all of this has to be done somehow within social norms 1133 00:59:02,510 --> 00:59:05,930 and laws about fairness and explainability 1134 00:59:05,930 --> 00:59:08,600 and guarding against biases. 1135 00:59:08,600 --> 00:59:11,570 And those are real and important issues, 1136 00:59:11,570 --> 00:59:13,400 but I think that's where we're headed 1137 00:59:13,400 --> 00:59:15,950 with a lot of this alternative data. 1138 00:59:15,950 --> 00:59:18,190 Questions? 1139 00:59:18,190 --> 00:59:20,870 This is sthe stuff Dani gets a little nervous about, I think. 1140 00:59:24,870 --> 00:59:26,980 ROMAIN: Danielle? 1141 00:59:26,980 --> 00:59:29,500 GARY GENSLER: I knew I would spark something. 1142 00:59:29,500 --> 00:59:32,500 AUDIENCE: Well, since you asked. 1143 00:59:32,500 --> 00:59:34,960 So there is a international framework 1144 00:59:34,960 --> 00:59:38,710 of laws around the right to privacy from the human rights 1145 00:59:38,710 --> 00:59:42,010 space that are applicable to some 1146 00:59:42,010 --> 00:59:45,040 of the emerging ways in which we're seeing data be collected. 1147 00:59:45,040 --> 00:59:47,875 And I'm just curious your thoughts on 1148 00:59:47,875 --> 00:59:50,500 whether you see that ever really getting traction, particularly 1149 00:59:50,500 --> 00:59:55,930 in the US, in terms of impacting regulations around how data 1150 00:59:55,930 --> 00:59:59,290 is collected, how it's used. 1151 00:59:59,290 --> 01:00:01,413 I realize it's quite an open-ended question. 1152 01:00:01,413 --> 01:00:02,830 GARY GENSLER: I think that this is 1153 01:00:02,830 --> 01:00:05,260 going to be an ongoing public policy 1154 01:00:05,260 --> 01:00:07,060 debate and cultural debate. 1155 01:00:07,060 --> 01:00:09,175 And it's not something that will settle down 1156 01:00:09,175 --> 01:00:10,300 in just two or three years. 1157 01:00:10,300 --> 01:00:15,310 I think this is a multi-decade how we feel about this, 1158 01:00:15,310 --> 01:00:23,950 that as sensors, particularly, will go from 20, 1159 01:00:23,950 --> 01:00:27,310 30 billion or so sensors already00 maybe it's maybe 1160 01:00:27,310 --> 01:00:30,010 it's even further than that now to hundreds of billions 1161 01:00:30,010 --> 01:00:31,120 of sensors-- 1162 01:00:31,120 --> 01:00:36,360 as they become truly ubiquitous, and cameras 1163 01:00:36,360 --> 01:00:39,070 in the homes and the like, that data 1164 01:00:39,070 --> 01:00:42,760 is going to be collected, analyzed, stored, assessed, 1165 01:00:42,760 --> 01:00:46,480 and used in some way to underwrite insurance and extend 1166 01:00:46,480 --> 01:00:47,710 credit. 1167 01:00:47,710 --> 01:00:51,310 I think that's the large cultural trend 1168 01:00:51,310 --> 01:00:54,040 and technological and financial trend we're on. 1169 01:00:54,040 --> 01:00:55,490 So then how do we deal with that? 1170 01:00:55,490 --> 01:00:58,880 How do we feel about that, coupled with all the data 1171 01:00:58,880 --> 01:01:02,230 that's being collected as we are online 1172 01:01:02,230 --> 01:01:05,890 in our geolocations with our cell phones? 1173 01:01:05,890 --> 01:01:10,840 So we've seen some jurisdictions like Europe pass a General Data 1174 01:01:10,840 --> 01:01:13,090 Protection Regulation, GDPR. 1175 01:01:13,090 --> 01:01:16,810 We've seen in the US it's more state-based, California. 1176 01:01:16,810 --> 01:01:20,750 But even years ago, Illinois put something in place 1177 01:01:20,750 --> 01:01:25,570 around photographs of our face that are unique to us 1178 01:01:25,570 --> 01:01:27,890 and so forth. 1179 01:01:27,890 --> 01:01:31,460 So a handful of states, and so forth. 1180 01:01:31,460 --> 01:01:33,460 I think we're going to keep trying to figure out 1181 01:01:33,460 --> 01:01:34,690 what's the right balance. 1182 01:01:34,690 --> 01:01:37,240 And even this crisis we're living through right, now 1183 01:01:37,240 --> 01:01:43,760 the coronavirus crisis, about contact tracing. 1184 01:01:43,760 --> 01:01:47,560 If we can save lives, and we can legitimately 1185 01:01:47,560 --> 01:01:50,380 really save lives and have better health care 1186 01:01:50,380 --> 01:01:55,960 outcomes by tracing our contacts out, 1187 01:01:55,960 --> 01:01:58,690 not just who we've seen in the last two days, 1188 01:01:58,690 --> 01:02:01,510 but the last 14 days, and who they've seen, 1189 01:02:01,510 --> 01:02:04,830 and how some countries like South Korea, 1190 01:02:04,830 --> 01:02:07,330 and even Vietnam and elsewhere, have used 1191 01:02:07,330 --> 01:02:10,638 contact tracing to some success. 1192 01:02:10,638 --> 01:02:12,430 There's a lot of research to be done to see 1193 01:02:12,430 --> 01:02:15,790 how that's truly playing out. 1194 01:02:15,790 --> 01:02:18,910 Will we shift this debate a little bit? 1195 01:02:18,910 --> 01:02:21,490 Will we all say, look, I'll give up 1196 01:02:21,490 --> 01:02:23,350 a little bit of my civil liberties, 1197 01:02:23,350 --> 01:02:27,640 a little bit of my privacy, for the good of the community 1198 01:02:27,640 --> 01:02:29,390 and the good of health care and so forth. 1199 01:02:29,390 --> 01:02:32,770 So I think this is going to play out for quite some time, Dani. 1200 01:02:32,770 --> 01:02:36,660 And I also think technology can provide solutions, 1201 01:02:36,660 --> 01:02:38,950 and some of it can be around blockchain technology, 1202 01:02:38,950 --> 01:02:43,300 where Luke and we were talking earlier about encryption 1203 01:02:43,300 --> 01:02:46,900 you can have data on an immutable record, really, 1204 01:02:46,900 --> 01:02:49,510 a tamper resistant record on the blockchain technology that 1205 01:02:49,510 --> 01:02:51,520 is fully public. 1206 01:02:51,520 --> 01:02:55,000 But you can also use various encryption methods 1207 01:02:55,000 --> 01:02:59,500 called zero knowledge proofs and others to sort of lock it 1208 01:02:59,500 --> 01:03:01,560 down and make it harder to see. 1209 01:03:01,560 --> 01:03:03,220 And there's great work on this being 1210 01:03:03,220 --> 01:03:07,510 done at MIT by faculty members like Sandy Pentland 1211 01:03:07,510 --> 01:03:10,630 and Silvio Micali and the like. 1212 01:03:10,630 --> 01:03:14,690 So I think technology can help this debate as well. 1213 01:03:14,690 --> 01:03:17,560 It might be that we share things in certain circumstances 1214 01:03:17,560 --> 01:03:22,572 and we sort of protected in other circumstances. 1215 01:03:22,572 --> 01:03:24,280 So I'm sorry you're going to end up being 1216 01:03:24,280 --> 01:03:26,470 a little nervous for a while. 1217 01:03:26,470 --> 01:03:28,310 I think technology can help us. 1218 01:03:28,310 --> 01:03:31,390 But I do think we're going to be sharing a lot more data. 1219 01:03:34,870 --> 01:03:37,570 We talked about blockchain technology. 1220 01:03:37,570 --> 01:03:40,990 The big question, to me, is adoption 1221 01:03:40,990 --> 01:03:44,050 rests on addressing what's its viability? 1222 01:03:44,050 --> 01:03:49,220 What's its comparative viability and value proposition? 1223 01:03:49,220 --> 01:03:52,120 Do we really need a private currency, 1224 01:03:52,120 --> 01:03:56,060 or is the fiat currency working just fine. 1225 01:03:56,060 --> 01:03:58,570 There's been private currencies for centuries, 1226 01:03:58,570 --> 01:04:01,090 and bitcoin has shown that it can 1227 01:04:01,090 --> 01:04:07,540 live for now about a dozen years as a speculative digital store 1228 01:04:07,540 --> 01:04:09,170 of value. 1229 01:04:09,170 --> 01:04:14,690 But can other cryptocurrencies take hold in small ecosystems, 1230 01:04:14,690 --> 01:04:19,040 whether it's on gaming sites, in an industry that's 1231 01:04:19,040 --> 01:04:22,550 nearly $175 billion, $200 billion industry 1232 01:04:22,550 --> 01:04:24,740 around the globe, can it take hold 1233 01:04:24,740 --> 01:04:27,530 in online gambling in a legal way, 1234 01:04:27,530 --> 01:04:31,640 not in some of the sketchier, fraud-oriented ways. 1235 01:04:31,640 --> 01:04:34,340 Can private currencies in gambling 1236 01:04:34,340 --> 01:04:39,860 be like casino chips, but again, in a, hopefully, legal way. 1237 01:04:39,860 --> 01:04:42,530 Could it have some broader applications? 1238 01:04:42,530 --> 01:04:47,030 And blockchain technology has a shared database structure, 1239 01:04:47,030 --> 01:04:48,170 a shared ledger. 1240 01:04:48,170 --> 01:04:50,870 Can it promote standardization? 1241 01:04:50,870 --> 01:04:53,300 Can it promote some co-opetition where 1242 01:04:53,300 --> 01:04:56,690 some infrastructure is shared, and then companies 1243 01:04:56,690 --> 01:05:00,260 compete on top of it, like in trade finance and elsewhere. 1244 01:05:00,260 --> 01:05:01,730 I think school's still out. 1245 01:05:01,730 --> 01:05:05,735 We don't know yet, beyond sort of this thing 1246 01:05:05,735 --> 01:05:07,110 that we've already seen that it's 1247 01:05:07,110 --> 01:05:09,090 been a heck of a catalyst for change. 1248 01:05:09,090 --> 01:05:12,870 It is pushing up against big finance and central banks 1249 01:05:12,870 --> 01:05:15,030 to think about payment systems, I 1250 01:05:15,030 --> 01:05:21,690 think, in a more inclusive, more 24 hour a day, seven day a week 1251 01:05:21,690 --> 01:05:22,620 way. 1252 01:05:22,620 --> 01:05:24,540 I think it's making payment system 1253 01:05:24,540 --> 01:05:30,240 providers, central banks, and big tech think about new ways 1254 01:05:30,240 --> 01:05:32,680 to do cross-border remittances. 1255 01:05:32,680 --> 01:05:34,950 And I think Facebook has both sort 1256 01:05:34,950 --> 01:05:40,270 of put some nervousness in central bankers, 1257 01:05:40,270 --> 01:05:45,670 but hopefully also promote them to do their payment solutions 1258 01:05:45,670 --> 01:05:47,510 in a better way for the public. 1259 01:05:47,510 --> 01:05:50,050 So it's been a catalyst for change. 1260 01:05:50,050 --> 01:05:53,670 But I think school's still out about how much mainstream 1261 01:05:53,670 --> 01:05:55,658 adoption we'll see. 1262 01:05:55,658 --> 01:05:57,700 And the payment system, we're going to quickly do 1263 01:05:57,700 --> 01:05:59,050 some of the sectors. 1264 01:05:59,050 --> 01:06:01,768 These were some of the pain points we talked about. 1265 01:06:01,768 --> 01:06:03,310 Who were the players that we've seen? 1266 01:06:03,310 --> 01:06:06,220 Well, big financer players in every one of these sectors. 1267 01:06:06,220 --> 01:06:12,200 I won't repeat this slide, but never count big finance out. 1268 01:06:12,200 --> 01:06:15,500 Most of these companies are spending multiple billions 1269 01:06:15,500 --> 01:06:18,440 of dollars of a year on their technology budgets. 1270 01:06:18,440 --> 01:06:20,390 Most of these companies that you see 1271 01:06:20,390 --> 01:06:23,030 on the screen, the Bank of Americs, 1272 01:06:23,030 --> 01:06:30,140 is the Cap Ones, the Vanguards, have not hundreds, but maybe 1273 01:06:30,140 --> 01:06:33,890 thousands of people in their data analytics machine learning 1274 01:06:33,890 --> 01:06:34,820 side. 1275 01:06:34,820 --> 01:06:38,420 And nearly all of them are using robo advisors or chat bots 1276 01:06:38,420 --> 01:06:39,740 by now. 1277 01:06:39,740 --> 01:06:44,900 So big finance is incorporating as much of this technology in, 1278 01:06:44,900 --> 01:06:47,030 but they have legacy business models. 1279 01:06:47,030 --> 01:06:50,090 They have legacy tech stacks. 1280 01:06:50,090 --> 01:06:53,010 They have legacy approaches. 1281 01:06:53,010 --> 01:06:58,290 And so that provides opportunities for big tech 1282 01:06:58,290 --> 01:06:59,390 and startups. 1283 01:06:59,390 --> 01:07:01,400 So in the payment space, we talked about it. 1284 01:07:01,400 --> 01:07:08,450 Ally Pay and M-Pesa in Kenya and Africa, really got in there. 1285 01:07:08,450 --> 01:07:11,420 And then Amazon and Google and so forth. 1286 01:07:11,420 --> 01:07:15,330 And in South Korea, Kakao Pay have really rolled in. 1287 01:07:15,330 --> 01:07:18,290 And in certain countries, like in Africa, 1288 01:07:18,290 --> 01:07:24,770 in Asia, big tech fundamentally dominates the payment space. 1289 01:07:24,770 --> 01:07:28,010 Startups got in, often, around data aggregation, 1290 01:07:28,010 --> 01:07:32,890 like Plaid and others, and open API, but also got in 1291 01:07:32,890 --> 01:07:36,460 around sectors like Toast in the restaurant business. 1292 01:07:36,460 --> 01:07:40,720 Or some just did the job a little better than big finance 1293 01:07:40,720 --> 01:07:41,890 and had enough time. 1294 01:07:41,890 --> 01:07:45,430 Some got in, also, around cross-border remittances 1295 01:07:45,430 --> 01:07:48,430 like Transferwise and the like. 1296 01:07:48,430 --> 01:07:53,630 countries like Brazil and seen Pax Sikora and PayTM in India. 1297 01:07:53,630 --> 01:07:56,890 So it's not isolated to developed countries. 1298 01:07:56,890 --> 01:07:58,900 It's not isolated to Asia. 1299 01:07:58,900 --> 01:08:02,140 It's around the globe, the payment space 1300 01:08:02,140 --> 01:08:05,560 has been very dynamic area. 1301 01:08:05,560 --> 01:08:07,000 Credit markets. 1302 01:08:07,000 --> 01:08:08,620 Credit markets we've talked about. 1303 01:08:08,620 --> 01:08:12,430 The market design has a lot of features, 1304 01:08:12,430 --> 01:08:15,250 and thus a lot of gaps and a lot of pain points, 1305 01:08:15,250 --> 01:08:18,609 whether it's around data, funding, marketing channels. 1306 01:08:18,609 --> 01:08:20,950 I think a lot of what we've seen in the credit area 1307 01:08:20,950 --> 01:08:25,090 has been about enhanced user experience and user interface, 1308 01:08:25,090 --> 01:08:27,560 but then using alternative data. 1309 01:08:27,560 --> 01:08:31,689 And on the funding side, trying to find an issuer bank partner 1310 01:08:31,689 --> 01:08:33,910 or securitizing. 1311 01:08:33,910 --> 01:08:36,220 By and large, the startups haven't said, 1312 01:08:36,220 --> 01:08:37,380 it's our balance sheet. 1313 01:08:40,020 --> 01:08:43,260 That's sort of like a comparative advantage, for now, 1314 01:08:43,260 --> 01:08:46,290 of the big financial firms. 1315 01:08:46,290 --> 01:08:50,069 And you could be in any part of the value chains, all the way 1316 01:08:50,069 --> 01:08:53,010 from the brokerage in origination all the way down 1317 01:08:53,010 --> 01:08:55,640 to collecting and foreclosing. 1318 01:08:55,640 --> 01:08:59,330 You'd have to pick sort of where you're going to be. 1319 01:08:59,330 --> 01:09:03,100 Competition has been fierce in big tech. 1320 01:09:03,100 --> 01:09:07,210 Not only does Amazon have Amazon Pay, they have Amazon Lending. 1321 01:09:07,210 --> 01:09:09,580 Apple has an Apple credit card now, 1322 01:09:09,580 --> 01:09:13,450 partnering up with Goldman Sachs and Mastercard. 1323 01:09:13,450 --> 01:09:17,779 Keiko Bank on top of Keiko Pay. 1324 01:09:17,779 --> 01:09:20,300 And often, the theory of the case 1325 01:09:20,300 --> 01:09:22,069 was build a payment infrastructure, 1326 01:09:22,069 --> 01:09:25,729 get in the payment side, and then put credit on top of it. 1327 01:09:25,729 --> 01:09:28,437 But then there's been a bunch of startups that have said, 1328 01:09:28,437 --> 01:09:30,229 we're not going to build the payment space. 1329 01:09:30,229 --> 01:09:33,050 That's already pretty darned competitive. 1330 01:09:33,050 --> 01:09:35,850 We're just going to come into the credit side. 1331 01:09:35,850 --> 01:09:40,670 And I've just sort of thrown up the logos of 20 or 25 credit 1332 01:09:40,670 --> 01:09:44,035 startups that tend to be unicorns. 1333 01:09:44,035 --> 01:09:46,160 I don't know if they're still worth over $1 billion 1334 01:09:46,160 --> 01:09:48,529 in the middle of the corona crisis. 1335 01:09:48,529 --> 01:09:50,960 But there's a lot of different models here. 1336 01:09:50,960 --> 01:09:53,510 Some are just going straight at the banking model 1337 01:09:53,510 --> 01:09:56,690 like Revolut other neo banks. 1338 01:09:56,690 --> 01:10:00,380 Some have come at it from the credit card space. 1339 01:10:00,380 --> 01:10:05,690 Some have done it from low cost credit reports 1340 01:10:05,690 --> 01:10:07,070 like Credit Karma. 1341 01:10:07,070 --> 01:10:09,410 So there's a lot of different angles 1342 01:10:09,410 --> 01:10:12,620 to how to break into the credit space, 1343 01:10:12,620 --> 01:10:18,310 all of it fundamentally going back to some slice of market 1344 01:10:18,310 --> 01:10:21,973 design, that if you're looking at one of these businesses, 1345 01:10:21,973 --> 01:10:23,890 if you're thinking about being employed by one 1346 01:10:23,890 --> 01:10:26,368 of these businesses, think what's their business model? 1347 01:10:26,368 --> 01:10:27,910 Is their business model fundamentally 1348 01:10:27,910 --> 01:10:30,100 about a better user experience? 1349 01:10:30,100 --> 01:10:32,680 Is it about somehow doing alternative data 1350 01:10:32,680 --> 01:10:35,200 in a way that's different or better than others? 1351 01:10:35,200 --> 01:10:37,710 Is it about one slice of the rolls? 1352 01:10:37,710 --> 01:10:40,390 And they're the best, and they're 1353 01:10:40,390 --> 01:10:43,690 trying to be the best at the front end brokerage. 1354 01:10:43,690 --> 01:10:47,380 We've seen in the mortgage space, for 20 years now, 1355 01:10:47,380 --> 01:10:50,200 this take off, but particularly post 1356 01:10:50,200 --> 01:10:53,830 the 2008 crisis, the Quicken Loan and Penny Mac 1357 01:10:53,830 --> 01:10:58,480 and others now dominate the front end of mortgages. 1358 01:10:58,480 --> 01:11:01,330 Now partly they're able to do that because our government 1359 01:11:01,330 --> 01:11:04,690 here in the US is so involved with conforming 1360 01:11:04,690 --> 01:11:07,690 loan market and Fannie Mae and Freddie Mac, 1361 01:11:07,690 --> 01:11:10,210 and what's called the government-sponsored 1362 01:11:10,210 --> 01:11:11,350 enterprises. 1363 01:11:11,350 --> 01:11:15,100 And we have a really large, developed $10 trillion 1364 01:11:15,100 --> 01:11:19,720 market, nearly, on mortgage securitizations. 1365 01:11:19,720 --> 01:11:22,420 So you can be Quicken Loan and be at the front end 1366 01:11:22,420 --> 01:11:27,780 and say, just like I might rent data storage in the cloud, 1367 01:11:27,780 --> 01:11:31,530 I'm kind of, in essence, renting balance sheet 1368 01:11:31,530 --> 01:11:34,040 by securitizing the mortgages. 1369 01:11:34,040 --> 01:11:35,880 A little tougher in some other fields, 1370 01:11:35,880 --> 01:11:39,930 but you've seen a real takeoff in the US of the personal loan 1371 01:11:39,930 --> 01:11:40,975 marketplace. 1372 01:11:40,975 --> 01:11:42,850 I think you can sort of envision that's going 1373 01:11:42,850 --> 01:11:47,550 to keep competing with the credit card space as well. 1374 01:11:47,550 --> 01:11:51,560 We talked about capital markets, and particularly this move 1375 01:11:51,560 --> 01:11:53,390 to zero commissions. 1376 01:11:53,390 --> 01:12:01,240 And late in 2019, we saw sort of the final part of 20-, 1377 01:12:01,240 --> 01:12:05,960 one might even say 50-year history of commissions coming 1378 01:12:05,960 --> 01:12:09,980 down and down, starting with the early 1970s, 1379 01:12:09,980 --> 01:12:13,970 when laws were changed to allow for competition 1380 01:12:13,970 --> 01:12:15,080 and commissions. 1381 01:12:15,080 --> 01:12:17,300 But it really took a startup named 1382 01:12:17,300 --> 01:12:21,200 Robin Hood to get to about 10 million users, 1383 01:12:21,200 --> 01:12:25,130 a free commission app. 1384 01:12:25,130 --> 01:12:28,220 Robin Hood just started a few years ago, 1385 01:12:28,220 --> 01:12:34,910 sort of competing with earlier fintech startups, earlier 1386 01:12:34,910 --> 01:12:40,940 fintech startups like Ameritrade and E-trade and the like. 1387 01:12:40,940 --> 01:12:46,760 And last fall of 2019, zero commission. 1388 01:12:46,760 --> 01:12:49,530 But I like this slide, which is this iceberg. 1389 01:12:49,530 --> 01:12:51,860 There's a lot of other ways to earn revenues, 1390 01:12:51,860 --> 01:12:59,280 revenues around selling the order flow, as Robinhood does, 1391 01:12:59,280 --> 01:13:04,740 or earning fees on managing and custodial side of the balance 1392 01:13:04,740 --> 01:13:07,880 sheets for investors. 1393 01:13:07,880 --> 01:13:10,270 So I think there's a competition going 1394 01:13:10,270 --> 01:13:15,520 on to what might look like zero commissions or zero fees, 1395 01:13:15,520 --> 01:13:19,240 and then there'll be other revenue models below it. 1396 01:13:19,240 --> 01:13:22,000 We also have seen robo advising. 1397 01:13:22,000 --> 01:13:25,300 And Betterment and Wealthfront and others 1398 01:13:25,300 --> 01:13:28,840 have really gotten significantly into robo advising. 1399 01:13:28,840 --> 01:13:32,680 2020, already 50 million, 60 million accounts that 1400 01:13:32,680 --> 01:13:36,220 have robo advising in there. 1401 01:13:36,220 --> 01:13:38,180 And so the capital markets. 1402 01:13:38,180 --> 01:13:40,780 This is the fintech startups. 1403 01:13:40,780 --> 01:13:44,350 Remember, Vanguard and Fidelity are not just 1404 01:13:44,350 --> 01:13:46,210 sleeping, or T. Rowe Price. 1405 01:13:46,210 --> 01:13:49,240 They are moving into robo advising as well. 1406 01:13:49,240 --> 01:13:53,020 And Robin Hood pushing to zero fees also 1407 01:13:53,020 --> 01:14:00,260 moved Ameritrade and Vanguard and Fidelity et al 1408 01:14:00,260 --> 01:14:03,500 into a more competitive space. 1409 01:14:03,500 --> 01:14:06,290 And they're building on top of another technology called 1410 01:14:06,290 --> 01:14:09,170 exchange traded funds, which was an earlier 1411 01:14:09,170 --> 01:14:12,380 era of fintech as well, these low cost asset 1412 01:14:12,380 --> 01:14:14,440 management funds. 1413 01:14:14,440 --> 01:14:17,620 We talked about the asset management side 1414 01:14:17,620 --> 01:14:21,460 itself, Acorn, Betterment and Wealthfront and the like. 1415 01:14:21,460 --> 01:14:23,770 These are just some of the unicorns. 1416 01:14:23,770 --> 01:14:26,930 In the capital market space as well, 1417 01:14:26,930 --> 01:14:29,320 we have a bunch of crypto exchanges. 1418 01:14:29,320 --> 01:14:31,390 But it's not just crypto exchanges. 1419 01:14:31,390 --> 01:14:35,440 A few of these companies are outside of that. 1420 01:14:35,440 --> 01:14:36,480 And then insurance. 1421 01:14:36,480 --> 01:14:38,840 I know it didn't light up the class, 1422 01:14:38,840 --> 01:14:42,110 but I kind of think insurance brings so much of this 1423 01:14:42,110 --> 01:14:43,530 together. 1424 01:14:43,530 --> 01:14:47,000 And I think it's worthwhile to study insurm tech even 1425 01:14:47,000 --> 01:14:50,220 if you're keen interest is over in credit tech or payment 1426 01:14:50,220 --> 01:14:55,700 tech, if I might say, because what's happening in insurtech 1427 01:14:55,700 --> 01:14:57,810 is the opportunities are real. 1428 01:14:57,810 --> 01:15:00,950 The user interfaces, the user experiences 1429 01:15:00,950 --> 01:15:04,620 are a little bit, frankly, more staid and a little bit more 1430 01:15:04,620 --> 01:15:05,460 clunkier. 1431 01:15:05,460 --> 01:15:09,180 They feel from an earlier time when you take out an insurance 1432 01:15:09,180 --> 01:15:11,030 policy. 1433 01:15:11,030 --> 01:15:13,550 The underwriting has a lot of the same issues. 1434 01:15:13,550 --> 01:15:18,440 But also the data, you can pull from a lot more data, 1435 01:15:18,440 --> 01:15:20,450 as we've talked about. 1436 01:15:20,450 --> 01:15:23,750 And the current sector, just like payment sector 1437 01:15:23,750 --> 01:15:26,210 has pain points, the insurance sector 1438 01:15:26,210 --> 01:15:30,000 has a lot of challenges from the fee structures, 1439 01:15:30,000 --> 01:15:32,870 between 8% and 15% of the revenues 1440 01:15:32,870 --> 01:15:35,420 go back to the agents and broker fees, 1441 01:15:35,420 --> 01:15:39,560 to the claims administration, a lot of significant operating 1442 01:15:39,560 --> 01:15:41,610 and expenses. 1443 01:15:41,610 --> 01:15:44,890 And we hear this debate around health care policy. 1444 01:15:44,890 --> 01:15:48,370 We hear it about, just how do we sort of take 1445 01:15:48,370 --> 01:15:51,610 the costs out of the system in insurance, 1446 01:15:51,610 --> 01:15:55,930 so for every dollar that you pay in premium, 1447 01:15:55,930 --> 01:16:00,100 a higher percentage of that dollar goes back in claims. 1448 01:16:00,100 --> 01:16:03,310 Now we'll always have some cost of claims management 1449 01:16:03,310 --> 01:16:05,800 and agents and brokers and the like. 1450 01:16:05,800 --> 01:16:08,530 But how do we sort of compress claims management? 1451 01:16:08,530 --> 01:16:10,750 How do we compress the selling so 1452 01:16:10,750 --> 01:16:13,870 that a higher percentage of premiums 1453 01:16:13,870 --> 01:16:17,820 ultimately goes to claims? 1454 01:16:17,820 --> 01:16:19,660 And so we've seen a lot of competition 1455 01:16:19,660 --> 01:16:23,170 in property and casualty, health, benefit administration. 1456 01:16:23,170 --> 01:16:24,970 More around property and casualty 1457 01:16:24,970 --> 01:16:27,880 because this is around automobile insurance 1458 01:16:27,880 --> 01:16:29,900 and household insurance. 1459 01:16:29,900 --> 01:16:31,870 Most of the insurance startups are 1460 01:16:31,870 --> 01:16:34,910 focusing on the retail sector. 1461 01:16:34,910 --> 01:16:37,580 But that's also true in credit products. 1462 01:16:37,580 --> 01:16:41,720 Think about where most of the payment and credit startups, 1463 01:16:41,720 --> 01:16:45,550 they're focusing on you and me and small businesses. 1464 01:16:45,550 --> 01:16:49,600 They're not trying to take away the payment and credit 1465 01:16:49,600 --> 01:16:53,780 allocation to Ford Motor Company. 1466 01:16:53,780 --> 01:16:57,940 They're trying to help us, help the broad public, 1467 01:16:57,940 --> 01:17:02,890 with a better user interface and a better product offering. 1468 01:17:02,890 --> 01:17:06,850 And in this area, these auto insurers 1469 01:17:06,850 --> 01:17:09,520 like Root Insurance can actually give us 1470 01:17:09,520 --> 01:17:12,220 insurance based upon the telematics 1471 01:17:12,220 --> 01:17:15,470 and how we're driving in that car. 1472 01:17:15,470 --> 01:17:17,740 Now that might square Dani a little bit, 1473 01:17:17,740 --> 01:17:19,700 that they know exactly where you're driving 1474 01:17:19,700 --> 01:17:21,290 and how you're driving and whether you 1475 01:17:21,290 --> 01:17:23,960 put your foot on your brake too often or not 1476 01:17:23,960 --> 01:17:26,360 in their data analytics model. 1477 01:17:26,360 --> 01:17:28,070 I'm not trying to judge how often Dani 1478 01:17:28,070 --> 01:17:29,900 puts your foot on the brake. 1479 01:17:29,900 --> 01:17:33,620 But in their data analytics model, they might think so. 1480 01:17:33,620 --> 01:17:36,860 Or in India, Policy Bazaar that really is kind of 1481 01:17:36,860 --> 01:17:40,700 like the Expedia of insurance in India, where you can 1482 01:17:40,700 --> 01:17:44,755 compare and contrast insurance. 1483 01:17:44,755 --> 01:17:46,130 There's a lot of exciting things. 1484 01:17:46,130 --> 01:17:47,930 Here I am going again, trying to get 1485 01:17:47,930 --> 01:17:49,700 you excited about insurtech. 1486 01:17:49,700 --> 01:17:51,810 But I would study insurtech to say, OK, 1487 01:17:51,810 --> 01:17:53,600 what can I learn from it, to move back 1488 01:17:53,600 --> 01:17:55,100 to the rest of fintech, what I might 1489 01:17:55,100 --> 01:18:01,490 call credit tech or investech or payment tech, if you wish, 1490 01:18:01,490 --> 01:18:05,720 because I think a lot that's going on here 1491 01:18:05,720 --> 01:18:08,630 will influence back. 1492 01:18:08,630 --> 01:18:10,580 Or you might say it's already happened. 1493 01:18:10,580 --> 01:18:12,380 Some of what's happening, insurance tech, 1494 01:18:12,380 --> 01:18:18,080 is a little later, but has already happened elsewhere. 1495 01:18:18,080 --> 01:18:20,570 We closed out this semester because we've lived 1496 01:18:20,570 --> 01:18:22,890 it, the coronavirus crisis. 1497 01:18:22,890 --> 01:18:25,340 And I think the coronavirus crisis, 1498 01:18:25,340 --> 01:18:27,770 beyond all these huge challenges we 1499 01:18:27,770 --> 01:18:30,470 have from a health care and economic point of view, 1500 01:18:30,470 --> 01:18:32,420 just putting it back in the context 1501 01:18:32,420 --> 01:18:34,400 of financial technology. 1502 01:18:34,400 --> 01:18:36,470 I think startups are going to have to focus, 1503 01:18:36,470 --> 01:18:39,200 and are already focusing on what we call runway, 1504 01:18:39,200 --> 01:18:42,150 their burn rates, their cash, their revenue, their adoption 1505 01:18:42,150 --> 01:18:43,400 and so forth. 1506 01:18:43,400 --> 01:18:45,890 Initial public offerings are on hold in 2020, 1507 01:18:45,890 --> 01:18:48,200 probably well into 2021. 1508 01:18:48,200 --> 01:18:50,450 Venture capital investment's already slowed 1509 01:18:50,450 --> 01:18:54,440 and will continue to slow, and valuations, thus, just 1510 01:18:54,440 --> 01:18:59,150 inevitably need to decline in this environment. 1511 01:18:59,150 --> 01:19:03,200 And consolidation is likely to increase. 1512 01:19:03,200 --> 01:19:05,190 There's clearly going to be winners and losers. 1513 01:19:05,190 --> 01:19:07,880 I'm not saying anything terribly important there. 1514 01:19:07,880 --> 01:19:09,110 But the sectors matter. 1515 01:19:09,110 --> 01:19:12,080 Some sectors related to compliance, 1516 01:19:12,080 --> 01:19:13,590 that might be pretty good. 1517 01:19:13,590 --> 01:19:18,200 But if you're a sector related to travel or entertainment 1518 01:19:18,200 --> 01:19:21,260 or restaurants, probably pretty bad. 1519 01:19:21,260 --> 01:19:26,450 So beyond the sectors, it's really back to runway. 1520 01:19:26,450 --> 01:19:28,010 Now there are opportunities serving 1521 01:19:28,010 --> 01:19:31,690 the various fiscal stimulus and loan programs in Europe, 1522 01:19:31,690 --> 01:19:34,520 the US, Asia, elsewhere. 1523 01:19:34,520 --> 01:19:37,550 I think the challenges are going to mount in the next six to 18 1524 01:19:37,550 --> 01:19:39,860 months around delinquencies. 1525 01:19:39,860 --> 01:19:43,610 We haven't seen this hit hard yet in the household sector. 1526 01:19:43,610 --> 01:19:47,030 But it's inevitable, as this crisis moves forward, 1527 01:19:47,030 --> 01:19:49,490 that the household sector is going to have 1528 01:19:49,490 --> 01:19:51,930 to repair their balance sheets. 1529 01:19:51,930 --> 01:19:53,900 And frankly, too, many people will 1530 01:19:53,900 --> 01:19:56,750 have to declare bankruptcy or be in default or delinquency. 1531 01:19:56,750 --> 01:20:01,540 The small business sector that a lot of fintech startups 1532 01:20:01,540 --> 01:20:05,450 supported will have to either repair their balance sheets 1533 01:20:05,450 --> 01:20:10,430 or go into delinquency and default. So a lot of challenges 1534 01:20:10,430 --> 01:20:11,540 around the startup deal. 1535 01:20:11,540 --> 01:20:14,810 But I say this from my time at Goldman Sachs. 1536 01:20:14,810 --> 01:20:20,110 Challenging times also present opportunities for all of us, 1537 01:20:20,110 --> 01:20:23,845 for you as individuals, and for these companies. 1538 01:20:23,845 --> 01:20:25,970 I want to close on something that Ben Franklin said 1539 01:20:25,970 --> 01:20:29,750 long ago about money, and it's about paying it forward. 1540 01:20:29,750 --> 01:20:31,670 "I do not pretend to give such a deed. 1541 01:20:31,670 --> 01:20:33,950 I only lend it. 1542 01:20:33,950 --> 01:20:36,830 When you meet with another honest man in similar distress, 1543 01:20:36,830 --> 01:20:41,450 you must pay me by lending this sum to him or her, 1544 01:20:41,450 --> 01:20:45,470 enjoining her to discharge the debt by a like operation 1545 01:20:45,470 --> 01:20:49,760 when she shall be able and shall meet with another opportunity." 1546 01:20:49,760 --> 01:20:51,530 This is Benjamin Franklin on money. 1547 01:20:51,530 --> 01:20:57,080 I would say it was fintech of the 1770s to 1790s. 1548 01:20:57,080 --> 01:21:00,800 "I hope it may us go through many hands 1549 01:21:00,800 --> 01:21:04,340 before it meets with a nave"-- nave is an old English word 1550 01:21:04,340 --> 01:21:05,570 to say fool-- 1551 01:21:05,570 --> 01:21:08,510 "meet with a nave that will stop its progress. 1552 01:21:08,510 --> 01:21:11,120 This is a trick of mine of doing a deal of good 1553 01:21:11,120 --> 01:21:13,080 with a little money." 1554 01:21:13,080 --> 01:21:17,270 So take this little homage, this little sang of Benjamin 1555 01:21:17,270 --> 01:21:19,250 Franklin, late 18th century. 1556 01:21:19,250 --> 01:21:21,380 I call it fintech. 1557 01:21:21,380 --> 01:21:24,020 And I ask you just to do a little good, 1558 01:21:24,020 --> 01:21:28,010 not just because we're in these coronavirus times. 1559 01:21:28,010 --> 01:21:31,250 But I've so enjoyed being with the 80 or so 1560 01:21:31,250 --> 01:21:33,800 of you these last 12 sessions. 1561 01:21:33,800 --> 01:21:38,350 Do me the favor, six months from now, 12 months from now, 1562 01:21:38,350 --> 01:21:38,930 write me. 1563 01:21:38,930 --> 01:21:40,080 Tell me where you are. 1564 01:21:40,080 --> 01:21:42,170 Tell me you're just safe and well. 1565 01:21:42,170 --> 01:21:45,140 If you're graduating, tell me that you got a job. 1566 01:21:45,140 --> 01:21:48,230 If you didn't get a job, ask career advice. 1567 01:21:48,230 --> 01:21:50,030 If you're working in the fintech space, 1568 01:21:50,030 --> 01:21:52,130 tell me what you're doing in the fintech space. 1569 01:21:52,130 --> 01:21:53,020 I'd love to learn. 1570 01:21:53,020 --> 01:21:56,470 I've learned so much being your faculty member.