1 00:00:00,500 --> 00:00:01,473 [RUSTLING] 2 00:00:01,473 --> 00:00:02,455 [CLICKING] 3 00:00:02,455 --> 00:00:06,383 [SQUEAKING] 4 00:00:11,012 --> 00:00:12,460 GARY GENSLER: Welcome to FinTech-- 5 00:00:12,460 --> 00:00:15,400 Shaping the World of Finance. 6 00:00:15,400 --> 00:00:18,160 We're going to talk about challenger banks today. 7 00:00:18,160 --> 00:00:22,360 And we've chatted in our last session about credit, payment 8 00:00:22,360 --> 00:00:23,470 before that. 9 00:00:23,470 --> 00:00:26,590 But challenger banks is an interesting topic 10 00:00:26,590 --> 00:00:28,790 that I thought we should take a moment 11 00:00:28,790 --> 00:00:31,670 and just talk about challenger banks. 12 00:00:31,670 --> 00:00:37,290 This is this concept built upon building a bank that's 13 00:00:37,290 --> 00:00:44,070 internet-only, building a bank that has no bricks and mortar 14 00:00:44,070 --> 00:00:45,630 office. 15 00:00:45,630 --> 00:00:47,763 Now, it's not actually a new concept. 16 00:00:47,763 --> 00:00:50,430 And in this lecture, we're going to talk a little bit about what 17 00:00:50,430 --> 00:00:54,600 was happening even then in the 1990s and the 2000s 18 00:00:54,600 --> 00:00:58,050 when online banking started. 19 00:00:58,050 --> 00:01:01,260 But this term, challenger bank, and another related term, 20 00:01:01,260 --> 00:01:07,140 neobanks, relates to something a little bit later. 21 00:01:07,140 --> 00:01:14,220 Ally Bank, which used to be the old auto lending 22 00:01:14,220 --> 00:01:16,350 part of General Motors, the largest-- 23 00:01:16,350 --> 00:01:19,350 at one point, at one point the largest auto manufacturer 24 00:01:19,350 --> 00:01:21,110 in the US-- 25 00:01:21,110 --> 00:01:25,400 Ally Bank in 2009 decided it was not just going 26 00:01:25,400 --> 00:01:26,275 to be an auto lender. 27 00:01:26,275 --> 00:01:27,567 They're going to become a bank. 28 00:01:27,567 --> 00:01:29,210 And in 2009, they said they were going 29 00:01:29,210 --> 00:01:31,640 to be an internet-only bank. 30 00:01:31,640 --> 00:01:35,030 Over in Europe, and particularly the United Kingdom, 31 00:01:35,030 --> 00:01:37,890 many people say challenger banks started in Europe, 32 00:01:37,890 --> 00:01:40,070 in the United Kingdom, particularly 33 00:01:40,070 --> 00:01:42,710 when the official sector got involved 34 00:01:42,710 --> 00:01:44,960 and through something called the Payment Systems 35 00:01:44,960 --> 00:01:47,540 Directive and the Payment System Directive 2 36 00:01:47,540 --> 00:01:50,750 and then later in the United Kingdom around open banking-- 37 00:01:50,750 --> 00:01:54,600 we've talked about this, about open APIs-- 38 00:01:54,600 --> 00:01:57,600 that it opened up the system a little bit. 39 00:01:57,600 --> 00:02:04,290 And by 2013 to 2015, you saw a slew of these challenger banks 40 00:02:04,290 --> 00:02:08,610 and neobanks getting into this space, 41 00:02:08,610 --> 00:02:12,720 first particularly in Europe, then a lot in the US, 42 00:02:12,720 --> 00:02:13,920 now around the globe. 43 00:02:13,920 --> 00:02:18,030 You can find challenger banks and neobanks in Brazil, 44 00:02:18,030 --> 00:02:21,690 in India, and Southeast Asia, in China. 45 00:02:21,690 --> 00:02:25,020 Some of them have been started by big tech companies. 46 00:02:25,020 --> 00:02:27,000 And in the last few years, you have 47 00:02:27,000 --> 00:02:31,110 big finance reacting as well, like Goldman Sachs starting 48 00:02:31,110 --> 00:02:33,040 Marcus. 49 00:02:33,040 --> 00:02:36,240 And so now the term gets a little loose. 50 00:02:36,240 --> 00:02:39,280 Is a challenger bank and neobank if Goldman Sachs 51 00:02:39,280 --> 00:02:40,210 is starting Marcus? 52 00:02:40,210 --> 00:02:42,010 That doesn't feel like a challenger bank. 53 00:02:42,010 --> 00:02:47,020 Goldman Sachs is established 140, 150-year-old investment 54 00:02:47,020 --> 00:02:48,290 bank. 55 00:02:48,290 --> 00:02:50,230 So it gets a little blurry. 56 00:02:50,230 --> 00:02:52,720 And I admit that in this space, there's sometimes 57 00:02:52,720 --> 00:02:55,850 terms don't fit well together. 58 00:02:55,850 --> 00:02:57,550 I'm going to upload the slides here 59 00:02:57,550 --> 00:02:58,930 for a second and share slides. 60 00:02:58,930 --> 00:03:02,268 But Romain is there any questions as we're starting? 61 00:03:02,268 --> 00:03:03,310 TA: Nothing so far, Gary. 62 00:03:13,415 --> 00:03:15,578 GARY GENSLER: So we're going to go back to a topic 63 00:03:15,578 --> 00:03:17,120 that we didn't have quite enough time 64 00:03:17,120 --> 00:03:20,990 to finish in class seven, credit scoring and alternative data. 65 00:03:20,990 --> 00:03:23,270 And it's an important topic that underlies 66 00:03:23,270 --> 00:03:25,820 some of what's going on with challenger backs, not 67 00:03:25,820 --> 00:03:27,420 everything. 68 00:03:27,420 --> 00:03:30,290 And then we're going to talk about some traditional banking, 69 00:03:30,290 --> 00:03:33,740 online banking as it sort of began in the late '90S 70 00:03:33,740 --> 00:03:34,760 and in the noughts. 71 00:03:34,760 --> 00:03:39,380 And what is this new wave, neo or challenging banks, 72 00:03:39,380 --> 00:03:44,420 some of how they're funded and their own valuations, and then 73 00:03:44,420 --> 00:03:48,410 the big finance reaction. 74 00:03:48,410 --> 00:03:50,740 So back to data-- 75 00:03:50,740 --> 00:03:52,410 there is a company. 76 00:03:52,410 --> 00:03:54,150 Many of us know it as FICO. 77 00:03:54,150 --> 00:03:59,370 It's actually called the Fair Isaac Company, started 60 78 00:03:59,370 --> 00:04:04,050 some years ago, started by two Stanford graduates, 79 00:04:04,050 --> 00:04:08,460 not MIT, but a very good institution. 80 00:04:08,460 --> 00:04:11,070 And they were more into data analytics. 81 00:04:11,070 --> 00:04:14,430 And remember, credit cards had started in the 1950s. 82 00:04:14,430 --> 00:04:17,550 And here it was, the late '50s, early 60s, 83 00:04:17,550 --> 00:04:24,882 a fintech company of its time trying to do analysis on data. 84 00:04:24,882 --> 00:04:28,370 A gentleman named Fair and a gentleman named Isaac-- 85 00:04:28,370 --> 00:04:29,840 I don't recall their first names, 86 00:04:29,840 --> 00:04:31,460 I think one was Bill Isaac-- 87 00:04:31,460 --> 00:04:32,570 start this company. 88 00:04:32,570 --> 00:04:34,850 It's a data analytics company. 89 00:04:34,850 --> 00:04:39,290 And by the late 1970s, it had taken hold. 90 00:04:39,290 --> 00:04:42,530 By the 1980s, it was the dominant player 91 00:04:42,530 --> 00:04:47,400 for consumer credit ratings. 92 00:04:47,400 --> 00:04:52,120 And their system is now used in 32 countries. 93 00:04:52,120 --> 00:04:56,660 And many other companies are using the FICO score system. 94 00:04:56,660 --> 00:04:58,513 But it's only one system. 95 00:04:58,513 --> 00:05:00,430 And this gives you a little bit of background. 96 00:05:00,430 --> 00:05:04,300 It's about what your amounts that you owe, the payment 97 00:05:04,300 --> 00:05:10,720 history on those credit cards and other bank relations. 98 00:05:10,720 --> 00:05:12,460 Are you taking out new credit? 99 00:05:12,460 --> 00:05:16,000 We could do a whole class just on FICO scores. 100 00:05:16,000 --> 00:05:18,840 They have updated it at least eight times. 101 00:05:18,840 --> 00:05:20,800 And in the summer of 2020, I think 102 00:05:20,800 --> 00:05:22,990 they're rolling out FICO 9.0. 103 00:05:22,990 --> 00:05:24,880 It's possibly FICO 10.0. 104 00:05:24,880 --> 00:05:29,590 So it's not static, but it's built in another era. 105 00:05:29,590 --> 00:05:33,790 It's built in an era about your payment history 106 00:05:33,790 --> 00:05:36,850 around your bank credit. 107 00:05:36,850 --> 00:05:38,470 It doesn't include many of the things 108 00:05:38,470 --> 00:05:41,280 that we would normally think to include. 109 00:05:41,280 --> 00:05:44,050 And so what is it that we're going through now? 110 00:05:44,050 --> 00:05:45,550 We're going through a period of time 111 00:05:45,550 --> 00:05:47,383 where folks are saying, well, wait a minute, 112 00:05:47,383 --> 00:05:50,740 maybe there are other ways, alternative ways 113 00:05:50,740 --> 00:05:54,730 to figure out and assess your credit. 114 00:05:54,730 --> 00:05:56,440 And it's called alternative data. 115 00:05:56,440 --> 00:06:00,130 Alternative Data in a sense is just alternative to whatever 116 00:06:00,130 --> 00:06:02,320 FICO might be using. 117 00:06:02,320 --> 00:06:05,080 This could be your bank, checking, employment, income, 118 00:06:05,080 --> 00:06:07,150 insurance, tenant, utilities. 119 00:06:07,150 --> 00:06:09,610 All of this currently is not in FICO scoring. 120 00:06:09,610 --> 00:06:13,450 Now, in some countries, employment data and income data 121 00:06:13,450 --> 00:06:18,130 is already and has been for decades used for consumer 122 00:06:18,130 --> 00:06:19,870 credit underwriting. 123 00:06:19,870 --> 00:06:22,750 But in the US and up to 30 other countries 124 00:06:22,750 --> 00:06:25,870 using the FICO scores, a lot of this, 125 00:06:25,870 --> 00:06:28,720 even this classic information like did you 126 00:06:28,720 --> 00:06:33,040 pay your rent on time, did you pay your utilities on time, 127 00:06:33,040 --> 00:06:36,160 is not necessarily included in FICO. 128 00:06:36,160 --> 00:06:40,300 It's partly because they didn't have the data in the 1970s 129 00:06:40,300 --> 00:06:43,210 when Fair and Isaac were doing this. 130 00:06:43,210 --> 00:06:45,280 And eight versions later, they still 131 00:06:45,280 --> 00:06:49,340 haven't included a lot of utility and tenancy. 132 00:06:49,340 --> 00:06:53,170 OK, secondly, there's something called cash flow underwriting. 133 00:06:53,170 --> 00:06:56,920 And if we go to China and we go to payment wallets 134 00:06:56,920 --> 00:07:01,470 around the globe, but let's stay with China, Alipay and WeChat 135 00:07:01,470 --> 00:07:05,390 Pay now can really see a lot of your cash flow, 136 00:07:05,390 --> 00:07:08,530 not just your expenditures, but your revenues, if you're 137 00:07:08,530 --> 00:07:15,100 moving your income in on some regular basis into a wallet. 138 00:07:15,100 --> 00:07:18,280 And cash flow underwriting is even more relevant-- again, 139 00:07:18,280 --> 00:07:21,580 let's stay with Alipay for a moment-- 140 00:07:21,580 --> 00:07:25,150 about small and medium-sized enterprises. 141 00:07:25,150 --> 00:07:28,510 So whether it's Alipay in China or Toast, 142 00:07:28,510 --> 00:07:31,390 a payment start up in the restaurant business here 143 00:07:31,390 --> 00:07:35,860 in the US, Toast can start to see all the revenues 144 00:07:35,860 --> 00:07:39,790 and expenditures of a restaurant and sort of make a credit 145 00:07:39,790 --> 00:07:41,940 decision based upon that. 146 00:07:41,940 --> 00:07:44,440 So cash flow underwriting is a very important thing. 147 00:07:44,440 --> 00:07:47,380 You don't necessarily need machine learning 148 00:07:47,380 --> 00:07:51,700 or AI to incorporate this alternative data. 149 00:07:51,700 --> 00:07:56,270 But the alternative data is something that FICO doesn't do. 150 00:07:56,270 --> 00:08:01,090 And classic banking, doing small and medium-sized lending, 151 00:08:01,090 --> 00:08:07,070 doesn't necessarily do good cash flow underwriting. 152 00:08:07,070 --> 00:08:10,235 Romain any questions? 153 00:08:10,235 --> 00:08:11,540 TA: None so far on this topic. 154 00:08:11,540 --> 00:08:13,457 We got one question from Luke that perhaps you 155 00:08:13,457 --> 00:08:16,070 can touch upon later on why online banking started 156 00:08:16,070 --> 00:08:19,440 in Europe versus other regions. 157 00:08:19,440 --> 00:08:20,440 GARY GENSLER: OK, OK. 158 00:08:20,440 --> 00:08:26,070 Let me hold the Luke question when we get into that as well. 159 00:08:26,070 --> 00:08:27,880 Your consumption, your purchase data-- 160 00:08:27,880 --> 00:08:30,250 Visa and Mastercard already are looking at this 161 00:08:30,250 --> 00:08:32,260 to cross-sell to us. 162 00:08:32,260 --> 00:08:35,169 And others are looking at it to cross-sell. 163 00:08:35,169 --> 00:08:39,460 If you're buying sports equipment or hiking equipment, 164 00:08:39,460 --> 00:08:42,100 we, all of a sudden, get advertising for it. 165 00:08:42,100 --> 00:08:45,910 But can alternative data, like what you buy, 166 00:08:45,910 --> 00:08:47,110 relate to your credit? 167 00:08:47,110 --> 00:08:49,000 Well, let's talk about something that's been 168 00:08:49,000 --> 00:08:50,990 known for some time. 169 00:08:50,990 --> 00:08:52,960 I don't know how many of you would 170 00:08:52,960 --> 00:08:57,820 have ever, when you went to replace an automobile tire, 171 00:08:57,820 --> 00:08:59,740 decide to get it retreaded. 172 00:08:59,740 --> 00:09:05,130 Literally, a retread is putting rubber, new rubber 173 00:09:05,130 --> 00:09:07,980 on the old tire. 174 00:09:07,980 --> 00:09:10,830 Now, retreads or something that we 175 00:09:10,830 --> 00:09:13,050 don't usually talk about in a finance course. 176 00:09:13,050 --> 00:09:15,420 But it has been shown that those who 177 00:09:15,420 --> 00:09:20,370 buy retreads are a little bit more susceptible to credit 178 00:09:20,370 --> 00:09:25,320 risk, meaning default, meaning they're a higher risk credit. 179 00:09:25,320 --> 00:09:28,260 What if you could track people's purchases, 180 00:09:28,260 --> 00:09:33,480 whether it's automobile retreads versus new tires-- 181 00:09:33,480 --> 00:09:36,000 you see the difference-- or anything else 182 00:09:36,000 --> 00:09:38,580 that in the purchase history gives you a sense, 183 00:09:38,580 --> 00:09:41,490 is this a higher risk credit or a lower risk credit? 184 00:09:41,490 --> 00:09:44,550 You can take purchase and consumption data 185 00:09:44,550 --> 00:09:48,870 and tie it to are they higher insurance risk? 186 00:09:48,870 --> 00:09:52,046 Are they a higher risk in their credit? 187 00:09:52,046 --> 00:09:53,830 Now, you have to be careful. 188 00:09:53,830 --> 00:09:59,370 You have to be careful to comply with local law and societal 189 00:09:59,370 --> 00:10:04,500 norms about not using alternative data that might be 190 00:10:04,500 --> 00:10:06,360 suggestive of something else. 191 00:10:06,360 --> 00:10:09,320 It might be suggestive of cultural differences, 192 00:10:09,320 --> 00:10:12,630 of race or gender or ethnic background 193 00:10:12,630 --> 00:10:14,590 or sexual orientation. 194 00:10:14,590 --> 00:10:16,740 And if your consumption of purchase data 195 00:10:16,740 --> 00:10:20,520 has to do with that and you end up with something in the US, 196 00:10:20,520 --> 00:10:22,440 you could easily be on the wrong side 197 00:10:22,440 --> 00:10:24,720 of what's called the Equal Credit Opportunity 198 00:10:24,720 --> 00:10:28,950 Act, that you have to basically be fair and unbiased 199 00:10:28,950 --> 00:10:31,720 in your extension of credit. 200 00:10:31,720 --> 00:10:33,980 And this is sort of at the cutting ground 201 00:10:33,980 --> 00:10:37,280 of alternative data, is how does alternative data 202 00:10:37,280 --> 00:10:43,850 get used, but not inadvertently, or worse, purposely, 203 00:10:43,850 --> 00:10:46,200 trip up fairness and bias issues. 204 00:10:46,200 --> 00:10:50,030 And we saw that recently with Apple Credit Card. 205 00:10:50,030 --> 00:10:55,490 Apple Credit Card rolled out in the spring of 2019-- 206 00:10:55,490 --> 00:10:59,030 a partnership between Apple, Goldman Sachs' Marcus 207 00:10:59,030 --> 00:11:02,780 and Mastercard, a new, innovative thing 208 00:11:02,780 --> 00:11:04,880 that big tech was rolling out with big 209 00:11:04,880 --> 00:11:07,160 find in the spring of 2019. 210 00:11:07,160 --> 00:11:10,550 And all of a sudden, a venture capitalist 211 00:11:10,550 --> 00:11:13,490 runs into the Apple Credit Card and finds 212 00:11:13,490 --> 00:11:17,240 that he and his spouse were going to get 213 00:11:17,240 --> 00:11:19,690 10 times different credit. 214 00:11:19,690 --> 00:11:21,550 They file tax returns together. 215 00:11:21,550 --> 00:11:25,700 They were together, apparently worth many millions of dollars, 216 00:11:25,700 --> 00:11:27,370 maybe centimillionaire. 217 00:11:27,370 --> 00:11:31,180 And he starts to advertise on Twitter 218 00:11:31,180 --> 00:11:34,120 and promote that the Apple Credit Card had a problem. 219 00:11:34,120 --> 00:11:36,820 And then Steve Wozniak, the co-founder of Apple, 220 00:11:36,820 --> 00:11:40,120 publicly announces that he ran into the same problem 221 00:11:40,120 --> 00:11:42,070 with his wife and himself. 222 00:11:42,070 --> 00:11:44,110 Again, they filed their taxes together. 223 00:11:44,110 --> 00:11:46,750 They're both extraordinarily wealthy. 224 00:11:46,750 --> 00:11:49,180 Why would one have 10 times or 20 times more 225 00:11:49,180 --> 00:11:50,800 the credit than the other? 226 00:11:50,800 --> 00:11:53,300 There seemed to be, for whatever reason, 227 00:11:53,300 --> 00:11:57,970 some gender bias in the Apple Credit allocation alternative 228 00:11:57,970 --> 00:11:59,650 data system. 229 00:11:59,650 --> 00:12:02,870 Now, we don't know yet to this day what it was. 230 00:12:02,870 --> 00:12:06,130 But what we do know is that Apple Credit Cards stubbed 231 00:12:06,130 --> 00:12:08,710 their toe on their rollout. 232 00:12:08,710 --> 00:12:10,390 Sometimes it's about the law. 233 00:12:10,390 --> 00:12:13,180 Sometimes, though, it's about business. 234 00:12:13,180 --> 00:12:16,270 Sometimes it's about breaking societal norms. 235 00:12:16,270 --> 00:12:19,600 But in that case, Apple had a tough rollout. 236 00:12:19,600 --> 00:12:21,340 They roll out Apple Credit Card. 237 00:12:21,340 --> 00:12:23,230 And yet they have the co-founder, 238 00:12:23,230 --> 00:12:26,380 former founder of Apple saying not so fast, 239 00:12:26,380 --> 00:12:29,240 you've got a problem here. 240 00:12:29,240 --> 00:12:33,070 The other sets of data is our social footprint, 241 00:12:33,070 --> 00:12:35,710 our social and media footprint-- app usage, 242 00:12:35,710 --> 00:12:38,950 browsing history, the like, geolocation, et cetera. 243 00:12:38,950 --> 00:12:42,070 And in this world of coronavirus and the crisis, 244 00:12:42,070 --> 00:12:44,770 we know that geolocation is going to be used 245 00:12:44,770 --> 00:12:48,790 and is already likely being used for a lot of contact 246 00:12:48,790 --> 00:12:51,880 tracing in many countries, certainly 247 00:12:51,880 --> 00:12:54,040 in Korea from the news reports. 248 00:12:54,040 --> 00:12:58,070 And I'm guessing Luke could tell us more about that. 249 00:12:58,070 --> 00:13:02,990 But we know that contact tracing and all 250 00:13:02,990 --> 00:13:07,630 of the attributes of figuring out who we are as an individual 251 00:13:07,630 --> 00:13:09,610 are being traced more and more. 252 00:13:09,610 --> 00:13:13,510 In China, there's a social credit scoring system 253 00:13:13,510 --> 00:13:18,490 that is an official part of the government's 254 00:13:18,490 --> 00:13:20,650 approach to extending credit. 255 00:13:20,650 --> 00:13:23,030 It might be whether you can buy an airline ticket. 256 00:13:23,030 --> 00:13:26,080 It may be whether you can even get on the commuter rail 257 00:13:26,080 --> 00:13:29,320 system, depending upon your app usage. 258 00:13:29,320 --> 00:13:32,920 And that app usage in China even includes, as I 259 00:13:32,920 --> 00:13:35,260 understand it, not just your-- 260 00:13:35,260 --> 00:13:37,180 what you're doing on Alipay or WeChat 261 00:13:37,180 --> 00:13:40,780 Pay, but also what you're doing on dating websites. 262 00:13:40,780 --> 00:13:43,510 Literally, I repeat on dating websites, 263 00:13:43,510 --> 00:13:48,520 and whether and how you're on any website or any app 264 00:13:48,520 --> 00:13:53,420 could be a little bit like the tire retreading, 265 00:13:53,420 --> 00:13:54,510 that you might be-- 266 00:13:54,510 --> 00:13:56,810 you might find, through the alternative data, 267 00:13:56,810 --> 00:13:59,100 that it's a little different. 268 00:13:59,100 --> 00:14:03,300 Here in the US, many fintech companies 269 00:14:03,300 --> 00:14:05,190 are approaching the regulators and saying 270 00:14:05,190 --> 00:14:08,100 we don't want to trip up and be on the wrong side of the Equal 271 00:14:08,100 --> 00:14:09,540 Credit Opportunity Act. 272 00:14:09,540 --> 00:14:11,820 We don't want to be on the wrong side of other laws, 273 00:14:11,820 --> 00:14:14,730 like Fair Credit Reporting, Truth in Lending Act, 274 00:14:14,730 --> 00:14:17,160 and a series of other laws. 275 00:14:17,160 --> 00:14:19,590 Please help us, regulators, interpret 276 00:14:19,590 --> 00:14:23,220 how we can use alternative data, how we can actually 277 00:14:23,220 --> 00:14:28,870 be more inclusive, provide more and better finance 278 00:14:28,870 --> 00:14:32,440 to more people at a lower cost, but not 279 00:14:32,440 --> 00:14:36,250 be on the wrong side of these important societal 280 00:14:36,250 --> 00:14:38,060 and legal norms. 281 00:14:38,060 --> 00:14:40,780 So it's a very interesting place. 282 00:14:40,780 --> 00:14:46,240 I'll use one example from a conversation with Lending Club. 283 00:14:46,240 --> 00:14:50,440 I had the honor to be on a stage-- 284 00:14:50,440 --> 00:14:52,960 before the crisis, one of the last conferences 285 00:14:52,960 --> 00:14:55,000 was an MIT fintech conference. 286 00:14:55,000 --> 00:14:58,510 And I was on the stage with the CEO of Lending Club 287 00:14:58,510 --> 00:15:02,710 and having a good interview, a fireside chat. 288 00:15:02,710 --> 00:15:06,340 And I asked about alternative data and how they use it. 289 00:15:06,340 --> 00:15:07,760 They use it. 290 00:15:07,760 --> 00:15:10,650 But what they find is in using alternative data 291 00:15:10,650 --> 00:15:13,570 in that marketplace lending company, 292 00:15:13,570 --> 00:15:18,100 they then use machine learning on the alternative data. 293 00:15:18,100 --> 00:15:22,030 They find some what they believe to be correlations. 294 00:15:22,030 --> 00:15:26,530 And then they just use straight up regression analysis. 295 00:15:26,530 --> 00:15:30,850 Once they identify an attribute, they 296 00:15:30,850 --> 00:15:32,920 put the machine learning to the side. 297 00:15:32,920 --> 00:15:33,970 They've used it. 298 00:15:33,970 --> 00:15:38,320 But then they sort of then go to the down-scaled old way, 299 00:15:38,320 --> 00:15:39,760 just straight up. 300 00:15:39,760 --> 00:15:42,560 That's an attribute we've identified. 301 00:15:42,560 --> 00:15:44,780 Tire retreads matter. 302 00:15:44,780 --> 00:15:49,060 Let's just use it that way. 303 00:15:49,060 --> 00:15:52,360 So they do it by trying to find some attributes 304 00:15:52,360 --> 00:15:56,420 and then move on, as I understood the conversation. 305 00:15:56,420 --> 00:15:59,830 So there's a tremendous ecosystem in alternative data, 306 00:15:59,830 --> 00:16:00,370 too. 307 00:16:00,370 --> 00:16:02,110 This is just a slide that pulled. 308 00:16:02,110 --> 00:16:04,680 You can readily get these types of slides on the internet. 309 00:16:04,680 --> 00:16:09,440 But this is from CB Insights as to alternative data sources. 310 00:16:09,440 --> 00:16:10,720 This is a bunch of companies-- 311 00:16:10,720 --> 00:16:13,390 I wouldn't necessarily call them fintech startups, 312 00:16:13,390 --> 00:16:15,640 but they're companies trying to service, 313 00:16:15,640 --> 00:16:18,550 here's how we can help you either with machine 314 00:16:18,550 --> 00:16:21,160 learning, AI, or actually sources 315 00:16:21,160 --> 00:16:24,160 of data in this whole space. 316 00:16:24,160 --> 00:16:28,270 So I'm going to move on to the challenger bank discussion, 317 00:16:28,270 --> 00:16:30,512 but pause for a moment to see if there are questions. 318 00:16:30,512 --> 00:16:32,470 I know Luke had a question, but that was really 319 00:16:32,470 --> 00:16:33,620 about challenger banks. 320 00:16:33,620 --> 00:16:35,025 So [Romain? 321 00:16:35,025 --> 00:16:38,350 TA: Hassan. 322 00:16:38,350 --> 00:16:40,480 AUDIENCE: Hi, yeah. 323 00:16:40,480 --> 00:16:42,250 I mean, if you could just elaborate 324 00:16:42,250 --> 00:16:44,825 on cash flow underwriting. 325 00:16:44,825 --> 00:16:47,040 I do not quite get it. 326 00:16:47,040 --> 00:16:49,270 GARY GENSLER: So let's do it in a small business. 327 00:16:49,270 --> 00:16:55,330 And let's use either the example of toast in the US 328 00:16:55,330 --> 00:16:57,670 with restaurants or almost any business 329 00:16:57,670 --> 00:17:00,580 that Alipay has the wallet. 330 00:17:00,580 --> 00:17:04,599 What Alipay or Toast and see on that small business 331 00:17:04,599 --> 00:17:07,420 or restaurant is they can see their revenues 332 00:17:07,420 --> 00:17:10,304 and they can see their expenditures to the extent 333 00:17:10,304 --> 00:17:14,260 that it's kept inside that payment architecture. 334 00:17:14,260 --> 00:17:18,760 So in Alipay, they've incentivized the millions 335 00:17:18,760 --> 00:17:23,290 of merchants using Alipay not only to receive revenues 336 00:17:23,290 --> 00:17:27,280 into their Alipay Wallet, but also to pay their suppliers 337 00:17:27,280 --> 00:17:29,470 with the Alipay Wallet. 338 00:17:29,470 --> 00:17:32,620 So what Alipay can do and does do 339 00:17:32,620 --> 00:17:37,335 is they can start to see the entire picture or 80% or 90% 340 00:17:37,335 --> 00:17:37,960 of the picture. 341 00:17:37,960 --> 00:17:40,960 They might not see the entire picture, 342 00:17:40,960 --> 00:17:47,560 but they can literally build a whole 360 degrees around them. 343 00:17:47,560 --> 00:17:50,500 Or take something like Intuit here in the US, 344 00:17:50,500 --> 00:17:52,390 which is the company that was founded 345 00:17:52,390 --> 00:17:57,070 20 plus years ago that has the most dominant sort of tax 346 00:17:57,070 --> 00:17:59,800 software, TurboTax. 347 00:17:59,800 --> 00:18:02,290 Intuit is also in the fintech space. 348 00:18:02,290 --> 00:18:05,500 They recently announced buying Credit Karma 349 00:18:05,500 --> 00:18:07,330 for billions of dollars. 350 00:18:07,330 --> 00:18:10,900 And they also have other fintech offerings, 351 00:18:10,900 --> 00:18:14,920 where they can maybe build a greater picture. 352 00:18:14,920 --> 00:18:17,050 I call it cash flow underwriting. 353 00:18:17,050 --> 00:18:19,660 It means underwriting, assessing the risk, 354 00:18:19,660 --> 00:18:23,830 because you can see the revenues and expenditures and build, 355 00:18:23,830 --> 00:18:28,750 in essence, an income statement on the merchant. 356 00:18:28,750 --> 00:18:33,670 Amazon can do some of that, not completely as well as Alipay. 357 00:18:33,670 --> 00:18:36,790 But Amazon can do some of that through Amazon Prime 358 00:18:36,790 --> 00:18:39,700 and what they can see on the merchant. 359 00:18:39,700 --> 00:18:41,190 Did that help? 360 00:18:41,190 --> 00:18:44,270 AUDIENCE: Thank you. 361 00:18:44,270 --> 00:18:46,620 GARY GENSLER: And it gives them a competitive advantage. 362 00:18:46,620 --> 00:18:50,220 And it's part of why getting into the payment space 363 00:18:50,220 --> 00:18:52,800 before getting into the credit space 364 00:18:52,800 --> 00:18:56,250 is a business model, particularly in big tech. 365 00:18:56,250 --> 00:18:58,290 Now, I'm not saying that's what was 366 00:18:58,290 --> 00:19:01,770 the initial reason that Alipay got into the payment space. 367 00:19:01,770 --> 00:19:05,100 Alipay was trying to promote their online platform. 368 00:19:05,100 --> 00:19:07,470 Amazon got into the payment space 369 00:19:07,470 --> 00:19:09,580 to promote their online platform. 370 00:19:09,580 --> 00:19:12,720 And even Toast, the restaurant software business, 371 00:19:12,720 --> 00:19:17,760 got into the restaurant business to sell tablets. 372 00:19:17,760 --> 00:19:21,210 They were thinking we'll sell the better hardware 373 00:19:21,210 --> 00:19:25,590 to the restaurateurs, because this tablet was 374 00:19:25,590 --> 00:19:27,870 important for the servers, what we 375 00:19:27,870 --> 00:19:29,520 might call waiters and waitresses, 376 00:19:29,520 --> 00:19:31,530 walking around a restaurant. 377 00:19:31,530 --> 00:19:35,130 But what they quickly found is that the Toast-- 378 00:19:35,130 --> 00:19:36,420 this is the example-- 379 00:19:36,420 --> 00:19:39,180 they pivoted from being a hardware tablet business 380 00:19:39,180 --> 00:19:40,560 to being a payment business. 381 00:19:40,560 --> 00:19:43,800 And then they start to be a credit business as well, 382 00:19:43,800 --> 00:19:47,910 because the data they're receiving in a partial cash 383 00:19:47,910 --> 00:19:51,630 flow underwriting-- they don't have a full 360-- 384 00:19:51,630 --> 00:19:54,450 allows them to start to underwrite in that business. 385 00:19:54,450 --> 00:19:57,570 And frankly, they're likely to be able to do it better 386 00:19:57,570 --> 00:19:59,310 than a traditional bank. 387 00:19:59,310 --> 00:20:01,410 A traditional bank that might get 388 00:20:01,410 --> 00:20:04,770 annual or quarterly financials will not 389 00:20:04,770 --> 00:20:08,880 have quite the up-to-date sensitivity as to how 390 00:20:08,880 --> 00:20:09,880 that restaurant's doing. 391 00:20:13,295 --> 00:20:17,450 TA: And we have a question from Danielle about data aggregators 392 00:20:17,450 --> 00:20:17,992 on the slide. 393 00:20:17,992 --> 00:20:19,200 GARY GENSLER: Please, please. 394 00:20:19,200 --> 00:20:21,080 TA: Are the different data aggregator players 395 00:20:21,080 --> 00:20:23,860 for the purposes of selling to fintech customers 396 00:20:23,860 --> 00:20:26,120 or for selling to companies who want 397 00:20:26,120 --> 00:20:29,250 to use the data for personalized advertising? 398 00:20:29,250 --> 00:20:30,530 And if yes, why? 399 00:20:30,530 --> 00:20:32,983 Do you anticipate any consolidation? 400 00:20:32,983 --> 00:20:34,400 GARY GENSLER: No, I think Danielle 401 00:20:34,400 --> 00:20:35,810 raised a good question. 402 00:20:35,810 --> 00:20:37,220 They're data aggregators. 403 00:20:37,220 --> 00:20:40,970 And the terms, again, are not terribly well-defined. 404 00:20:40,970 --> 00:20:44,450 They're data aggregators that could be there, 405 00:20:44,450 --> 00:20:48,560 as Danielle points out, to cross-market, to sell. 406 00:20:48,560 --> 00:20:52,040 And even-- let's use Credit Karma for a minute. 407 00:20:52,040 --> 00:20:57,020 Credit Karma initially was about providing free access 408 00:20:57,020 --> 00:20:58,970 to your credit report. 409 00:20:58,970 --> 00:21:01,790 That was their initial insight. 410 00:21:01,790 --> 00:21:06,290 What they found over time is they could cross-sell 411 00:21:06,290 --> 00:21:09,660 and become a friendly website for free 412 00:21:09,660 --> 00:21:11,480 to give you access to credit. 413 00:21:11,480 --> 00:21:13,040 They were cross-selling. 414 00:21:13,040 --> 00:21:16,720 But then they were also collecting a fee. 415 00:21:16,720 --> 00:21:18,340 And they were collecting more data. 416 00:21:18,340 --> 00:21:23,140 And inevitably, they had 100 plus million files, 417 00:21:23,140 --> 00:21:26,590 even though they had much fewer customers. 418 00:21:26,590 --> 00:21:30,100 But data aggregation could also come from another space. 419 00:21:30,100 --> 00:21:34,240 Plaid, Galileo, and others, we call them also 420 00:21:34,240 --> 00:21:35,350 data aggregators. 421 00:21:35,350 --> 00:21:38,920 And they started as software providers 422 00:21:38,920 --> 00:21:42,190 to provide an open API, as we discussed. 423 00:21:42,190 --> 00:21:45,430 But then they create a data warehouse. 424 00:21:45,430 --> 00:21:47,080 And they become data aggregators. 425 00:21:47,080 --> 00:21:51,170 So the term data aggregator can be used in many ways. 426 00:21:51,170 --> 00:21:54,490 One small way it can be used is the second way Danielle 427 00:21:54,490 --> 00:21:57,080 mentioned, and it's kind of mentioned on this slide 428 00:21:57,080 --> 00:21:58,480 of alternative data. 429 00:21:58,480 --> 00:22:03,440 There's some companies that just said we will collect data. 430 00:22:03,440 --> 00:22:06,050 FICO score doesn't have utility data. 431 00:22:06,050 --> 00:22:09,530 We'll collect the utility data and rental payment data. 432 00:22:09,530 --> 00:22:14,120 We believe it will become an alternative data aggregator. 433 00:22:14,120 --> 00:22:16,040 So yes, they're are data aggregator. 434 00:22:16,040 --> 00:22:20,000 But they're collecting data with the clear mind 435 00:22:20,000 --> 00:22:23,630 that they're going to enhance credit scoring. 436 00:22:23,630 --> 00:22:25,670 And as we turn now to challenger banks, 437 00:22:25,670 --> 00:22:27,420 challenger banks might say, look, 438 00:22:27,420 --> 00:22:31,010 we don't have the ability in our first year 439 00:22:31,010 --> 00:22:33,530 or even maybe 10 years of business 440 00:22:33,530 --> 00:22:36,680 to be the one collecting all that utility and rent payment 441 00:22:36,680 --> 00:22:38,750 or social media data. 442 00:22:38,750 --> 00:22:40,760 We want to turn to somebody else. 443 00:22:40,760 --> 00:22:42,770 You might create a business simply 444 00:22:42,770 --> 00:22:45,680 to say we are going to be a data aggregator 445 00:22:45,680 --> 00:22:50,120 to enhance the underwriting of these banks. 446 00:22:50,120 --> 00:22:52,190 There's hundreds of challenger banks. 447 00:22:52,190 --> 00:22:55,340 I want to be a service provider to those challenger banks. 448 00:22:55,340 --> 00:22:56,690 So Danielle, good question. 449 00:22:56,690 --> 00:22:58,195 It could be on marketing. 450 00:22:58,195 --> 00:22:59,570 It could be that you've got there 451 00:22:59,570 --> 00:23:03,020 because you were in the business of something else, payments 452 00:23:03,020 --> 00:23:05,780 or credit scoring, like Credit Karma. 453 00:23:05,780 --> 00:23:08,000 Or you could specifically say I just 454 00:23:08,000 --> 00:23:11,630 want to have a business model simply 455 00:23:11,630 --> 00:23:15,690 about an alternative data field that I think 456 00:23:15,690 --> 00:23:17,600 and my machine learning might tell me 457 00:23:17,600 --> 00:23:19,490 that alternative data field is going 458 00:23:19,490 --> 00:23:21,050 to give us better underwriting. 459 00:23:21,050 --> 00:23:23,570 And then I will sell to all the other fintechs. 460 00:23:23,570 --> 00:23:25,760 I will service fintechs by collecting data. 461 00:23:28,750 --> 00:23:31,105 Romain anything else, or-- 462 00:23:31,105 --> 00:23:33,366 TA: We have Nikhil and Brian with their hands up. 463 00:23:33,366 --> 00:23:34,116 Go ahead, you two. 464 00:23:34,116 --> 00:23:36,491 GARY GENSLER: OK, Nikhil or Brian and then we'll move on. 465 00:23:36,491 --> 00:23:38,740 AUDIENCE: I had a question around underwriting 466 00:23:38,740 --> 00:23:40,220 based on cash flows. 467 00:23:40,220 --> 00:23:42,490 So I wrote my paper on alternative data. 468 00:23:42,490 --> 00:23:44,470 And something that came out was most 469 00:23:44,470 --> 00:23:47,230 of the alternative data lending firms are 470 00:23:47,230 --> 00:23:48,620 focused on small businesses. 471 00:23:48,620 --> 00:23:53,710 So the Amazon example that you gave is for their suppliers. 472 00:23:53,710 --> 00:23:55,510 Is there regulation in the US that's 473 00:23:55,510 --> 00:24:00,160 preventing a cash-flow-based underwriting for customers? 474 00:24:00,160 --> 00:24:04,470 Because countries like Kenya have it with M-Shwari. 475 00:24:04,470 --> 00:24:07,480 Baidu piloted something with Zest AI. 476 00:24:07,480 --> 00:24:10,690 So just curious, what's limiting cash-flow-based underwriting 477 00:24:10,690 --> 00:24:12,700 in the US specifically? 478 00:24:12,700 --> 00:24:14,750 GARY GENSLER: I think it's a great question. 479 00:24:14,750 --> 00:24:18,220 I think it's not just regulatory. 480 00:24:18,220 --> 00:24:25,600 It's also that as of now, as of now, 481 00:24:25,600 --> 00:24:29,020 the folks they could probably do the best cash flow 482 00:24:29,020 --> 00:24:34,060 underwriting on us are the banks themselves, 483 00:24:34,060 --> 00:24:39,790 that Bank of America and Citi sees your rent payment going 484 00:24:39,790 --> 00:24:42,130 out, sees your Visa payments going out, 485 00:24:42,130 --> 00:24:45,070 and sees your wages coming in. 486 00:24:45,070 --> 00:24:49,370 I mean, literally the banks themselves. 487 00:24:49,370 --> 00:24:52,870 And yet, they have not, as I understand it, 488 00:24:52,870 --> 00:24:58,360 yet done a full 360 cash-flow underwriting. 489 00:24:58,360 --> 00:25:01,570 A number of them are enhancing their credit card products 490 00:25:01,570 --> 00:25:04,810 and enhancing those products by taking the FICO scores and then 491 00:25:04,810 --> 00:25:07,390 sort of like FICO plus a little. 492 00:25:07,390 --> 00:25:10,300 But interestingly, the big banks have collected together 493 00:25:10,300 --> 00:25:12,700 and formed something called Vantage, 494 00:25:12,700 --> 00:25:15,620 which is a competitor to FICO. 495 00:25:15,620 --> 00:25:19,000 And it's a sort of consortium amongst some big banks. 496 00:25:19,000 --> 00:25:22,690 But it doesn't fully include the income side. 497 00:25:22,690 --> 00:25:26,410 And I think it does relate to some of the regulatory issues 498 00:25:26,410 --> 00:25:28,120 here in the US. 499 00:25:28,120 --> 00:25:32,080 In the earlier wave, in the earlier wave of data analytics 500 00:25:32,080 --> 00:25:33,580 in the 1960s-- 501 00:25:33,580 --> 00:25:36,910 remember, credit cards came along in the '50s and '60s-- 502 00:25:36,910 --> 00:25:39,610 we passed two really important laws-- 503 00:25:39,610 --> 00:25:43,120 one about bias, called Equal Credit Opportunity Act, 504 00:25:43,120 --> 00:25:47,080 and one about credit reporting agencies. 505 00:25:47,080 --> 00:25:50,260 These companies like TransUnion and Equifax, 506 00:25:50,260 --> 00:25:53,680 they're regulated under the Fair Credit Reporting Act. 507 00:25:53,680 --> 00:25:57,340 And a lot of banks, they're just sort of 508 00:25:57,340 --> 00:25:59,890 trying to work through those issues. 509 00:25:59,890 --> 00:26:02,620 And again, this sets up the challenger bank discussion. 510 00:26:02,620 --> 00:26:06,020 It's part of why challenger banks around the globe-- 511 00:26:06,020 --> 00:26:07,960 they started more in Europe-- 512 00:26:07,960 --> 00:26:11,920 but the challenger banks around the globe had an alternative. 513 00:26:11,920 --> 00:26:13,900 This is one of the reasons I think that they 514 00:26:13,900 --> 00:26:17,240 have a bit of an alternative. 515 00:26:17,240 --> 00:26:18,920 I think this is where we're headed. 516 00:26:18,920 --> 00:26:24,980 We will, as individuals, be more looked at this way. 517 00:26:24,980 --> 00:26:27,320 One little story-- there was one other question, 518 00:26:27,320 --> 00:26:29,165 Romain I'm sorry, who was it? 519 00:26:29,165 --> 00:26:30,205 TA: Brian. 520 00:26:30,205 --> 00:26:32,170 GARY GENSLER: Brian and then one little story 521 00:26:32,170 --> 00:26:34,722 close out alternative data. 522 00:26:34,722 --> 00:26:36,430 AUDIENCE: Could you please talk about how 523 00:26:36,430 --> 00:26:40,450 legislation, such as GDPR and other similar legislation, 524 00:26:40,450 --> 00:26:42,397 has affected this space? 525 00:26:42,397 --> 00:26:43,730 GARY GENSLER: So great question. 526 00:26:43,730 --> 00:26:48,670 There's also privacy concerns about sharing my data. 527 00:26:48,670 --> 00:26:50,050 Now, we all share our data. 528 00:26:50,050 --> 00:26:53,230 That sort of horse is out of that barn. 529 00:26:53,230 --> 00:26:55,660 But then society sort of says wait, 530 00:26:55,660 --> 00:26:57,540 what's your protection here? 531 00:26:57,540 --> 00:27:05,170 You're at the forefront of this, passes the GDPR, the General 532 00:27:05,170 --> 00:27:08,320 Directive, and the P is not privacy. 533 00:27:08,320 --> 00:27:09,940 I'm sorry, I can't remember the-- 534 00:27:09,940 --> 00:27:11,335 AUDIENCE: Protection something. 535 00:27:11,335 --> 00:27:13,840 GARY GENSLER: Protection Regulation, data-- data 536 00:27:13,840 --> 00:27:16,990 protection, that's right, General Data Protection 537 00:27:16,990 --> 00:27:18,280 Regulation. 538 00:27:18,280 --> 00:27:22,730 And they passed GDPR, basically the right to be forgotten, 539 00:27:22,730 --> 00:27:24,820 the rights about your information, the right 540 00:27:24,820 --> 00:27:28,210 to basically review it and so forth. 541 00:27:28,210 --> 00:27:31,750 It has some similarities to those laws that 542 00:27:31,750 --> 00:27:37,000 were way back in the 1970s called Fair Credit Reporting 543 00:27:37,000 --> 00:27:40,090 Act, even, but a lot further. 544 00:27:40,090 --> 00:27:43,420 And what these privacy laws, and more recently in the US, 545 00:27:43,420 --> 00:27:48,010 the California Consumer Protection Act or CCPA-- 546 00:27:48,010 --> 00:27:52,030 we all got notices in 2019 and regarding from Google 547 00:27:52,030 --> 00:27:54,550 and others about the CCPA. 548 00:27:54,550 --> 00:27:58,510 What they've done is they've leaned into an ability for you 549 00:27:58,510 --> 00:28:01,960 to review your data and potentially edit your data 550 00:28:01,960 --> 00:28:04,780 and delete some of what's being tracked. 551 00:28:04,780 --> 00:28:10,330 It does raise some of the R data protection-- 552 00:28:10,330 --> 00:28:14,770 General Data Protection Regulation. 553 00:28:14,770 --> 00:28:20,080 But it doesn't go to the heart of whether using the data 554 00:28:20,080 --> 00:28:23,980 is going to lead to biased outcomes, which 555 00:28:23,980 --> 00:28:27,550 is about fairness and equal credit, 556 00:28:27,550 --> 00:28:31,570 or whether you're going to be regulated like a credit agency. 557 00:28:31,570 --> 00:28:37,670 So I think the GDPR and CCPA better protect the public. 558 00:28:37,670 --> 00:28:41,380 They also raise some of the costs and barriers 559 00:28:41,380 --> 00:28:42,920 to entry in this field. 560 00:28:42,920 --> 00:28:45,370 So there's some economics to it. 561 00:28:45,370 --> 00:28:49,630 I think they might also tend a little bit towards then 562 00:28:49,630 --> 00:28:53,470 homogeneic outcomes, meaning that all the banks will 563 00:28:53,470 --> 00:28:56,380 be doing similar things to protect themselves 564 00:28:56,380 --> 00:28:59,450 in the regulatory environment. 565 00:28:59,450 --> 00:29:01,190 So I think it's a consideration. 566 00:29:01,190 --> 00:29:03,490 It doesn't necessarily slow this down. 567 00:29:03,490 --> 00:29:06,190 It's also an opportunity for a fintech startup. 568 00:29:06,190 --> 00:29:10,780 If you think you can be GDPR-CCPA compliant 569 00:29:10,780 --> 00:29:15,160 and still be a really talented alternative data aggregator, 570 00:29:15,160 --> 00:29:17,290 that's an opportunity for you. 571 00:29:17,290 --> 00:29:20,650 If you're ahead of the regulatory curve and lower 572 00:29:20,650 --> 00:29:24,680 regulatory risk for the other startups, 573 00:29:24,680 --> 00:29:26,840 so that's an opportunity. 574 00:29:26,840 --> 00:29:28,820 One little comment on alternative data 575 00:29:28,820 --> 00:29:30,420 that I like to mention-- 576 00:29:30,420 --> 00:29:33,890 and I don't remember where I read this, so I apologize. 577 00:29:33,890 --> 00:29:37,850 I can't quote this-- the study accurately. 578 00:29:37,850 --> 00:29:42,740 But there has been a study about credit 579 00:29:42,740 --> 00:29:46,190 as it relates to charging your phones. 580 00:29:46,190 --> 00:29:48,460 And so I just pass this along to you, 581 00:29:48,460 --> 00:29:53,510 that if some alternative data provider and alternative data 582 00:29:53,510 --> 00:29:58,310 underwriter wants to assess whether you or I are 583 00:29:58,310 --> 00:30:04,170 good credit, apparently, if one charges your phone every night, 584 00:30:04,170 --> 00:30:08,952 we're more likely to repay our credit card and personal loans. 585 00:30:08,952 --> 00:30:10,910 And if you don't charge your phone every night, 586 00:30:10,910 --> 00:30:13,100 that's fine with me. 587 00:30:13,100 --> 00:30:15,120 That's totally fine. 588 00:30:15,120 --> 00:30:19,990 But apparently, you're a little bit higher risk for credit. 589 00:30:19,990 --> 00:30:23,110 That's what one survey has shown. 590 00:30:23,110 --> 00:30:24,013 And I apologize. 591 00:30:24,013 --> 00:30:25,930 I just don't even remember all the things I've 592 00:30:25,930 --> 00:30:27,790 read, where I've breaded this. 593 00:30:27,790 --> 00:30:34,290 And so what if some challenger bank just tracks 594 00:30:34,290 --> 00:30:36,300 whether you charge your phone? 595 00:30:36,300 --> 00:30:38,550 By the way, that's not hard to do. 596 00:30:38,550 --> 00:30:40,210 It's easy. 597 00:30:40,210 --> 00:30:43,820 These phones are really quite sophisticated-- 598 00:30:43,820 --> 00:30:47,620 geolocation, tracking, contact tracing, everything. 599 00:30:47,620 --> 00:30:50,220 So let's talk about the readings and everything. 600 00:30:50,220 --> 00:30:51,570 I'm not going to dive into it. 601 00:30:51,570 --> 00:30:53,835 But there were three readings about challenger banks, 602 00:30:53,835 --> 00:30:59,130 neobanks, and some of the challenges going forward 603 00:30:59,130 --> 00:30:59,910 in this field. 604 00:31:02,980 --> 00:31:05,860 What is a challenger bank or neobank? 605 00:31:05,860 --> 00:31:06,807 And since we're-- 606 00:31:06,807 --> 00:31:08,140 I don't want to run out of time. 607 00:31:08,140 --> 00:31:12,630 Let me just sort of say challenger and neobank are 608 00:31:12,630 --> 00:31:17,320 often used interchangeably, but they're actually-- 609 00:31:17,320 --> 00:31:21,106 to the more sort of specific, they're a little bit different. 610 00:31:21,106 --> 00:31:24,650 A neobank is something that is acting like a bank 611 00:31:24,650 --> 00:31:28,490 but is not yet registered or licensed 612 00:31:28,490 --> 00:31:30,620 as a bank in its jurisdiction. 613 00:31:30,620 --> 00:31:33,890 And a challenger bank more technically 614 00:31:33,890 --> 00:31:35,990 would be something that has a license. 615 00:31:35,990 --> 00:31:39,860 Now, most people use these terms interchangeably. 616 00:31:39,860 --> 00:31:42,380 But there's slight differences. 617 00:31:42,380 --> 00:31:43,910 Do they have yet registered? 618 00:31:43,910 --> 00:31:45,890 And challenger banks and neobanks 619 00:31:45,890 --> 00:31:48,560 are used to talk about this thing that 620 00:31:48,560 --> 00:31:52,550 happened in these last seven to ten years. 621 00:31:52,550 --> 00:31:55,130 I'm going in a little bit talk about how 622 00:31:55,130 --> 00:32:00,050 this relates to the online banking that came before it. 623 00:32:00,050 --> 00:32:02,130 The pain points in traditional banking-- 624 00:32:02,130 --> 00:32:04,950 we're going to dive into this in a minute in a few slides. 625 00:32:04,950 --> 00:32:06,360 So I'll hold off. 626 00:32:06,360 --> 00:32:10,850 But those pain points about the costs that the big banks have, 627 00:32:10,850 --> 00:32:13,760 the bricks and mortar legacy business that they have, 628 00:32:13,760 --> 00:32:17,030 the legacy technology are real and they've 629 00:32:17,030 --> 00:32:20,630 provided some opportunity to challenger banks. 630 00:32:20,630 --> 00:32:23,930 And the question is how will challenger banks and neobanks 631 00:32:23,930 --> 00:32:25,470 affect the landscape? 632 00:32:25,470 --> 00:32:28,740 So one is what is this phenomena? 633 00:32:28,740 --> 00:32:31,850 Two, why have they been able to tap into this? 634 00:32:31,850 --> 00:32:34,190 What's the traditional bank's problems? 635 00:32:34,190 --> 00:32:36,020 And three, what's it mean for the future? 636 00:32:36,020 --> 00:32:39,750 That's what we're going to try to cover going forward. 637 00:32:39,750 --> 00:32:41,390 So the challenges-- 638 00:32:41,390 --> 00:32:45,080 I mentioned a couple of them, but look, we all know it. 639 00:32:45,080 --> 00:32:49,790 In the US, there's about 78,000 branches still in the US. 640 00:32:49,790 --> 00:32:52,520 That's down from about 82,000 or 83,000. 641 00:32:52,520 --> 00:32:55,520 What will that number be in 10 years? 642 00:32:55,520 --> 00:32:57,620 It's not declining leaps and bounds. 643 00:32:57,620 --> 00:33:00,380 But particularly after the coronavirus crisis, 644 00:33:00,380 --> 00:33:03,120 whenever we get to the other side of this, 645 00:33:03,120 --> 00:33:09,530 whether it optimistically is in months or more, realistically, 646 00:33:09,530 --> 00:33:13,100 in a couple of years and we-- all this social distancing 647 00:33:13,100 --> 00:33:15,860 starts to move around and we start 648 00:33:15,860 --> 00:33:21,440 to get out there, this 78,000 branches, do we need them all? 649 00:33:21,440 --> 00:33:24,380 And yet surveys of the public, still people 650 00:33:24,380 --> 00:33:28,250 like to have a physical branch for that occasional time you 651 00:33:28,250 --> 00:33:29,920 go into a physical branch. 652 00:33:29,920 --> 00:33:32,400 They have legacy technology. 653 00:33:32,400 --> 00:33:35,480 The products themselves are somewhat commoditized. 654 00:33:35,480 --> 00:33:38,750 A mortgages a mortgage, by and large, an auto loan, 655 00:33:38,750 --> 00:33:40,250 and even a small business loan. 656 00:33:40,250 --> 00:33:42,260 There's a certain commoditization 657 00:33:42,260 --> 00:33:45,200 of payments and credit. 658 00:33:45,200 --> 00:33:49,880 And how, when you still have 4,000 to 5,000 banks in the US 659 00:33:49,880 --> 00:33:52,070 and in many countries still hundreds of banks, 660 00:33:52,070 --> 00:33:54,770 how do they literally compete with each other 661 00:33:54,770 --> 00:33:58,970 on a product that is somewhat commoditized? 662 00:33:58,970 --> 00:34:01,220 Now, you might say that's hard for the new competitor. 663 00:34:01,220 --> 00:34:03,137 But the new competitor then comes in and says, 664 00:34:03,137 --> 00:34:06,410 well, maybe I can only provide something to the restaurant 665 00:34:06,410 --> 00:34:09,170 business, or I can provide something that is really 666 00:34:09,170 --> 00:34:11,360 a great user interface. 667 00:34:11,360 --> 00:34:15,290 And the cost and fees are real, too. 668 00:34:15,290 --> 00:34:17,600 The traditional banking, not just 669 00:34:17,600 --> 00:34:20,929 because of their branch network of nearly 80,000 branches 670 00:34:20,929 --> 00:34:24,800 in the US, but also because of their employment, 671 00:34:24,800 --> 00:34:27,080 also because of their fees-- 672 00:34:27,080 --> 00:34:30,120 a lot of the challenger banks broke in by saying, listen, 673 00:34:30,120 --> 00:34:32,780 we're not going to charge you the same overdraft fees 674 00:34:32,780 --> 00:34:34,250 or late fees. 675 00:34:34,250 --> 00:34:38,989 And if you do a deep dive and a deep study on one consumer 676 00:34:38,989 --> 00:34:42,830 banking, a lot of the revenue is on fees, 677 00:34:42,830 --> 00:34:46,770 whether it's overdraft, late fees and the like. 678 00:34:46,770 --> 00:34:50,659 And so challenger banks also said we can go in there 679 00:34:50,659 --> 00:34:54,540 and try to challenge online in a different way and, of course, 680 00:34:54,540 --> 00:34:56,909 provide greater user interface. 681 00:34:56,909 --> 00:34:58,910 I think, to Luke's earlier question, 682 00:34:58,910 --> 00:35:02,540 partly why challenger banks started in Europe 683 00:35:02,540 --> 00:35:05,470 was partly because of the Payment System directive. 684 00:35:05,470 --> 00:35:09,530 There was first PDS, Payment System Directive, 685 00:35:09,530 --> 00:35:12,890 what's now called 1 and PSD 2. 686 00:35:12,890 --> 00:35:14,900 I think it's partly because of the Payment 687 00:35:14,900 --> 00:35:19,370 System directives, but partly the 2008 crisis. 688 00:35:19,370 --> 00:35:23,600 Now, Ally Bank here in the US technically 689 00:35:23,600 --> 00:35:26,030 was the first one in 2009 here. 690 00:35:26,030 --> 00:35:29,300 But a lot of people don't say that was the first one. 691 00:35:29,300 --> 00:35:31,290 But that came after the crisis. 692 00:35:31,290 --> 00:35:33,080 And then you saw this spur of them 693 00:35:33,080 --> 00:35:36,770 in the early teens that came along. 694 00:35:36,770 --> 00:35:39,290 And I think a lot of it had to do with trust. 695 00:35:39,290 --> 00:35:41,480 And the European situation it was going through, 696 00:35:41,480 --> 00:35:46,670 the European debt crisis, all the way through 2012, 2013. 697 00:35:46,670 --> 00:35:49,850 And you saw an enormous number of these starting 698 00:35:49,850 --> 00:35:53,720 between 2011 and 2015. 699 00:35:53,720 --> 00:35:57,730 And so this is a little bit the history of online banking. 700 00:35:57,730 --> 00:36:01,640 And I take this from FT Partners, which 701 00:36:01,640 --> 00:36:03,500 is Financial Technology Partners, which 702 00:36:03,500 --> 00:36:09,140 is a small investment bank started by a former Wall Street 703 00:36:09,140 --> 00:36:11,240 banker, I think a handful from Goldman 704 00:36:11,240 --> 00:36:13,610 and maybe Morgan Stanley and the others. 705 00:36:13,610 --> 00:36:15,920 But we're not going to go through each of these. 706 00:36:15,920 --> 00:36:17,930 But the Bank of Scotland actually 707 00:36:17,930 --> 00:36:21,710 started to do online banking back in 1983. 708 00:36:21,710 --> 00:36:27,350 In essence, anything you think is new has something way back. 709 00:36:27,350 --> 00:36:30,630 Bank of America was already online by 2001. 710 00:36:30,630 --> 00:36:34,010 Yodlee, which is-- we talked about Yodlee when we talked 711 00:36:34,010 --> 00:36:35,750 about APIs-- 712 00:36:35,750 --> 00:36:38,540 had already been in this business. 713 00:36:38,540 --> 00:36:41,570 And ING, which was part of a big insurance company, 714 00:36:41,570 --> 00:36:43,700 started retail banking in the late '90s. 715 00:36:43,700 --> 00:36:45,110 The internet came along. 716 00:36:45,110 --> 00:36:47,450 An earlier wave of fintech developed 717 00:36:47,450 --> 00:36:50,060 and online banking came along. 718 00:36:50,060 --> 00:36:54,620 Most of it, most of it related to traditional banks. 719 00:36:54,620 --> 00:36:59,840 And the distinction that I draw between online banking 720 00:36:59,840 --> 00:37:01,390 and challenger banking-- 721 00:37:01,390 --> 00:37:05,660 it's a light distinction, but an important one-- online banking 722 00:37:05,660 --> 00:37:08,720 generally was related to companies 723 00:37:08,720 --> 00:37:11,570 that had bricks and mortars, traditional business models, 724 00:37:11,570 --> 00:37:15,200 generally-- not always, not always. 725 00:37:15,200 --> 00:37:20,540 And Mint was launched in 2006, later acquired by Intuit. 726 00:37:20,540 --> 00:37:23,510 That tax software company acquired Mint. 727 00:37:23,510 --> 00:37:27,320 ING, an insurance company, has something that Capital One 728 00:37:27,320 --> 00:37:30,320 buys for $9 billion in 2011. 729 00:37:30,320 --> 00:37:36,490 So the earlier wave of non-banking online banking, 730 00:37:36,490 --> 00:37:40,310 non-traditional banking, like ING Direct and Mint, 731 00:37:40,310 --> 00:37:42,290 were acquired. 732 00:37:42,290 --> 00:37:44,570 They were eaten up, taken up. 733 00:37:44,570 --> 00:37:48,170 And Ally-- Ally was an auto lending company 734 00:37:48,170 --> 00:37:50,930 formed 100 years ago by General Motors. 735 00:37:50,930 --> 00:37:55,370 It was the first captive auto lending company. 736 00:37:55,370 --> 00:37:58,430 But this set this stage to what if I just 737 00:37:58,430 --> 00:38:01,640 started something that never had a banking license, 738 00:38:01,640 --> 00:38:03,770 never had a bricks and mortar? 739 00:38:03,770 --> 00:38:05,750 It's just a start up. 740 00:38:05,750 --> 00:38:09,200 I'm just going to go into some of these cracks, 741 00:38:09,200 --> 00:38:11,810 some of these opportunities the traditional banks 742 00:38:11,810 --> 00:38:14,900 are giving me-- costs, fees, trust, user interface-- 743 00:38:14,900 --> 00:38:16,770 and I'm going to start something. 744 00:38:16,770 --> 00:38:18,800 And so what are the reasons? 745 00:38:18,800 --> 00:38:20,870 And from a survey, from a different group, 746 00:38:20,870 --> 00:38:23,670 The Financial Brand, these are just 747 00:38:23,670 --> 00:38:25,740 some of the reasons people are saying. 748 00:38:25,740 --> 00:38:28,080 It's easy to set up an account. 749 00:38:28,080 --> 00:38:30,600 I like the fees and the rates. 750 00:38:30,600 --> 00:38:33,960 Better experience, quality service, in one 751 00:38:33,960 --> 00:38:37,270 survey a couple of months ago. 752 00:38:37,270 --> 00:38:40,000 That's an opportunity as well. 753 00:38:40,000 --> 00:38:41,520 Here's a list. 754 00:38:41,520 --> 00:38:43,270 You can make the list so much longer. 755 00:38:43,270 --> 00:38:45,720 There's hundreds of other companies. 756 00:38:45,720 --> 00:38:47,370 But what I tried to do in this is 757 00:38:47,370 --> 00:38:51,360 sort of stick to around the world different banks. 758 00:38:51,360 --> 00:38:54,540 Ally Bank from 11 years ago, but look 759 00:38:54,540 --> 00:39:00,420 at the spate of startups in 2013 to 2016 or '16. 760 00:39:00,420 --> 00:39:04,990 Now, some of these are new, like BigPay in India. 761 00:39:04,990 --> 00:39:09,300 Some of them are multi-billion valuation companies already, 762 00:39:09,300 --> 00:39:13,440 like Nubank in Brazil, started about seven years ago. 763 00:39:13,440 --> 00:39:17,460 There's a bulk of them from the UK, like OakNorth. 764 00:39:17,460 --> 00:39:21,000 Now, Revolut is in 30 countries now, 765 00:39:21,000 --> 00:39:26,820 one of the earliest and largest out of Europe. 766 00:39:26,820 --> 00:39:29,380 Some are coming from other fintech space. 767 00:39:29,380 --> 00:39:32,800 Like SoFi didn't start as what people would traditionally 768 00:39:32,800 --> 00:39:34,140 call a challenger bank. 769 00:39:34,140 --> 00:39:38,110 SoFi kind of started as an online lending platform, 770 00:39:38,110 --> 00:39:40,120 not quite peer to peer. 771 00:39:40,120 --> 00:39:42,400 But then SoFi is moving over. 772 00:39:42,400 --> 00:39:45,130 You can put on this list Betterment 773 00:39:45,130 --> 00:39:47,350 that we will talk about in the next lecture that 774 00:39:47,350 --> 00:39:49,480 does wealth management. 775 00:39:49,480 --> 00:39:52,870 Betterment is starting to move into this space. 776 00:39:52,870 --> 00:39:57,400 Plaid has started to say they will move into this space. 777 00:39:57,400 --> 00:39:59,440 And then WeBank is an interesting one 778 00:39:59,440 --> 00:40:02,080 that's really formed by a bunch of commercial banks 779 00:40:02,080 --> 00:40:06,580 in China that's saying we're going to just have 780 00:40:06,580 --> 00:40:08,890 an online platform. 781 00:40:08,890 --> 00:40:11,440 Is it really part of those commercial banks or is it 782 00:40:11,440 --> 00:40:14,680 separately a challenger bank. 783 00:40:14,680 --> 00:40:18,740 So I've got a lot going on, and KakaoBank, 784 00:40:18,740 --> 00:40:21,320 which is started by one of the big tech firms. 785 00:40:21,320 --> 00:40:24,080 I chose not to put Ant Financial on here. 786 00:40:24,080 --> 00:40:27,720 But you could arguably say Ant Financial, part of Alipay. 787 00:40:27,720 --> 00:40:30,740 I was trying to stick to things that are really classically 788 00:40:30,740 --> 00:40:32,960 called challenger neobanks. 789 00:40:32,960 --> 00:40:36,280 So the vocabulary is a little loose. 790 00:40:36,280 --> 00:40:38,950 Most people, when they think of challenger neobanks, 791 00:40:38,950 --> 00:40:40,930 they think of Revolut in Europe. 792 00:40:40,930 --> 00:40:43,090 If you want to study a case closely, 793 00:40:43,090 --> 00:40:47,390 look at Revolut, now in 30 countries. 794 00:40:47,390 --> 00:40:51,200 But a lot of other players who try to get in-- 795 00:40:51,200 --> 00:40:53,320 TransferWise from the payment space. 796 00:40:53,320 --> 00:40:56,360 So some from the payment space some, from the big tech, 797 00:40:56,360 --> 00:41:00,290 like KakaoBank, some from commercial banks, 798 00:41:00,290 --> 00:41:03,200 some from investment banks, like Goldman Sachs starts Marcus. 799 00:41:06,380 --> 00:41:08,240 A lot going on. 800 00:41:08,240 --> 00:41:09,620 Nearly every country-- 801 00:41:09,620 --> 00:41:13,490 Australia is represented here, China, Southeast Asia 802 00:41:13,490 --> 00:41:16,160 and the like. 803 00:41:16,160 --> 00:41:18,320 But this sort of gives you a map of it, 804 00:41:18,320 --> 00:41:20,870 back to that FT Partners thing. 805 00:41:20,870 --> 00:41:24,830 This blows it up even more, just with their logos. 806 00:41:24,830 --> 00:41:28,610 But you can see it's really around the globe right now. 807 00:41:28,610 --> 00:41:32,943 Kind of was emanated out of Europe, populated out of the US 808 00:41:32,943 --> 00:41:34,610 then, and then all of a sudden, you just 809 00:41:34,610 --> 00:41:37,370 saw it kind of all around the globe. 810 00:41:37,370 --> 00:41:41,120 Brazil, enormous contributions out of Brazil in the business 811 00:41:41,120 --> 00:41:45,560 models as well and the like. 812 00:41:45,560 --> 00:41:53,440 Paytm out of India as well, in a sense, I should have mentioned. 813 00:41:53,440 --> 00:41:55,240 Quite a sort of interesting-- and this 814 00:41:55,240 --> 00:41:57,130 will be up in Canvas, of course. 815 00:41:57,130 --> 00:41:58,935 So what are their product offerings? 816 00:41:58,935 --> 00:42:00,310 What are their product offerings? 817 00:42:00,310 --> 00:42:03,670 Well, any bank has the same sort of list of product offerings, 818 00:42:03,670 --> 00:42:04,570 of course. 819 00:42:04,570 --> 00:42:08,200 And this is just one example, again, from this FT Partners. 820 00:42:08,200 --> 00:42:10,660 Checking and savings-- to be a challenger bank, 821 00:42:10,660 --> 00:42:13,210 it's usually right there, somewhere in debit 822 00:42:13,210 --> 00:42:16,070 cards, credit cards, checking, savings. 823 00:42:16,070 --> 00:42:19,990 And basically, they would say it was easy to an account. 824 00:42:19,990 --> 00:42:21,970 And we will actually pay you something. 825 00:42:21,970 --> 00:42:25,270 Revolut, interestingly, started in the cross-border payment 826 00:42:25,270 --> 00:42:28,725 area and basically said you could have an account-- 827 00:42:28,725 --> 00:42:30,100 you can be in one country and you 828 00:42:30,100 --> 00:42:33,660 could have an account in multiple currencies. 829 00:42:33,660 --> 00:42:38,250 Traditional banking is a single-currency product, partly 830 00:42:38,250 --> 00:42:41,080 regulation, partly history. 831 00:42:41,080 --> 00:42:42,630 But Revolut says we're online. 832 00:42:42,630 --> 00:42:44,670 You can have multiple currencies. 833 00:42:44,670 --> 00:42:49,270 And we won't charge you all of that cross-border cost. 834 00:42:49,270 --> 00:42:51,250 One might say it started as a payment company. 835 00:42:51,250 --> 00:42:55,790 But it was really an offering you accounts as well. 836 00:42:55,790 --> 00:42:57,580 And this idea of offering an account-- 837 00:42:57,580 --> 00:43:00,160 remember, the term neobank and challenger bank, 838 00:43:00,160 --> 00:43:02,770 often used interchangeably, but neobank is something 839 00:43:02,770 --> 00:43:05,050 that's not yet licensed. 840 00:43:05,050 --> 00:43:08,290 You've seen a spate of companies in the United Kingdom getting 841 00:43:08,290 --> 00:43:10,660 licensed from 2016 to 2019. 842 00:43:10,660 --> 00:43:13,900 Many that were not licensed as traditional banks 843 00:43:13,900 --> 00:43:16,330 then filed for their banking licenses. 844 00:43:16,330 --> 00:43:19,870 And you've seen that happening along the way. 845 00:43:19,870 --> 00:43:23,350 When will SoFi, for instance, get their banking license 846 00:43:23,350 --> 00:43:24,880 in the US, so to speak. 847 00:43:24,880 --> 00:43:29,100 You'll see that sort of going along the way as well. 848 00:43:32,450 --> 00:43:35,230 Romain I'm just going to pause for questions just to see-- 849 00:43:35,230 --> 00:43:37,160 get a little engaged here. 850 00:43:37,160 --> 00:43:40,250 And then I'm going to go to their account base. 851 00:43:40,250 --> 00:43:42,920 And we're going to talk about their account base. 852 00:43:42,920 --> 00:43:46,220 And then we'll turn to funding, valuations, and a little bit 853 00:43:46,220 --> 00:43:48,945 how traditional banks are reacting. 854 00:43:48,945 --> 00:43:49,810 TA: Excellent. 855 00:43:49,810 --> 00:43:51,227 Alessandro, do you want to go? 856 00:43:51,227 --> 00:43:52,310 AUDIENCE: Yeah, thank you. 857 00:43:52,310 --> 00:43:53,360 Hi, Gary. 858 00:43:53,360 --> 00:43:56,000 So my question is many of these companies 859 00:43:56,000 --> 00:44:00,410 that offer credit cards, they often partner up with banks. 860 00:44:00,410 --> 00:44:03,740 Is this because of regulatory issues or a balance 861 00:44:03,740 --> 00:44:05,110 sheet issues? 862 00:44:05,110 --> 00:44:06,610 GARY GENSLER: It's a great question. 863 00:44:06,610 --> 00:44:07,670 It's really both. 864 00:44:07,670 --> 00:44:10,700 And it's not just-- 865 00:44:10,700 --> 00:44:12,580 it's not just in the credit card business. 866 00:44:12,580 --> 00:44:16,340 You found that marketplace lenders also 867 00:44:16,340 --> 00:44:19,040 partnered up with banks. 868 00:44:19,040 --> 00:44:24,650 The idea of entering part of finance-- 869 00:44:24,650 --> 00:44:27,650 you always have a decision in any business of buy 870 00:44:27,650 --> 00:44:31,190 versus build or rent versus bill. 871 00:44:31,190 --> 00:44:32,960 You can do this with your cloud storage. 872 00:44:32,960 --> 00:44:35,990 Do I want to build my data warehouse or rent it? 873 00:44:35,990 --> 00:44:38,210 Well, by and large, if you're a fintech startup, 874 00:44:38,210 --> 00:44:40,460 you're going to rent your cloud storage now, right? 875 00:44:40,460 --> 00:44:42,650 You're not going to build your data warehouse. 876 00:44:42,650 --> 00:44:47,360 You might similarly rent somebody else's balance sheet. 877 00:44:47,360 --> 00:44:51,200 And so part of what you've seen is about balance sheet. 878 00:44:51,200 --> 00:44:55,110 You start a business and you try to find some funding, 879 00:44:55,110 --> 00:44:56,900 but you don't have the funding yourself. 880 00:44:56,900 --> 00:44:58,620 You don't have a balance sheet. 881 00:44:58,620 --> 00:45:01,460 And you can get a warehouse line of credit. 882 00:45:01,460 --> 00:45:04,160 And you can use the asset securitization market 883 00:45:04,160 --> 00:45:08,540 as well to offload those credit card receivables, those auto 884 00:45:08,540 --> 00:45:10,490 receivables, student loan, and, of course, 885 00:45:10,490 --> 00:45:12,380 mortgage receivables. 886 00:45:12,380 --> 00:45:14,522 And-- excuse me-- 887 00:45:14,522 --> 00:45:19,390 [SNEEZES] and so you see [SNEEZES] in some lines 888 00:45:19,390 --> 00:45:21,550 of business, particularly mortgages, 889 00:45:21,550 --> 00:45:24,640 that what one might call alternative finance-- 890 00:45:24,640 --> 00:45:27,250 Quicken Loans, PennyMac, and others 891 00:45:27,250 --> 00:45:29,800 have built very significant-- well 892 00:45:29,800 --> 00:45:34,660 over 50% of mortgages in the US now, 50% to 60%, 893 00:45:34,660 --> 00:45:39,970 originated by the non-banks because they can readily 894 00:45:39,970 --> 00:45:42,490 offload the balance sheet issues. 895 00:45:42,490 --> 00:45:45,760 But to your question, a lot of it is also regulatory. 896 00:45:45,760 --> 00:45:50,080 And in the credit card space and in the peer-to-peer lending 897 00:45:50,080 --> 00:45:53,350 space, without a bank license, it's 898 00:45:53,350 --> 00:45:56,920 hard to do all the pieces you need to do. 899 00:45:56,920 --> 00:46:00,340 And without kind of diving into each of the regulatory reasons 900 00:46:00,340 --> 00:46:01,990 why, you tend to-- 901 00:46:01,990 --> 00:46:05,223 so Amazon partnered with Synchrony, for instance. 902 00:46:05,223 --> 00:46:06,640 And subsequent, it looks like they 903 00:46:06,640 --> 00:46:11,890 may have also partnered with JPMorgan Chase on credit card 904 00:46:11,890 --> 00:46:12,670 offerings. 905 00:46:12,670 --> 00:46:14,830 And there are banks, like Synchrony and others, 906 00:46:14,830 --> 00:46:18,430 they will say we'll basically white label. 907 00:46:18,430 --> 00:46:20,460 Some of them, like Synchrony, that's a co-brand, 908 00:46:20,460 --> 00:46:21,940 on Amazon-Synchrony. 909 00:46:21,940 --> 00:46:24,960 Some will say we'll just be in the background. 910 00:46:24,960 --> 00:46:27,640 And there are actually some licensed banks 911 00:46:27,640 --> 00:46:31,300 that make a business out of servicing fintech companies. 912 00:46:31,300 --> 00:46:35,200 And there's a whole group of licensed banks 913 00:46:35,200 --> 00:46:38,860 that in both Europe and the US feel 914 00:46:38,860 --> 00:46:42,800 that's a line of business in and of itself. 915 00:46:42,800 --> 00:46:43,815 AUDIENCE: Thank you. 916 00:46:43,815 --> 00:46:45,405 TA: Martin. 917 00:46:45,405 --> 00:46:47,580 AUDIENCE: Yes, with so many banks 918 00:46:47,580 --> 00:46:50,600 and with so many similar credit offerings, 919 00:46:50,600 --> 00:46:54,130 how do you see the differentiation will happen? 920 00:46:54,130 --> 00:46:54,680 And there-- 921 00:46:54,680 --> 00:46:55,888 GARY GENSLER: Great question. 922 00:46:55,888 --> 00:46:58,470 Look, this is a very exciting space, 923 00:46:58,470 --> 00:47:00,150 but it's going to shake out. 924 00:47:00,150 --> 00:47:07,860 I mean, it's hard to imagine that they'll all be there. 925 00:47:07,860 --> 00:47:09,470 There is one website-- 926 00:47:09,470 --> 00:47:12,890 I didn't choose to put it up here-- that actually follows 927 00:47:12,890 --> 00:47:17,690 the top 10 challenger banks, by every six months, 928 00:47:17,690 --> 00:47:21,880 they post who is getting the most web traffic? 929 00:47:21,880 --> 00:47:26,320 And part of it-- if you're SoFi, and in one year 930 00:47:26,320 --> 00:47:29,680 you get the most web traffic, but three years later, people 931 00:47:29,680 --> 00:47:32,740 aren't going on your website, that's a problem. 932 00:47:32,740 --> 00:47:34,780 I'm not saying SoFi yet has that problem. 933 00:47:37,330 --> 00:47:40,930 So the way that these companies-- they've each 934 00:47:40,930 --> 00:47:42,230 started a little different. 935 00:47:42,230 --> 00:47:44,380 Some start in a slice of a domain, 936 00:47:44,380 --> 00:47:46,570 like Revolt is the cross-border. 937 00:47:46,570 --> 00:47:50,290 Some come at it from the open API side. 938 00:47:50,290 --> 00:47:52,810 Some come from the low-cost side. 939 00:47:52,810 --> 00:47:57,970 They find one pain point and they try to do that well. 940 00:47:57,970 --> 00:48:01,540 All of them that are successful now pretty much started 941 00:48:01,540 --> 00:48:03,402 between 2013 and 2018. 942 00:48:03,402 --> 00:48:05,110 So it's a little harder to think somebody 943 00:48:05,110 --> 00:48:06,490 is going to get in now. 944 00:48:06,490 --> 00:48:09,100 But it might be a geography. 945 00:48:09,100 --> 00:48:14,120 It might be like BigPay, which I think is in Southeast Asia. 946 00:48:14,120 --> 00:48:17,630 It might be a geography as well. 947 00:48:17,630 --> 00:48:19,270 But I do think there's going to be-- 948 00:48:19,270 --> 00:48:20,710 there's going to be shakeout. 949 00:48:20,710 --> 00:48:24,010 And even amongst the unicorns, those 950 00:48:24,010 --> 00:48:27,610 that are valued over $1 billion, customers matter. 951 00:48:27,610 --> 00:48:29,170 And maybe this sets up. 952 00:48:29,170 --> 00:48:31,750 I'm going to show you two competing 953 00:48:31,750 --> 00:48:34,280 charts on challenger banks. 954 00:48:34,280 --> 00:48:35,320 This is just Europe. 955 00:48:35,320 --> 00:48:36,910 This is just Europe. 956 00:48:36,910 --> 00:48:39,700 But you see the growth in customers-- 957 00:48:39,700 --> 00:48:44,170 Revolt, N26, Monese and so forth, 958 00:48:44,170 --> 00:48:47,410 just this remarkable growth of customers. 959 00:48:47,410 --> 00:48:51,010 That's getting noted by traditional banks. 960 00:48:51,010 --> 00:48:56,230 You get 5, 10 million customer accounts, that's 961 00:48:56,230 --> 00:48:58,640 a remarkable thing to happen. 962 00:48:58,640 --> 00:49:00,760 We'll talk more about this when we do asset 963 00:49:00,760 --> 00:49:02,980 management in the next lecture. 964 00:49:02,980 --> 00:49:08,050 But most of the biggest online brokers, to give you scaling, 965 00:49:08,050 --> 00:49:11,890 were somewhere between 6 and 12 million customer accounts, 966 00:49:11,890 --> 00:49:19,330 E-Trade and the like, TD Ameritrade, so forth. 967 00:49:19,330 --> 00:49:22,830 So to give this on a scale of customer accounts, 968 00:49:22,830 --> 00:49:25,290 these started to really get noticed. 969 00:49:25,290 --> 00:49:27,420 And another customer account chart-- 970 00:49:27,420 --> 00:49:31,200 different service, it's hard to know whether these numbers are 971 00:49:31,200 --> 00:49:34,440 accurate, but these are self-reported by the companies. 972 00:49:34,440 --> 00:49:37,980 This is by year of crop, the 2013 startups-- 973 00:49:37,980 --> 00:49:42,420 Nubank, Aspiration, Tandem, Chime. 974 00:49:42,420 --> 00:49:45,580 Well, Nubank getting to 15 million customer accounts 975 00:49:45,580 --> 00:49:48,960 or KakaoBank in Korea getting to 10 million customers, 976 00:49:48,960 --> 00:49:54,240 Chime, 6 million, 5 million for MoneyLion, Revolut, 6, 977 00:49:54,240 --> 00:49:56,400 and some other figures show Revolut might 978 00:49:56,400 --> 00:49:59,990 be closer to 9 million now-- 979 00:49:59,990 --> 00:50:03,980 these are significant in and of themselves, 980 00:50:03,980 --> 00:50:05,100 that they can get to that. 981 00:50:05,100 --> 00:50:07,930 So part of a business model-- 982 00:50:07,930 --> 00:50:09,980 you can see hundreds of challenger banks. 983 00:50:09,980 --> 00:50:15,720 But many of them still only have 100,000 to 400,000 accounts. 984 00:50:15,720 --> 00:50:17,490 And that's a great start, but you've 985 00:50:17,490 --> 00:50:20,560 got to kind of get further. 986 00:50:20,560 --> 00:50:22,440 And the venture capitalists behind it 987 00:50:22,440 --> 00:50:24,900 have to decide what's their exit strategy? 988 00:50:24,900 --> 00:50:26,820 Can you sell this to somebody else who's 989 00:50:26,820 --> 00:50:29,890 trying to get a foothold in this space or not? 990 00:50:29,890 --> 00:50:32,640 Stash, by the way, that's listed here 991 00:50:32,640 --> 00:50:36,660 is more in the online brokerage business than it 992 00:50:36,660 --> 00:50:38,050 on an online banking. 993 00:50:38,050 --> 00:50:41,170 So I chose not to list it earlier. 994 00:50:41,170 --> 00:50:44,730 Tandem, only half a million customers, 995 00:50:44,730 --> 00:50:47,010 is a really big one in the UK. 996 00:50:47,010 --> 00:50:51,540 But Starling Bank and Revolut and Monzo are bigger. 997 00:50:51,540 --> 00:50:53,940 So how does Tandem break through when 998 00:50:53,940 --> 00:50:58,050 you've got four or five that are bigger, just as an example, 999 00:50:58,050 --> 00:51:00,320 in the UK? 1000 00:51:00,320 --> 00:51:03,300 And so then valuations-- 1001 00:51:03,300 --> 00:51:05,980 and it's really hard to get behind valuations. 1002 00:51:05,980 --> 00:51:10,237 All of this is before the corona crisis. 1003 00:51:10,237 --> 00:51:11,820 Some of these are going to be winners. 1004 00:51:11,820 --> 00:51:16,050 Some of them are going to be losers, this one taken 1005 00:51:16,050 --> 00:51:19,620 from a Forbes article in late 2019 based 1006 00:51:19,620 --> 00:51:24,140 on PitchBook Data from their last funding round. 1007 00:51:24,140 --> 00:51:26,600 Now, Nubank-- look, let's go back. 1008 00:51:26,600 --> 00:51:30,680 Nubank, 15 or so million customers-- 1009 00:51:30,680 --> 00:51:34,010 Nubank's been the big success story here. 1010 00:51:34,010 --> 00:51:37,460 Revolut, if that's a true number from their last funding round, 1011 00:51:37,460 --> 00:51:43,750 that seems a little out of what the customer numbers are. 1012 00:51:43,750 --> 00:51:46,700 It might be an out-of-date figure. 1013 00:51:46,700 --> 00:51:47,930 Valuations aren't clear. 1014 00:51:47,930 --> 00:51:49,860 This is not stock market valuation. 1015 00:51:49,860 --> 00:51:52,760 So I'm going to give you another take on this as well, 1016 00:51:52,760 --> 00:51:54,980 is this is the actual funding-- 1017 00:51:54,980 --> 00:51:57,530 back to the FT Partners survey-- 1018 00:51:57,530 --> 00:52:01,070 this is actual funding that these companies have done 1019 00:52:01,070 --> 00:52:05,200 and then what they call pre-money valuations of Nubank 1020 00:52:05,200 --> 00:52:07,350 and Chime and some of the others. 1021 00:52:07,350 --> 00:52:11,690 So these are not insignificant players in the market. 1022 00:52:11,690 --> 00:52:14,480 If you're thinking about going to work for one of these, 1023 00:52:14,480 --> 00:52:17,090 it would be an exciting career path. 1024 00:52:17,090 --> 00:52:21,200 But even the Nubanks and Chimes and N26s 1025 00:52:21,200 --> 00:52:25,490 have to figure out how they stay viable, how they stay vibrant 1026 00:52:25,490 --> 00:52:32,338 against the traditional players and the big tech companies. 1027 00:52:32,338 --> 00:52:34,130 And if you're all the way at the other end, 1028 00:52:34,130 --> 00:52:35,780 if you're a company like Dave-- 1029 00:52:35,780 --> 00:52:39,140 Dave is thought of as just maybe a unicorn 1030 00:52:39,140 --> 00:52:41,750 and it's only raised 76 million. 1031 00:52:41,750 --> 00:52:44,600 Now, that number may or may not-- 1032 00:52:44,600 --> 00:52:48,920 but if you're Dave and you're there with a couple employees, 1033 00:52:48,920 --> 00:52:51,020 will it be there in three years or not? 1034 00:52:51,020 --> 00:52:52,528 What's their exit strategy? 1035 00:52:52,528 --> 00:52:54,320 So I think you've raised the right question 1036 00:52:54,320 --> 00:52:56,970 about all of these. 1037 00:52:56,970 --> 00:53:02,690 And the history of banking is we don't-- we do have 5,000 6,000 1038 00:53:02,690 --> 00:53:03,680 or so in the US. 1039 00:53:03,680 --> 00:53:05,510 But they're community banks. 1040 00:53:05,510 --> 00:53:09,660 They locally serve and really work hard in their communities. 1041 00:53:09,660 --> 00:53:11,240 They know their business community. 1042 00:53:11,240 --> 00:53:11,900 They know that. 1043 00:53:11,900 --> 00:53:13,820 And they have some bricks and mortars. 1044 00:53:13,820 --> 00:53:18,130 In an online space, we know that network effects matter. 1045 00:53:18,130 --> 00:53:21,700 And with network effects online, how many of these will exist 1046 00:53:21,700 --> 00:53:23,920 is a great question. 1047 00:53:23,920 --> 00:53:27,350 Romain one more slide on funding, 1048 00:53:27,350 --> 00:53:30,980 but questions is I thought this was kind of interesting. 1049 00:53:30,980 --> 00:53:32,760 I don't necessarily subscribe to it. 1050 00:53:32,760 --> 00:53:35,810 But they-- this one site sort of said let's take 1051 00:53:35,810 --> 00:53:38,240 a look at the funding numbers. 1052 00:53:38,240 --> 00:53:43,010 What are their funding numbers and how many customers 1053 00:53:43,010 --> 00:53:44,070 they have? 1054 00:53:44,070 --> 00:53:48,600 So if you take what their last valuation number is divided 1055 00:53:48,600 --> 00:53:51,510 by their customers, and they come up with this concept, 1056 00:53:51,510 --> 00:53:55,510 that customers are worth about $730 apiece, 1057 00:53:55,510 --> 00:53:59,190 if you just average everything, you divide their valuation 1058 00:53:59,190 --> 00:54:02,680 by their customer count. 1059 00:54:02,680 --> 00:54:04,600 It doesn't work perfectly. 1060 00:54:04,600 --> 00:54:05,600 It absolutely doesn't. 1061 00:54:05,600 --> 00:54:10,890 But you can get a sense of this is about an account aggregation 1062 00:54:10,890 --> 00:54:12,060 model. 1063 00:54:12,060 --> 00:54:14,770 Can you build accounts, get to a million? 1064 00:54:14,770 --> 00:54:18,360 Can you get to 5 or 10 million and actually service 1065 00:54:18,360 --> 00:54:18,900 and provide? 1066 00:54:18,900 --> 00:54:21,210 So what's happening is a lot of these companies 1067 00:54:21,210 --> 00:54:23,340 are negative funding. 1068 00:54:23,340 --> 00:54:25,980 By negative funding, what they're really doing 1069 00:54:25,980 --> 00:54:31,220 is they're offering products to the public, 1070 00:54:31,220 --> 00:54:34,100 but they're not covering all of their costs. 1071 00:54:34,100 --> 00:54:38,420 They might be offering interest rates on the deposits. 1072 00:54:38,420 --> 00:54:45,620 They might be having lower costs and fees, lower late fees 1073 00:54:45,620 --> 00:54:47,660 and account maintenance fees. 1074 00:54:47,660 --> 00:54:49,370 They might not have account maintenance. 1075 00:54:49,370 --> 00:54:52,640 A lot of these will allow you to have in essence micro payments 1076 00:54:52,640 --> 00:54:54,440 and micro accounts. 1077 00:54:54,440 --> 00:54:57,140 But how they're funding that is the venture capitalists 1078 00:54:57,140 --> 00:55:00,020 are funding it, basically, because what they're trying 1079 00:55:00,020 --> 00:55:02,630 to do is build valuation. 1080 00:55:02,630 --> 00:55:06,830 Their whole model is about building value and then 1081 00:55:06,830 --> 00:55:12,470 exiting somehow and judging when to do that exit. 1082 00:55:12,470 --> 00:55:16,970 Now, some of them will get to be income positive. 1083 00:55:16,970 --> 00:55:21,560 But those that right now aren't in the corona environment, 1084 00:55:21,560 --> 00:55:25,670 coronavirus crisis we're in, there's a new term 1085 00:55:25,670 --> 00:55:29,540 that you'll hear amongst venture capitalists, is runway. 1086 00:55:29,540 --> 00:55:32,960 How much runway does a company have? 1087 00:55:32,960 --> 00:55:37,070 Did its last funding round, did they raise enough money 1088 00:55:37,070 --> 00:55:42,710 to sustain them for six months more or two years more? 1089 00:55:42,710 --> 00:55:45,650 And a lot relates to one was their last funding round? 1090 00:55:45,650 --> 00:55:48,080 If their last funding round was in the last six months 1091 00:55:48,080 --> 00:55:51,345 and they have enough runway, enough cash 1092 00:55:51,345 --> 00:55:56,210 so that their cash burn, they can last two or three years, 1093 00:55:56,210 --> 00:56:00,412 that's more optimistic than if their runway is six months 1094 00:56:00,412 --> 00:56:02,120 and they haven't done their last funding. 1095 00:56:02,120 --> 00:56:04,560 It's been a year ago. 1096 00:56:04,560 --> 00:56:07,060 They did their A round, but they haven't done their B round. 1097 00:56:07,060 --> 00:56:10,240 And they've done their C round, haven't done their D round. 1098 00:56:10,240 --> 00:56:12,370 And, in essence, they have to-- a lot 1099 00:56:12,370 --> 00:56:14,830 of these companies in the middle of the coronavirus crisis 1100 00:56:14,830 --> 00:56:20,710 have to figure out how to extend their runway, 1101 00:56:20,710 --> 00:56:23,260 maybe do a suboptimal new funding 1102 00:56:23,260 --> 00:56:27,520 round that doesn't boost their valuation, 1103 00:56:27,520 --> 00:56:30,920 merge or consolidate. 1104 00:56:30,920 --> 00:56:34,340 So it's also a challenging time, depending upon what 1105 00:56:34,340 --> 00:56:35,990 they hadn't anticipated-- 1106 00:56:35,990 --> 00:56:37,670 quote, how much runway they have, 1107 00:56:37,670 --> 00:56:40,350 when was their last funding round and the like, 1108 00:56:40,350 --> 00:56:44,340 and have they gone cash-flow positive? 1109 00:56:44,340 --> 00:56:46,220 Most of these have not necessarily 1110 00:56:46,220 --> 00:56:48,635 gone cash-flow positive. 1111 00:56:48,635 --> 00:56:50,510 Romain I'll pause for questions, and then I'm 1112 00:56:50,510 --> 00:56:53,287 going to talk about traditional banks. 1113 00:56:53,287 --> 00:56:54,620 TA: So you almost just answered. 1114 00:56:54,620 --> 00:56:56,450 We had a question from Alida in the chat 1115 00:56:56,450 --> 00:57:00,238 asking whether this model is profitable. 1116 00:57:00,238 --> 00:57:03,850 GARY GENSLER: Alida it is profitable in terms of selling 1117 00:57:03,850 --> 00:57:06,670 equity to venture capitalists. 1118 00:57:06,670 --> 00:57:09,100 It is profitable for some of the banks that 1119 00:57:09,100 --> 00:57:14,030 have been sold even earlier times to traditional banks. 1120 00:57:14,030 --> 00:57:19,800 And for some on bottom line has crossed over and is profitable. 1121 00:57:19,800 --> 00:57:23,490 But we don't have financials for the hundreds of challenger 1122 00:57:23,490 --> 00:57:24,660 and neobanks. 1123 00:57:24,660 --> 00:57:29,790 But by and large, most, before the coronavirus crisis, 1124 00:57:29,790 --> 00:57:33,680 would have been in a negative earnings space. 1125 00:57:33,680 --> 00:57:36,180 And what they were trying to do is basically build 1126 00:57:36,180 --> 00:57:39,870 digital footprint, build customer base 1127 00:57:39,870 --> 00:57:41,610 to get to that first half million, 1128 00:57:41,610 --> 00:57:43,110 to get to that first million, to get 1129 00:57:43,110 --> 00:57:45,410 to the 5 and 10 million round. 1130 00:57:45,410 --> 00:57:49,290 You get 5 or 10 million customers, what you've built 1131 00:57:49,290 --> 00:57:52,050 is you've built a valuable thing that somebody else might 1132 00:57:52,050 --> 00:57:53,700 want to buy. 1133 00:57:53,700 --> 00:57:56,310 And you've tended-- you've tended to then to get 1134 00:57:56,310 --> 00:57:57,840 to cash-flow positive by then. 1135 00:57:57,840 --> 00:57:59,830 But I don't have the financials. 1136 00:57:59,830 --> 00:58:01,770 I wouldn't be able to tell you which 1137 00:58:01,770 --> 00:58:07,140 ones of these small players are yet cash-flow positive. 1138 00:58:07,140 --> 00:58:08,430 I will say this-- 1139 00:58:08,430 --> 00:58:10,830 three to six months from now, more of them 1140 00:58:10,830 --> 00:58:12,480 will consolidate, go out of business, 1141 00:58:12,480 --> 00:58:15,430 or get cash-flow positive. 1142 00:58:15,430 --> 00:58:20,200 They'll have a need to, as I say, extend that runway. 1143 00:58:20,200 --> 00:58:25,190 And then we have Carlos with his anecdote. 1144 00:58:25,190 --> 00:58:26,820 AUDIENCE: Yeah, I have-- 1145 00:58:26,820 --> 00:58:31,230 I have a question as it pertains to these banks in developed 1146 00:58:31,230 --> 00:58:32,470 versus developing markets. 1147 00:58:32,470 --> 00:58:33,990 And I guess it's for two reasons. 1148 00:58:33,990 --> 00:58:37,080 One is that on one end, the extent 1149 00:58:37,080 --> 00:58:40,138 of the unbanked or underbanked population in-- 1150 00:58:40,138 --> 00:58:42,680 and especially because when you showed the valuations, right, 1151 00:58:42,680 --> 00:58:45,720 Nubank is by far an outlier in a country like Brazil. 1152 00:58:45,720 --> 00:58:48,120 So one is that probably a lot of people 1153 00:58:48,120 --> 00:58:49,830 that are clients of Nubank did not 1154 00:58:49,830 --> 00:58:53,486 previously have bank accounts or were significantly underbanked. 1155 00:58:53,486 --> 00:58:55,820 And in a moment like right now with the corona crisis, 1156 00:58:55,820 --> 00:58:58,320 I mean, they're going to need these accounts more than ever, 1157 00:58:58,320 --> 00:59:01,920 whereas people using neobanks in developed markets, 1158 00:59:01,920 --> 00:59:03,480 like the United States, arguably, 1159 00:59:03,480 --> 00:59:04,980 in a lower-interest-rate environment 1160 00:59:04,980 --> 00:59:07,950 might be less prompted or less interested in actually holding 1161 00:59:07,950 --> 00:59:09,810 onto these accounts, versus what they 1162 00:59:09,810 --> 00:59:11,082 can use in traditional banks. 1163 00:59:11,082 --> 00:59:12,540 GARY GENSLER: I think you're right. 1164 00:59:12,540 --> 00:59:17,450 But there's one other aspect that you imply, 1165 00:59:17,450 --> 00:59:19,380 but you didn't explicitly say. 1166 00:59:19,380 --> 00:59:21,780 It's the cost structure and the competitive structure 1167 00:59:21,780 --> 00:59:23,140 in the countries. 1168 00:59:23,140 --> 00:59:24,120 So taking Brazil-- 1169 00:59:24,120 --> 00:59:28,980 Brazil is a very high-cost banking sector. 1170 00:59:28,980 --> 00:59:32,730 It has a lot of historic reasons about the concentration. 1171 00:59:32,730 --> 00:59:35,100 It has to do with the government involvement. 1172 00:59:35,100 --> 00:59:38,760 But of not just Latin America, but around the globe, 1173 00:59:38,760 --> 00:59:42,060 it has, if not the highest, one of the highest 1174 00:59:42,060 --> 00:59:43,440 net-interest margin. 1175 00:59:43,440 --> 00:59:46,320 This is the margin that the traditional banks, 1176 00:59:46,320 --> 00:59:50,550 like Itaú and others, are able to charge between what they 1177 00:59:50,550 --> 00:59:52,590 lend money at and what they borrow money. 1178 00:59:52,590 --> 00:59:54,720 That's called the net interest margin. 1179 00:59:54,720 --> 00:59:58,830 And if in the US and even in Mexico and Canada, 1180 00:59:58,830 --> 01:00:02,040 we're running, give or take, around 300 basis points, 1181 01:00:02,040 --> 01:00:04,590 meaning what you pay a depositor versus what 1182 01:00:04,590 --> 01:00:06,810 you lend on the other side. 1183 01:00:06,810 --> 01:00:08,940 In Brazil, it's multiples of that. 1184 01:00:08,940 --> 01:00:13,160 Even for high-grade corporate borrowers, it's more than that. 1185 01:00:13,160 --> 01:00:16,440 But for small, medium-sized businesses and the public, 1186 01:00:16,440 --> 01:00:20,130 it's close to 10 times that, meaning it's 1187 01:00:20,130 --> 01:00:25,770 20% to 30% net interest margins for a certain sector 1188 01:00:25,770 --> 01:00:26,950 of the market. 1189 01:00:26,950 --> 01:00:30,720 So I think Nubank and the four or five others in Brazil 1190 01:00:30,720 --> 01:00:37,270 had an opportunity because it's a highly inefficient consumer 1191 01:00:37,270 --> 01:00:40,180 banking and small and medium-sized banking market. 1192 01:00:40,180 --> 01:00:43,780 And recall from our last class, I 1193 01:00:43,780 --> 01:00:46,510 said that fintech companies have largely focused 1194 01:00:46,510 --> 01:00:50,420 on the household sector, whether it's mortgages, personal loans, 1195 01:00:50,420 --> 01:00:53,000 credit cards, and the like. 1196 01:00:53,000 --> 01:00:57,170 They've focused on the consumer sector of savings accounts 1197 01:00:57,170 --> 01:01:00,360 and credit card provisions and the like. 1198 01:01:00,360 --> 01:01:02,760 And they focused on small and medium-sized business. 1199 01:01:02,760 --> 01:01:04,020 They're not trying to compete. 1200 01:01:04,020 --> 01:01:07,110 Nubank is not trying to compete with Itaú about the big 1201 01:01:07,110 --> 01:01:08,460 corporate lending. 1202 01:01:08,460 --> 01:01:10,540 And they're not going to compete in Brazil, 1203 01:01:10,540 --> 01:01:13,230 as about half of the-- half of the lending at the big 1204 01:01:13,230 --> 01:01:17,340 corporate level is from basically government-banked-- 1205 01:01:17,340 --> 01:01:20,320 government-backed banks. 1206 01:01:20,320 --> 01:01:23,160 So I think when you look at the developed world-- 1207 01:01:23,160 --> 01:01:27,060 the Europe, the US, and so forth-- 1208 01:01:27,060 --> 01:01:31,710 we have challenges in our banking system, but they're-- 1209 01:01:31,710 --> 01:01:35,190 it's around those pain points I talked 1210 01:01:35,190 --> 01:01:38,490 about earlier about legacy systems and legacy user 1211 01:01:38,490 --> 01:01:42,190 interfaces and sometimes high fees and so forth. 1212 01:01:42,190 --> 01:01:44,250 But in every country, you have to look 1213 01:01:44,250 --> 01:01:48,060 at what's the inefficiency of the current traditional banking 1214 01:01:48,060 --> 01:01:48,960 system? 1215 01:01:48,960 --> 01:01:52,620 And is online banking, is that an opportunity 1216 01:01:52,620 --> 01:02:00,030 to shed a costly branch network, shed costly legacy systems, 1217 01:02:00,030 --> 01:02:04,200 or, frankly, just do it better, cheaper? 1218 01:02:04,200 --> 01:02:06,750 Ultimately, that's what it is about being an entrepreneur. 1219 01:02:06,750 --> 01:02:10,710 Can you do a better, cheaper, faster? 1220 01:02:10,710 --> 01:02:15,430 And so how have the traditional banks reacted? 1221 01:02:15,430 --> 01:02:17,440 They're not staying still. 1222 01:02:17,440 --> 01:02:20,410 All the way back from the online banking movement, 1223 01:02:20,410 --> 01:02:24,280 they were right foursquare in the online banking 1224 01:02:24,280 --> 01:02:27,490 movement of the 1990s to the late noughts. 1225 01:02:27,490 --> 01:02:29,620 What have they done in the last decade? 1226 01:02:29,620 --> 01:02:32,320 They've started to say maybe we have to rebrand and do 1227 01:02:32,320 --> 01:02:33,400 something different. 1228 01:02:33,400 --> 01:02:35,395 Goldman Sachs is not the only one with Marcus. 1229 01:02:35,395 --> 01:02:37,180 Now, Goldman Sachs is an investment bank. 1230 01:02:37,180 --> 01:02:39,850 They were not protecting their brand. 1231 01:02:39,850 --> 01:02:45,070 But, as I said, Cap One in 2011 bought this big ING Direct. 1232 01:02:45,070 --> 01:02:49,600 Cap One said oh, for $9 billion, we can move to this other area. 1233 01:02:49,600 --> 01:02:53,950 But then others are starting to have named branded products. 1234 01:02:53,950 --> 01:02:56,920 JPMorgan and others are saying we maybe 1235 01:02:56,920 --> 01:03:03,100 should do something that looks like an online-- 1236 01:03:03,100 --> 01:03:06,880 we'll even use a different name and call it something else 1237 01:03:06,880 --> 01:03:08,260 and react. 1238 01:03:08,260 --> 01:03:14,440 So I wouldn't count out any of these big traditional banks. 1239 01:03:14,440 --> 01:03:16,300 And where we are in three or five 1240 01:03:16,300 --> 01:03:17,680 years is going to be interesting. 1241 01:03:17,680 --> 01:03:21,930 I also would encourage you to think big tech. 1242 01:03:21,930 --> 01:03:25,060 Apple Credit Card is not quite a challenger bank. 1243 01:03:25,060 --> 01:03:28,450 Amazon Prime is not quite a challenger bank. 1244 01:03:28,450 --> 01:03:33,550 But when do they, like Alibaba, file for a banking license, 1245 01:03:33,550 --> 01:03:39,240 like Alibaba did and got Ant Financial, like in Korea 1246 01:03:39,240 --> 01:03:47,430 Kakao has done and already is just getting huge market share? 1247 01:03:47,430 --> 01:03:54,020 So I don't think that's too much in our distant future. 1248 01:03:54,020 --> 01:03:58,070 I think that's near term, that we'll see that. 1249 01:03:58,070 --> 01:04:00,140 Now, we have different traditions in the US. 1250 01:04:00,140 --> 01:04:02,780 We have a tradition, both in culture 1251 01:04:02,780 --> 01:04:05,330 and embedded in law, about separation 1252 01:04:05,330 --> 01:04:07,400 of banking and commerce. 1253 01:04:07,400 --> 01:04:10,730 And we went through this in the 1980s and 1990s, 1254 01:04:10,730 --> 01:04:13,040 when the department store chains, like Walmart, 1255 01:04:13,040 --> 01:04:14,360 wanted to get into banking. 1256 01:04:14,360 --> 01:04:17,330 And they-- there was all this sort of back and forth about 1257 01:04:17,330 --> 01:04:20,090 whether they can have banking licenses, what type of banking 1258 01:04:20,090 --> 01:04:23,480 license, and industrial loan companies and other things 1259 01:04:23,480 --> 01:04:28,340 really in the intricacy of banking law and licensing. 1260 01:04:28,340 --> 01:04:30,980 We will have some of those debates and challenges 1261 01:04:30,980 --> 01:04:37,280 as well as, I believe, Amazon and Apple and others and Google 1262 01:04:37,280 --> 01:04:40,180 get further into this space.