1 00:00:00,000 --> 00:00:02,485 [SQUEAKING] 2 00:00:02,485 --> 00:00:03,976 [RUSTLING] 3 00:00:03,976 --> 00:00:09,940 [CLICKING] 4 00:00:09,940 --> 00:00:13,750 GARY GENSLER: Welcome to FinTech-- 5 00:00:13,750 --> 00:00:18,650 Shaping the Future of The World of Finance. 6 00:00:18,650 --> 00:00:19,820 And happy spring. 7 00:00:19,820 --> 00:00:20,540 It's May. 8 00:00:20,540 --> 00:00:22,328 I thought what the heck? 9 00:00:22,328 --> 00:00:23,870 You've got to be a little innovative. 10 00:00:23,870 --> 00:00:25,520 Try teaching from outdoor. 11 00:00:25,520 --> 00:00:29,650 So this is not a virtual backdrop. 12 00:00:29,650 --> 00:00:36,290 If the sun is too much or the birds are too much, 13 00:00:36,290 --> 00:00:40,100 that's an additional thing Romain can give me advice on. 14 00:00:40,100 --> 00:00:44,870 Now today we're going to dive into insurance and technology 15 00:00:44,870 --> 00:00:49,700 or what some call this little sector of fintech as insurtech. 16 00:00:49,700 --> 00:00:52,730 But it's not so little, it's not just 17 00:00:52,730 --> 00:00:56,930 simply because the excitement is around payments and challenger 18 00:00:56,930 --> 00:01:02,330 banks and apps like Robinhood and the capital markets 19 00:01:02,330 --> 00:01:05,349 and so forth that we leave insurtech to the end. 20 00:01:05,349 --> 00:01:08,020 I also think insurtech is an interesting field 21 00:01:08,020 --> 00:01:12,370 that it brings all the pieces together in some way. 22 00:01:12,370 --> 00:01:16,240 It brings together a part of finance 23 00:01:16,240 --> 00:01:19,000 that has big legacy incumbents. 24 00:01:19,000 --> 00:01:22,090 In every country we're in, there is 25 00:01:22,090 --> 00:01:25,900 this handful of life-insurance companies and property 26 00:01:25,900 --> 00:01:30,220 and casualty insurance-- that's those that offer lines 27 00:01:30,220 --> 00:01:34,220 of insurance on our homes, our autos, and so forth; 28 00:01:34,220 --> 00:01:37,150 health-care insurers, particularly 29 00:01:37,150 --> 00:01:41,785 in those countries that have, like the US, big, 30 00:01:41,785 --> 00:01:44,650 open health-care systems. 31 00:01:44,650 --> 00:01:47,740 These insurance companies, big, legacy companies, 32 00:01:47,740 --> 00:01:53,200 have tended to be a little behind the curve of technology 33 00:01:53,200 --> 00:01:55,000 compared to the Wall Street firms-- 34 00:01:55,000 --> 00:01:57,760 not a lot behind but a little behind. 35 00:01:57,760 --> 00:02:03,730 And then the insurtech itself, the fintech within insurance, 36 00:02:03,730 --> 00:02:06,460 has done some really interesting things particularly 37 00:02:06,460 --> 00:02:09,280 around alternative data, and we're going to dive into some 38 00:02:09,280 --> 00:02:12,040 of these opportunities. 39 00:02:12,040 --> 00:02:18,670 And also they're a little bit new forms of alternative data. 40 00:02:18,670 --> 00:02:20,680 It's not just about our credit weighting. 41 00:02:20,680 --> 00:02:23,170 It's about our driving. 42 00:02:23,170 --> 00:02:25,030 It's about our homes. 43 00:02:25,030 --> 00:02:29,940 It's about thinking about can we underwrite insurance risk 44 00:02:29,940 --> 00:02:35,100 related to how we live our lives, how we drive our cars, 45 00:02:35,100 --> 00:02:38,790 how small businesses are operated, 46 00:02:38,790 --> 00:02:42,180 and whether there's more information that can be brought 47 00:02:42,180 --> 00:02:46,350 to bear or not just financial inclusion but the pricing 48 00:02:46,350 --> 00:02:49,005 and underwriting of risk? 49 00:02:49,005 --> 00:02:50,880 Of course, we're going to see the same trends 50 00:02:50,880 --> 00:02:52,155 about artificial intelligence. 51 00:02:52,155 --> 00:02:56,070 We're going to see some really important trends around user 52 00:02:56,070 --> 00:02:57,450 interfaces. 53 00:02:57,450 --> 00:03:00,450 And one of the most interesting areas around user interfaces 54 00:03:00,450 --> 00:03:02,550 is what's called claims management, 55 00:03:02,550 --> 00:03:06,300 an area that hopefully you don't have to think about too much. 56 00:03:06,300 --> 00:03:09,330 But amongst this class of 80 or more, 57 00:03:09,330 --> 00:03:14,280 I'm sure that somebody has had a little fender bender, had 58 00:03:14,280 --> 00:03:16,860 some scrape up on the roads. 59 00:03:16,860 --> 00:03:19,980 And now that there are apps that you can just, 60 00:03:19,980 --> 00:03:23,040 on your mobile phone, take some quick photographs 61 00:03:23,040 --> 00:03:25,110 and put it right into the system. 62 00:03:25,110 --> 00:03:30,270 Claims management is changing quite a bit now as well, 63 00:03:30,270 --> 00:03:33,150 and there's startups in this space that basically 64 00:03:33,150 --> 00:03:36,750 say we will get you better claims management faster, 65 00:03:36,750 --> 00:03:40,320 smoother, and a better user experience about claims 66 00:03:40,320 --> 00:03:41,530 management. 67 00:03:41,530 --> 00:03:48,870 So with that, I'm going to try to upload the slides again. 68 00:03:48,870 --> 00:03:50,910 We can join sort of the community 69 00:03:50,910 --> 00:03:54,360 together, sort of videos on. 70 00:03:54,360 --> 00:03:56,640 Please raise your hands actively. 71 00:03:56,640 --> 00:04:00,990 Romain's going to be watching for any blue hands up 72 00:04:00,990 --> 00:04:03,400 also in the chat. 73 00:04:03,400 --> 00:04:06,660 And if the birds get to be too much, 74 00:04:06,660 --> 00:04:10,040 just know that I'll go back inside on Wednesday. 75 00:04:10,040 --> 00:04:11,350 Romain, how is it so far? 76 00:04:11,350 --> 00:04:12,600 TEACHING ASSISTANT: It's fine. 77 00:04:12,600 --> 00:04:13,530 It's very nice. 78 00:04:13,530 --> 00:04:14,700 Don't worry. 79 00:04:14,700 --> 00:04:15,930 GARY GENSLER: All right. 80 00:04:15,930 --> 00:04:18,510 He's assuring me that I can keep having 81 00:04:18,510 --> 00:04:23,550 a little bit of an indulgence here 82 00:04:23,550 --> 00:04:27,980 in this beautiful outdoor setting. 83 00:04:27,980 --> 00:04:31,415 And what I'm going to need to do is go to this Share Screens. 84 00:04:43,408 --> 00:04:45,200 So we're going to talk a little bit-- we're 85 00:04:45,200 --> 00:04:47,790 going to start a little bit about the insurance value 86 00:04:47,790 --> 00:04:51,840 chain and the sector's landscape now. 87 00:04:51,840 --> 00:04:54,600 And traditionally in business schools, 88 00:04:54,600 --> 00:04:58,650 traditionally in worlds of finance and finance majors, 89 00:04:58,650 --> 00:05:04,710 there's a lot of time spent on banking and on capital markets, 90 00:05:04,710 --> 00:05:09,420 and there's some time but less time spent on insurance. 91 00:05:09,420 --> 00:05:12,750 And so I'm going to just lay the groundwork a little bit 92 00:05:12,750 --> 00:05:16,740 about insurance, some of the challenges 93 00:05:16,740 --> 00:05:19,620 in the sector itself, and then get into this field 94 00:05:19,620 --> 00:05:22,410 fintech and insurance or what a lot of people 95 00:05:22,410 --> 00:05:25,290 call insurtech, opportunities, business models, 96 00:05:25,290 --> 00:05:26,730 and the startups. 97 00:05:26,730 --> 00:05:29,160 So that's sort of the goal of the class. 98 00:05:29,160 --> 00:05:34,140 The readings were really about this area of insurtech. 99 00:05:34,140 --> 00:05:38,310 There was a handful of them. 100 00:05:38,310 --> 00:05:40,008 The Bank of International Settlement 101 00:05:40,008 --> 00:05:41,550 reading was really talking about some 102 00:05:41,550 --> 00:05:45,900 of the regulatory challenges and whether insurtech 103 00:05:45,900 --> 00:05:51,720 will change some of the risks in terms of regulating this space. 104 00:05:51,720 --> 00:05:54,160 And then a little reading about Asia. 105 00:05:54,160 --> 00:05:56,322 And if you've not had the opportunity to look 106 00:05:56,322 --> 00:05:58,530 at some of the readings because they were just put up 107 00:05:58,530 --> 00:06:01,450 last Friday, go back. 108 00:06:01,450 --> 00:06:02,310 Sort of dive. 109 00:06:02,310 --> 00:06:05,520 Take a little look. 110 00:06:05,520 --> 00:06:07,800 And again, we'll see if anybody wants 111 00:06:07,800 --> 00:06:10,080 to give a little view in this field, 112 00:06:10,080 --> 00:06:15,480 but what opportunities in the current insurance sector-- 113 00:06:15,480 --> 00:06:17,910 or what are the pain points, you think, 114 00:06:17,910 --> 00:06:21,060 that you might want to highlight? 115 00:06:21,060 --> 00:06:24,060 And again, this is just to get a little conversation go. 116 00:06:24,060 --> 00:06:25,410 So Romain? 117 00:06:27,932 --> 00:06:29,390 TEACHING ASSISTANT: OK, here we go. 118 00:06:29,390 --> 00:06:31,180 Who will be our first volunteer for today? 119 00:06:35,732 --> 00:06:37,940 GARY GENSLER: Has anybody ever had insurance and felt 120 00:06:37,940 --> 00:06:39,190 there was a little pain point? 121 00:06:39,190 --> 00:06:40,467 Michael, I see a hand up. 122 00:06:40,467 --> 00:06:42,050 TEACHING ASSISTANT: As always, Michael 123 00:06:42,050 --> 00:06:43,950 is helping us out to get us started. 124 00:06:43,950 --> 00:06:47,275 Go ahead. 125 00:06:47,275 --> 00:06:49,910 AUDIENCE: I think one of the obvious ones 126 00:06:49,910 --> 00:06:51,050 is just processing time. 127 00:06:51,050 --> 00:06:55,810 It's just really kind of through each step 128 00:06:55,810 --> 00:06:57,365 of the process, underwriting-- 129 00:06:57,365 --> 00:06:59,590 I don't know-- processing, completing, [INAUDIBLE],, 130 00:06:59,590 --> 00:07:00,673 it just takes a long time. 131 00:07:00,673 --> 00:07:02,440 So kind of there's definitely a lot 132 00:07:02,440 --> 00:07:05,350 of opportunities for automatic feedback or leveraging AI 133 00:07:05,350 --> 00:07:08,620 to really speed that up for consumers. 134 00:07:08,620 --> 00:07:12,200 GARY GENSLER: So the two sides of processing-- it's the front 135 00:07:12,200 --> 00:07:17,890 and when you're actually trying to get your insurance, 136 00:07:17,890 --> 00:07:19,890 when you're actually-- it's called underwriting. 137 00:07:19,890 --> 00:07:22,440 The insurance company is pricing and giving you 138 00:07:22,440 --> 00:07:24,480 availability of insurance. 139 00:07:24,480 --> 00:07:26,670 And whether it's life insurance where 140 00:07:26,670 --> 00:07:28,770 you might have to send in some medical tests 141 00:07:28,770 --> 00:07:31,660 and the like, homeowners insurance, 142 00:07:31,660 --> 00:07:35,020 renters insurance, auto insurance 143 00:07:35,020 --> 00:07:38,290 where there's a process going on at the front end. 144 00:07:38,290 --> 00:07:40,750 And then there's also the process 145 00:07:40,750 --> 00:07:42,130 if you have an accident. 146 00:07:42,130 --> 00:07:45,640 Insurance is this product that basically if you never 147 00:07:45,640 --> 00:07:49,280 hear from your insurance company and they never hear from you, 148 00:07:49,280 --> 00:07:53,650 you've paid a premium to protect you against a risk, 149 00:07:53,650 --> 00:07:56,980 but the risk has not generated a loss. 150 00:07:56,980 --> 00:07:58,850 But then you have the accident. 151 00:07:58,850 --> 00:08:00,580 Then you have the problem. 152 00:08:00,580 --> 00:08:02,680 Then you have to send in a claim. 153 00:08:02,680 --> 00:08:05,200 And that process on the back end, 154 00:08:05,200 --> 00:08:08,230 the insurance companies want to protect against fraud. 155 00:08:08,230 --> 00:08:10,000 They want to fulfill your claim, but they 156 00:08:10,000 --> 00:08:12,010 want to protect against fraud, and 157 00:08:12,010 --> 00:08:16,390 the claim-adjustment process is a significant time 158 00:08:16,390 --> 00:08:21,910 and human and paper-based and sometimes legal-based 159 00:08:21,910 --> 00:08:23,320 circumstance. 160 00:08:23,320 --> 00:08:24,440 So, Michael, you're right. 161 00:08:24,440 --> 00:08:27,310 Pain points at the front end, particular pain points 162 00:08:27,310 --> 00:08:30,100 in the claims-processing side. 163 00:08:30,100 --> 00:08:32,169 Anything else you want to throw in? 164 00:08:35,762 --> 00:08:36,554 AUDIENCE: I guess-- 165 00:08:36,554 --> 00:08:38,971 GARY GENSLER: Oh, we've got a few other hands if you want. 166 00:08:38,971 --> 00:08:41,100 I mean if you want to-- 167 00:08:41,100 --> 00:08:42,316 AUDIENCE: That's fine. 168 00:08:42,316 --> 00:08:44,242 I can pass it off to somebody else. 169 00:08:44,242 --> 00:08:46,040 TEACHING ASSISTANT: Alessandro? 170 00:08:46,040 --> 00:08:47,970 AUDIENCE: Yeah, I think another big trend 171 00:08:47,970 --> 00:08:54,410 is we're going to see different type of insurances 172 00:08:54,410 --> 00:08:57,360 come to life as more and more risks are becoming 173 00:08:57,360 --> 00:08:58,980 quantifiable. 174 00:08:58,980 --> 00:09:02,280 And this leads to a number of events or things 175 00:09:02,280 --> 00:09:04,050 we can get insurance on. 176 00:09:04,050 --> 00:09:07,810 So the selection of things on which we can get insured 177 00:09:07,810 --> 00:09:09,210 is going to increase dramatically 178 00:09:09,210 --> 00:09:12,607 because the quantifiable risks are getting bigger and bigger. 179 00:09:12,607 --> 00:09:13,440 GARY GENSLER: Right. 180 00:09:13,440 --> 00:09:16,750 And sometimes it's not just a new risk in society 181 00:09:16,750 --> 00:09:19,880 but it's, as Alexandro is saying, the quantifiability 182 00:09:19,880 --> 00:09:20,380 of it. 183 00:09:20,380 --> 00:09:23,160 So let's take auto insurance and a company that 184 00:09:23,160 --> 00:09:26,790 started a number of years ago called Metromile. 185 00:09:26,790 --> 00:09:31,140 Simple concept-- what if we charged automobile insurance 186 00:09:31,140 --> 00:09:35,510 not by the month but maybe by the mile? 187 00:09:35,510 --> 00:09:39,370 Could we quantify that you've only driven a mile 188 00:09:39,370 --> 00:09:40,940 and that would be the insurance? 189 00:09:40,940 --> 00:09:43,940 And if you're a heavy driver, there's more. 190 00:09:43,940 --> 00:09:47,450 And, in fact, what if the mile is out on the country 191 00:09:47,450 --> 00:09:50,730 roads or it's actually in the cities 192 00:09:50,730 --> 00:09:52,370 and where you're driving? 193 00:09:52,370 --> 00:09:55,460 So quantifiability, the analytics, the computer 194 00:09:55,460 --> 00:09:59,510 capability, but also the connections to all of us 195 00:09:59,510 --> 00:10:01,790 with sensors and the internet of things. 196 00:10:05,550 --> 00:10:08,342 So I've sort of given a little bit of a preview, 197 00:10:08,342 --> 00:10:09,800 but there were some other hands up. 198 00:10:09,800 --> 00:10:13,280 How have that which we've talked about all semester-- 199 00:10:13,280 --> 00:10:16,310 machine learning, alternative data-- 200 00:10:16,310 --> 00:10:17,330 influenced this? 201 00:10:17,330 --> 00:10:19,760 But a new factor that we've really not talked 202 00:10:19,760 --> 00:10:23,480 much about in banking and challenger banks and payment 203 00:10:23,480 --> 00:10:27,260 systems is the internet-of-things devices 204 00:10:27,260 --> 00:10:32,240 possibly changing the field of insurance. 205 00:10:32,240 --> 00:10:35,820 TEACHING ASSISTANT: Danielle, would you like to go. 206 00:10:35,820 --> 00:10:36,600 AUDIENCE: Sure. 207 00:10:36,600 --> 00:10:39,320 And also you just broke up there, so if you lose me, 208 00:10:39,320 --> 00:10:42,750 sorry about my internet connection. 209 00:10:42,750 --> 00:10:45,890 So there's an opportunity for the insurance landscape 210 00:10:45,890 --> 00:10:49,280 to be changed by alternative data in that companies can now 211 00:10:49,280 --> 00:10:51,650 inform their underwriting process with a lot 212 00:10:51,650 --> 00:10:54,050 more information, a lot richer set of data 213 00:10:54,050 --> 00:10:55,740 than they previously had. 214 00:10:55,740 --> 00:10:58,040 So whereas for health insurance or life insurance 215 00:10:58,040 --> 00:11:00,140 you might have had to fill out a survey-- 216 00:11:00,140 --> 00:11:03,050 people are varying degrees of honest on those 217 00:11:03,050 --> 00:11:04,880 kinds of surveys. 218 00:11:04,880 --> 00:11:08,450 Now we're suddenly generating an extremely accurate picture 219 00:11:08,450 --> 00:11:12,080 of ourselves almost passively by everything we do in the world, 220 00:11:12,080 --> 00:11:14,960 and companies can access that information. 221 00:11:14,960 --> 00:11:16,820 GARY GENSLER: Right, or can they access it? 222 00:11:16,820 --> 00:11:18,770 They still have to have a network. 223 00:11:18,770 --> 00:11:21,080 And what's really interesting in this space 224 00:11:21,080 --> 00:11:23,720 is that just as we saw a data aggregation 225 00:11:23,720 --> 00:11:27,470 an important feature in the payment space with companies 226 00:11:27,470 --> 00:11:29,660 like Plaid and Galileo and everything 227 00:11:29,660 --> 00:11:31,970 that they were right there collecting 228 00:11:31,970 --> 00:11:35,330 data between the banks and the payment systems 229 00:11:35,330 --> 00:11:39,920 and then hundreds or thousands of fintech startups, 230 00:11:39,920 --> 00:11:42,440 that there were data aggregators in the middle, 231 00:11:42,440 --> 00:11:44,300 there are data aggregators in this field 232 00:11:44,300 --> 00:11:48,770 as well to collect that data, commercialize the data, 233 00:11:48,770 --> 00:11:53,030 but maybe stand between our lives and all 234 00:11:53,030 --> 00:11:55,940 the sensors and data-collection efforts 235 00:11:55,940 --> 00:11:58,680 and hundreds or thousands of startups on the other side 236 00:11:58,680 --> 00:11:59,180 as well. 237 00:12:01,770 --> 00:12:04,082 TEACHING ASSISTANT: Nikhil? 238 00:12:04,082 --> 00:12:05,000 AUDIENCE: Sure. 239 00:12:05,000 --> 00:12:07,730 I think this is more specific to the health-insurance side 240 00:12:07,730 --> 00:12:10,670 of things where a lot of times in health 241 00:12:10,670 --> 00:12:13,910 insurance, 90% of claims are auto adjudicated, 242 00:12:13,910 --> 00:12:16,640 and most of them are based on statistical methods. 243 00:12:16,640 --> 00:12:20,090 With the influx of AI, you can review claims 244 00:12:20,090 --> 00:12:21,790 that you weren't reviewing before, 245 00:12:21,790 --> 00:12:24,360 and so you could potentially uncover more fraud, waste, 246 00:12:24,360 --> 00:12:25,970 and abuse than you were. 247 00:12:25,970 --> 00:12:28,280 So this is a market that they haven't sized yet 248 00:12:28,280 --> 00:12:31,110 because they don't know how big fraud might be. 249 00:12:31,110 --> 00:12:33,230 So I think there's a big trend there as well. 250 00:12:33,230 --> 00:12:34,430 GARY GENSLER: Great point. 251 00:12:34,430 --> 00:12:38,510 And sometimes it's not truly artificial intelligence 252 00:12:38,510 --> 00:12:39,470 or machine learning. 253 00:12:39,470 --> 00:12:44,660 It's just the remarkable ability to collect, sort, clean up 254 00:12:44,660 --> 00:12:48,920 the data, standardize the data that might also 255 00:12:48,920 --> 00:12:53,640 have a bit of machine learning on top of it, 256 00:12:53,640 --> 00:12:55,810 but it doesn't have to be the machine learning. 257 00:12:55,810 --> 00:12:58,300 It's sort of this partnering up. 258 00:12:58,300 --> 00:13:00,130 And then what are some of the challenges? 259 00:13:00,130 --> 00:13:02,130 We're going to talk about some of the challenges 260 00:13:02,130 --> 00:13:06,700 for the startups trying to get in there-- capital, regulation. 261 00:13:06,700 --> 00:13:10,260 And does anybody have a view as to the date why 262 00:13:10,260 --> 00:13:12,360 we haven't seen-- 263 00:13:12,360 --> 00:13:14,550 with some important exceptions, but we 264 00:13:14,550 --> 00:13:17,400 haven't seen big tech firms getting 265 00:13:17,400 --> 00:13:22,227 engaged in the insurance field. 266 00:13:22,227 --> 00:13:23,810 Now, there's some important exceptions 267 00:13:23,810 --> 00:13:25,550 we'll talk about in China, by the way, 268 00:13:25,550 --> 00:13:29,670 but why we haven't seen Amazon-- 269 00:13:29,670 --> 00:13:32,670 they're in the credit-card space-- and Apple 270 00:13:32,670 --> 00:13:36,980 in payment space but why we haven't seen big tech, 271 00:13:36,980 --> 00:13:38,220 by and large. 272 00:13:38,220 --> 00:13:40,070 TEACHING ASSISTANT: Laira? 273 00:13:40,070 --> 00:13:42,770 AUDIENCE: Yeah, so I just think that it's mostly 274 00:13:42,770 --> 00:13:45,910 because there's a lot of barriers to entry, 275 00:13:45,910 --> 00:13:49,040 namely as you mentioned, regulation and also 276 00:13:49,040 --> 00:13:51,710 lack of expertise, which all of these insurance companies 277 00:13:51,710 --> 00:13:52,620 already have. 278 00:13:52,620 --> 00:13:56,960 So I think it's also regulation and high costs because of lack 279 00:13:56,960 --> 00:14:00,050 of expertise that prevents these big tech firms from entering 280 00:14:00,050 --> 00:14:02,000 into the insurance market. 281 00:14:02,000 --> 00:14:04,970 GARY GENSLER: All right, so I think that's partly right, 282 00:14:04,970 --> 00:14:06,800 but I'm also going to put out the question 283 00:14:06,800 --> 00:14:11,210 throughout the class and a thought experience at the end, 284 00:14:11,210 --> 00:14:13,460 is it just that we haven't gotten there yet? 285 00:14:13,460 --> 00:14:15,650 Is it a possibility that-- 286 00:14:15,650 --> 00:14:20,240 again, big tech is about data, networks, and activities, 287 00:14:20,240 --> 00:14:23,420 as the Bank of International Settlement has written. 288 00:14:23,420 --> 00:14:26,990 And so do they want to layer another activity 289 00:14:26,990 --> 00:14:29,090 upon that network-- 290 00:14:29,090 --> 00:14:31,110 upon that network? 291 00:14:31,110 --> 00:14:32,390 We haven't quite seen it. 292 00:14:32,390 --> 00:14:35,180 I said that with one important exception in China. 293 00:14:35,180 --> 00:14:40,270 In China a number of years ago, big tech-- 294 00:14:40,270 --> 00:14:44,560 Alibaba, Tencent that has WeChat-- 295 00:14:44,560 --> 00:14:47,140 partnered up with one of the largest insurance 296 00:14:47,140 --> 00:14:49,900 companies in China, and the three of them 297 00:14:49,900 --> 00:14:53,740 started effectively an insurtech. 298 00:14:53,740 --> 00:14:56,560 It's the world's largest insurtech 299 00:14:56,560 --> 00:15:01,180 if we still call it that, and it was owned and operated 300 00:15:01,180 --> 00:15:06,130 by three very big companies. 301 00:15:06,130 --> 00:15:08,910 Ping An was the insurance company. 302 00:15:08,910 --> 00:15:12,820 And so it wasn't direct. 303 00:15:12,820 --> 00:15:15,190 It was a joint venture. 304 00:15:15,190 --> 00:15:17,170 But it's a question of will we see this 305 00:15:17,170 --> 00:15:21,200 coming into the future or not? 306 00:15:21,200 --> 00:15:24,160 So let's just sort of dive into the value chain. 307 00:15:24,160 --> 00:15:25,590 You've got to design a product. 308 00:15:25,590 --> 00:15:26,590 You've got to market it. 309 00:15:26,590 --> 00:15:29,260 It goes all the way to claims management. 310 00:15:29,260 --> 00:15:32,410 You can be in any one of these pieces. 311 00:15:32,410 --> 00:15:34,690 This is just pulled from the Federal Insurance 312 00:15:34,690 --> 00:15:38,020 Office of the US Department of Treasury from a report 313 00:15:38,020 --> 00:15:39,730 last year. 314 00:15:39,730 --> 00:15:44,950 But each of these pieces might have a piece of competition 315 00:15:44,950 --> 00:15:46,360 and a pain point. 316 00:15:46,360 --> 00:15:49,060 We talked a little bit about claims management. 317 00:15:49,060 --> 00:15:52,090 Claims management separates insurance 318 00:15:52,090 --> 00:15:55,350 from the banking fintech we've talked 319 00:15:55,350 --> 00:15:58,200 about, but a lot of the underwriting 320 00:15:58,200 --> 00:15:59,520 and the front end-- 321 00:15:59,520 --> 00:16:02,730 designing a product and the marketing-- 322 00:16:02,730 --> 00:16:05,610 you'll see that an awful lot of disruption 323 00:16:05,610 --> 00:16:07,680 is going on at that front end. 324 00:16:07,680 --> 00:16:09,840 Marketing is sales. 325 00:16:09,840 --> 00:16:10,800 It's brokerage. 326 00:16:10,800 --> 00:16:12,790 It's bringing in the customer. 327 00:16:12,790 --> 00:16:18,420 It's a better user interface, a better user experience. 328 00:16:18,420 --> 00:16:21,360 In the underwriting and rating side, 329 00:16:21,360 --> 00:16:22,612 you can underwrite credit. 330 00:16:22,612 --> 00:16:23,820 You can underwrite insurance. 331 00:16:23,820 --> 00:16:26,760 It's basically how much insurance will 332 00:16:26,760 --> 00:16:31,260 I provide to whom at what price? 333 00:16:31,260 --> 00:16:34,770 And that underwriting process ultimately 334 00:16:34,770 --> 00:16:38,280 comes to administering the policy and then the claims 335 00:16:38,280 --> 00:16:39,870 management side of it. 336 00:16:39,870 --> 00:16:42,840 So each of these pieces of the value chain 337 00:16:42,840 --> 00:16:45,300 might have pain points. 338 00:16:45,300 --> 00:16:47,850 The front four of these, designing products 339 00:16:47,850 --> 00:16:51,630 to policy administration, actually has a lot of overlap 340 00:16:51,630 --> 00:16:54,960 with the banking sector-- 341 00:16:54,960 --> 00:16:56,430 not identical. 342 00:16:56,430 --> 00:16:59,380 Important differences. 343 00:16:59,380 --> 00:17:01,440 So what's this sector look like? 344 00:17:01,440 --> 00:17:05,819 I just list companies on these several next slides-- 345 00:17:05,819 --> 00:17:08,250 life insurance, property, casualty. 346 00:17:08,250 --> 00:17:11,099 What I want you to take out of is not particular names 347 00:17:11,099 --> 00:17:15,240 in your country, whether it's China Life or MetLife or Nippon 348 00:17:15,240 --> 00:17:18,270 Life and Swiss Life. 349 00:17:18,270 --> 00:17:22,470 It's that if you look at when these companies were founded, 350 00:17:22,470 --> 00:17:31,560 many are 20th century, but many are actually 19th century. 351 00:17:31,560 --> 00:17:36,440 The concentration and the survival 352 00:17:36,440 --> 00:17:38,720 the persistence of insurance companies 353 00:17:38,720 --> 00:17:41,240 is quite consistent around the globe. 354 00:17:41,240 --> 00:17:45,140 They talk about the big five in China, but in any country-- 355 00:17:45,140 --> 00:17:48,800 in any country there's a handful of insurance companies. 356 00:17:48,800 --> 00:17:52,850 Once you get to about the top 10, you're at 85% to 90 357 00:17:52,850 --> 00:17:55,670 plus percent market share, particularly 358 00:17:55,670 --> 00:17:59,630 in life insurance and property and casualty-- a little less so 359 00:17:59,630 --> 00:18:00,570 in some other fields. 360 00:18:00,570 --> 00:18:03,230 And there's, of course, health and managed care. 361 00:18:03,230 --> 00:18:06,890 More a feature of the US health-care system 362 00:18:06,890 --> 00:18:09,830 where it's private insurance. 363 00:18:09,830 --> 00:18:13,910 And then big diversified companies across the globe. 364 00:18:13,910 --> 00:18:18,410 Diversified simply means that it's both in life and property 365 00:18:18,410 --> 00:18:20,010 and casualty and the like. 366 00:18:20,010 --> 00:18:23,590 So it's multiline. 367 00:18:23,590 --> 00:18:26,470 But let's not forget there's also reinsurance and then 368 00:18:26,470 --> 00:18:30,850 brokers, and the brokerage side is an area where disruptors 369 00:18:30,850 --> 00:18:33,520 have started to say, well, maybe we can broker insurance. 370 00:18:33,520 --> 00:18:37,720 And by brokering insurance, it's basically making the sales, 371 00:18:37,720 --> 00:18:41,800 making the marketing, but not taking the risk 372 00:18:41,800 --> 00:18:45,460 onto your own balance sheet-- 373 00:18:45,460 --> 00:18:48,820 benefits administration, software. 374 00:18:48,820 --> 00:18:51,880 The only companies that are 21st-century companies 375 00:18:51,880 --> 00:18:55,780 on this whole sort of review is in the software and services 376 00:18:55,780 --> 00:18:57,650 side of things. 377 00:18:57,650 --> 00:19:02,230 So that's kind of my kind of overall point. 378 00:19:02,230 --> 00:19:04,450 US insurance premiums, just to give you 379 00:19:04,450 --> 00:19:08,110 a sense of scale the market, about $1 and 1/2 trillion 380 00:19:08,110 --> 00:19:09,020 a year. 381 00:19:09,020 --> 00:19:10,930 We talk about the finance sector that's 382 00:19:10,930 --> 00:19:14,500 about $7 and 1/2 trillion in the US-- 383 00:19:14,500 --> 00:19:16,750 I'm sorry, 7 and 1/2%-- 384 00:19:16,750 --> 00:19:20,050 7 and 1/2% of GDP-- 385 00:19:20,050 --> 00:19:23,860 overstatement there-- which is about $1 and 1/2 trillion. 386 00:19:23,860 --> 00:19:25,940 This is consistent with that size. 387 00:19:25,940 --> 00:19:28,190 These are the premiums being paid 388 00:19:28,190 --> 00:19:31,420 into direct premiums written. 389 00:19:34,800 --> 00:19:36,872 The health and life side of things, just 390 00:19:36,872 --> 00:19:38,080 to give you a little flavor-- 391 00:19:38,080 --> 00:19:40,750 and these will all be on Canvas, of course. 392 00:19:40,750 --> 00:19:43,900 By and large, a big part of the life and health side 393 00:19:43,900 --> 00:19:46,750 is savings products. 394 00:19:46,750 --> 00:19:51,780 There's competition between life companies and banks, 395 00:19:51,780 --> 00:19:54,640 life companies and investment-management companies 396 00:19:54,640 --> 00:19:56,870 on the annuity side. 397 00:19:56,870 --> 00:20:01,090 The disruption, so to speak, has been happening largely 398 00:20:01,090 --> 00:20:02,530 in property and casualty. 399 00:20:02,530 --> 00:20:06,580 But to the extent that insurtech starts to get into this space, 400 00:20:06,580 --> 00:20:09,010 I think you will see more and more disruption 401 00:20:09,010 --> 00:20:11,890 in the savings-products side as well. 402 00:20:11,890 --> 00:20:14,920 The companies like Betterment and all those companies 403 00:20:14,920 --> 00:20:17,560 we talked about in the wealth-management side 404 00:20:17,560 --> 00:20:21,910 or in wealthtech I think you will see starting to say, wait. 405 00:20:21,910 --> 00:20:24,670 Maybe we can get into the annuity side 406 00:20:24,670 --> 00:20:27,580 as well, the annuity and savings side 407 00:20:27,580 --> 00:20:29,950 of life-insurance products. 408 00:20:29,950 --> 00:20:33,250 Property and casualty, just a little breakdown 409 00:20:33,250 --> 00:20:34,045 here in the US. 410 00:20:36,840 --> 00:20:40,330 And so now we turn to challenges, pain points. 411 00:20:40,330 --> 00:20:41,590 What do we have? 412 00:20:41,590 --> 00:20:43,813 This is true of any incumbent company. 413 00:20:43,813 --> 00:20:45,730 You could be a company that's been in business 414 00:20:45,730 --> 00:20:48,400 for a hundred-plus years, and you're 415 00:20:48,400 --> 00:20:52,690 sitting there going I'm selling through an agent or broker 416 00:20:52,690 --> 00:20:54,470 network. 417 00:20:54,470 --> 00:20:59,120 And the agency and brokerage network here in the US 418 00:20:59,120 --> 00:21:02,560 takes a big chunk of fees. 419 00:21:02,560 --> 00:21:04,780 I think on the property/casualty side, 420 00:21:04,780 --> 00:21:10,970 we're probably in the 10% to 13% of premiums on average. 421 00:21:10,970 --> 00:21:15,550 But in some products, it's much larger, some a little smaller. 422 00:21:15,550 --> 00:21:19,990 The life side, the premiums I think are in high single digits 423 00:21:19,990 --> 00:21:23,340 to 10% on average. 424 00:21:23,340 --> 00:21:28,170 And while I'm not sort of an expert of all the fee, that 425 00:21:28,170 --> 00:21:33,560 basically means if you pay $1,000 for your auto insurance, 426 00:21:33,560 --> 00:21:39,990 $100 plus, $120 is being taken by those agents and brokers. 427 00:21:39,990 --> 00:21:42,120 Claims administration, we talked about it. 428 00:21:42,120 --> 00:21:44,340 Very paper and human intensive. 429 00:21:44,340 --> 00:21:48,060 Litigation risk all abound. 430 00:21:48,060 --> 00:21:52,980 Insurance companies are built up around this legacy tech, 431 00:21:52,980 --> 00:22:00,360 just like in payments and in credit. 432 00:22:00,360 --> 00:22:04,260 Of every dollar of premium, a big chunk is not only 433 00:22:04,260 --> 00:22:06,330 going to the agents and the brokers 434 00:22:06,330 --> 00:22:08,310 but a big chunk is going, of course, 435 00:22:08,310 --> 00:22:13,860 to the whole claims processing and the whole value chain. 436 00:22:13,860 --> 00:22:17,460 And because it's deeply regulated, in many marketplaces 437 00:22:17,460 --> 00:22:20,280 you can't offer a new type of insurance 438 00:22:20,280 --> 00:22:23,020 without getting some official sector approval. 439 00:22:23,020 --> 00:22:27,680 Going through the product-design phase 440 00:22:27,680 --> 00:22:33,300 often has legacy and time delays within it. 441 00:22:33,300 --> 00:22:35,730 And, of course, just like the other fields, 442 00:22:35,730 --> 00:22:38,970 there's whole sorts of questions about user interface and user 443 00:22:38,970 --> 00:22:40,230 experience. 444 00:22:40,230 --> 00:22:42,720 All of these present challenges, but they also 445 00:22:42,720 --> 00:22:44,700 present opportunities. 446 00:22:44,700 --> 00:22:46,717 Romain, any questions? 447 00:22:46,717 --> 00:22:48,300 TEACHING ASSISTANT: Not yet, but let's 448 00:22:48,300 --> 00:22:49,435 wait perhaps a few seconds. 449 00:22:55,930 --> 00:22:57,350 No, I don't see any hands up. 450 00:22:57,350 --> 00:22:58,350 GARY GENSLER: All right. 451 00:23:00,260 --> 00:23:05,340 So insurance and insurance tech-- 452 00:23:05,340 --> 00:23:07,530 you can sort of say I want to be involved 453 00:23:07,530 --> 00:23:11,910 in the pricing and underwriting, the quotes, the policy 454 00:23:11,910 --> 00:23:14,430 administration, the claims management, sort of four 455 00:23:14,430 --> 00:23:18,030 different sectors, improving the customer engagement, 456 00:23:18,030 --> 00:23:20,260 lowering operating costs. 457 00:23:20,260 --> 00:23:22,020 Look, of course it always goes back 458 00:23:22,020 --> 00:23:26,250 to can you do something better, quicker, cheaper? 459 00:23:26,250 --> 00:23:29,730 But as we talked about earlier, it could be in product design 460 00:23:29,730 --> 00:23:31,200 as well. 461 00:23:31,200 --> 00:23:34,410 It could be in not just doing it cheaper in pricing 462 00:23:34,410 --> 00:23:38,910 but more availability and, as Metromile has done, really 463 00:23:38,910 --> 00:23:44,520 in using metrics to get to a new type of product 464 00:23:44,520 --> 00:23:49,820 itself and the quoting and issuing of products. 465 00:23:49,820 --> 00:23:52,620 So what are some of the opportunities 466 00:23:52,620 --> 00:23:54,420 more specifically? 467 00:23:54,420 --> 00:23:57,840 I think across financial technology, 468 00:23:57,840 --> 00:24:01,110 it's about user interface and user experience. 469 00:24:01,110 --> 00:24:02,850 So that could be on the sales side. 470 00:24:02,850 --> 00:24:05,190 It can be in the account management, 471 00:24:05,190 --> 00:24:07,350 digital and mobile phones, but also 472 00:24:07,350 --> 00:24:11,580 in the conversational interface. 473 00:24:11,580 --> 00:24:14,940 Underwriting, we've talked about it, really not just 474 00:24:14,940 --> 00:24:19,180 the machine learning but the alternative data, 475 00:24:19,180 --> 00:24:20,650 and then claims processing. 476 00:24:20,650 --> 00:24:24,150 I think these are the big three. 477 00:24:24,150 --> 00:24:27,120 They seem pretty simple once I say it. 478 00:24:27,120 --> 00:24:29,890 But it's sort of like thinking about it, 479 00:24:29,890 --> 00:24:31,320 and if you have a business model, 480 00:24:31,320 --> 00:24:33,300 if you're going into this business 481 00:24:33,300 --> 00:24:36,600 or you're at Prudential Life Insurance or Nippon 482 00:24:36,600 --> 00:24:40,080 Life, it's sort of saying, how can we use technology 483 00:24:40,080 --> 00:24:42,700 to enhance user experience? 484 00:24:42,700 --> 00:24:46,860 How can we enhance basically the pricing and the availability 485 00:24:46,860 --> 00:24:49,080 of our product underwriting? 486 00:24:49,080 --> 00:24:51,390 How do we do the back end? 487 00:24:51,390 --> 00:24:54,480 The first two, user experience and underwriting, 488 00:24:54,480 --> 00:24:57,780 very much true in banking tech. 489 00:24:57,780 --> 00:25:01,050 Claims processing, somewhat unique here. 490 00:25:01,050 --> 00:25:02,400 And it's about data capture. 491 00:25:04,960 --> 00:25:08,800 Now, one part of the data capture-- 492 00:25:08,800 --> 00:25:10,360 and you can see it here-- 493 00:25:10,360 --> 00:25:14,950 drones, the internet of things, smartphones, telematics, 494 00:25:14,950 --> 00:25:15,910 wearables. 495 00:25:18,530 --> 00:25:21,020 I'm curious-- Romain, you'll see if anyone 496 00:25:21,020 --> 00:25:22,460 will raise their hand. 497 00:25:22,460 --> 00:25:26,590 Has anybody actually taken insurance 498 00:25:26,590 --> 00:25:30,700 where the insurance company has said for your automobile, 499 00:25:30,700 --> 00:25:33,610 we want to have access to your driving record 500 00:25:33,610 --> 00:25:36,760 directly through your car's telematics? 501 00:25:36,760 --> 00:25:42,370 Telematics is a word that didn't exist 30 and 50 years ago, 502 00:25:42,370 --> 00:25:45,940 or if it did exist, it wasn't commonly used. 503 00:25:45,940 --> 00:25:52,960 It's about all the information that your car is collecting 504 00:25:52,960 --> 00:25:59,730 on driving in the computers on board your car, 505 00:25:59,730 --> 00:26:02,190 and it's not just GPS. 506 00:26:02,190 --> 00:26:05,000 And it can sort of assess your driving. 507 00:26:05,000 --> 00:26:10,740 Smartphones as well can assess our driving because smartphones 508 00:26:10,740 --> 00:26:15,540 are not just locational devices, but they can get all the ups 509 00:26:15,540 --> 00:26:18,420 and downs of your driving. 510 00:26:18,420 --> 00:26:19,770 What does that mean? 511 00:26:19,770 --> 00:26:23,910 That between smartphones and the computers 512 00:26:23,910 --> 00:26:28,870 on our cars, a product can be offered 513 00:26:28,870 --> 00:26:31,090 that says we know enough about your driving 514 00:26:31,090 --> 00:26:34,622 that we will give you fine-tuned pricing and underwriting. 515 00:26:34,622 --> 00:26:35,830 TEACHING ASSISTANT: Danielle? 516 00:26:35,830 --> 00:26:36,705 GARY GENSLER: Romain? 517 00:26:39,210 --> 00:26:43,650 AUDIENCE: Yeah, I had a question around Google's deal 518 00:26:43,650 --> 00:26:46,920 with Ascension to store and analyze the medical records 519 00:26:46,920 --> 00:26:48,840 from their hospital network. 520 00:26:48,840 --> 00:26:52,980 And I'm curious if you think that kind of entrance, 521 00:26:52,980 --> 00:26:54,750 the big tech into the medical world, 522 00:26:54,750 --> 00:26:56,940 is going to be more common and if that might 523 00:26:56,940 --> 00:26:59,850 be a potential avenue for those kinds of companies getting 524 00:26:59,850 --> 00:27:03,602 the insight into health data that they 525 00:27:03,602 --> 00:27:05,310 would need to make them confident to move 526 00:27:05,310 --> 00:27:06,360 into insurance? 527 00:27:06,360 --> 00:27:08,910 GARY GENSLER: I think you've landed 528 00:27:08,910 --> 00:27:13,620 upon one area that big tech can be very influential, 529 00:27:13,620 --> 00:27:17,220 particularly the big AI companies like Google and Baidu 530 00:27:17,220 --> 00:27:20,190 in China, to the extent-- 531 00:27:20,190 --> 00:27:23,730 and we'll see there are some startups that 532 00:27:23,730 --> 00:27:28,260 are entering this field because they're very good at data 533 00:27:28,260 --> 00:27:30,720 analytics and machine learning. 534 00:27:30,720 --> 00:27:32,370 And to the extent that they can be 535 00:27:32,370 --> 00:27:35,830 data aggregators like Google, you're absolutely right. 536 00:27:35,830 --> 00:27:37,830 I don't think it's just health care, by the way, 537 00:27:37,830 --> 00:27:41,220 but I think health care is a really important piece of it, 538 00:27:41,220 --> 00:27:45,210 that many insurance companies, health-care management 539 00:27:45,210 --> 00:27:49,200 companies in the US have a tremendous amount of data, 540 00:27:49,200 --> 00:27:51,110 and they want to make more sense of it. 541 00:27:51,110 --> 00:27:53,190 And they want to, with that data, 542 00:27:53,190 --> 00:27:56,310 provide more fine-tuned insurance products 543 00:27:56,310 --> 00:28:00,940 but also help manage our health-care system better. 544 00:28:00,940 --> 00:28:04,170 So I think it is an avenue for certain big tech companies that 545 00:28:04,170 --> 00:28:09,840 are at the cutting edge of data analytics and machine learning. 546 00:28:09,840 --> 00:28:16,590 Not for every big tech company but for many I agree with that. 547 00:28:16,590 --> 00:28:19,350 Did anybody have an insurance-- did anybody take out 548 00:28:19,350 --> 00:28:20,550 an auto insurance? 549 00:28:24,320 --> 00:28:26,320 TEACHING ASSISTANT: I think probably Alessandro. 550 00:28:26,320 --> 00:28:29,450 He had his hand up but probably for a question. 551 00:28:29,450 --> 00:28:36,840 AUDIENCE: I had this little chip or a computer on board. 552 00:28:36,840 --> 00:28:41,120 And basically, yeah, it traced everything 553 00:28:41,120 --> 00:28:45,920 that I did with the car, like where I was, how many miles I 554 00:28:45,920 --> 00:28:48,270 drove, everything. 555 00:28:48,270 --> 00:28:53,375 And it actually helped me to decrease the cost of insurance. 556 00:28:53,375 --> 00:28:54,500 GARY GENSLER: There you go. 557 00:28:54,500 --> 00:28:55,190 There you go. 558 00:28:55,190 --> 00:29:02,050 Now, some of the first patents filed on telematics 559 00:29:02,050 --> 00:29:05,840 were in the late 1990s. 560 00:29:05,840 --> 00:29:08,170 But what we found is it's really changed 561 00:29:08,170 --> 00:29:10,180 the field of auto insurance. 562 00:29:10,180 --> 00:29:12,910 This little visualization about insurtech, 563 00:29:12,910 --> 00:29:16,600 again from the Department of Treasury study last fall, 564 00:29:16,600 --> 00:29:20,260 I think is just a good place to pause for a second. 565 00:29:20,260 --> 00:29:22,470 We're not going to go through every box, 566 00:29:22,470 --> 00:29:26,230 but whether it's sensors and telematics-- 567 00:29:26,230 --> 00:29:30,220 and this is a big piece of having those sensors 568 00:29:30,220 --> 00:29:35,260 and telematics there to lower Alexandro's cost of insurance 569 00:29:35,260 --> 00:29:37,840 but also for claims management. 570 00:29:37,840 --> 00:29:42,260 If the telematics and sensors are there upon an accident, 571 00:29:42,260 --> 00:29:47,410 then you can lower a little bit the cost of claims management, 572 00:29:47,410 --> 00:29:50,320 and the insurance companies can have confidence 573 00:29:50,320 --> 00:29:53,710 the accident actually happened, potentially figure out 574 00:29:53,710 --> 00:29:59,780 who's at fault, see right at that moment what's 575 00:29:59,780 --> 00:30:03,060 going on in that automobile. 576 00:30:03,060 --> 00:30:05,760 Visual computing, literally downloading, 577 00:30:05,760 --> 00:30:10,680 uploading photographs into the claims management process. 578 00:30:10,680 --> 00:30:13,020 We've already talked about machine learning and AI, 579 00:30:13,020 --> 00:30:15,960 but blockchain technology I want to touch upon. 580 00:30:15,960 --> 00:30:19,470 Of all the fields of blockchain technology 581 00:30:19,470 --> 00:30:22,080 that we generally have heard about-- and many of 582 00:30:22,080 --> 00:30:25,380 them are hype versus reality. 583 00:30:25,380 --> 00:30:31,440 One field is whether with the use of blockchain technology 584 00:30:31,440 --> 00:30:34,770 that we can have an append-only log that 585 00:30:34,770 --> 00:30:37,080 is tamper resistant whether there would 586 00:30:37,080 --> 00:30:42,880 be trust in a field of insurance called parametric insurance. 587 00:30:42,880 --> 00:30:47,350 In essence, if we had an automated smart contract 588 00:30:47,350 --> 00:30:52,810 that said that if this happens, I get paid. 589 00:30:52,810 --> 00:30:54,010 So what's the this? 590 00:30:54,010 --> 00:30:58,780 One field that a group of students studied last year, 591 00:30:58,780 --> 00:31:03,640 studying it on behalf of a group in Asia, 592 00:31:03,640 --> 00:31:08,870 was parametric insurance for airline delays. 593 00:31:08,870 --> 00:31:15,760 If you could embed on a tamper-resistant ledger. 594 00:31:15,760 --> 00:31:18,720 On that ledger, you would embed smart contracts 595 00:31:18,720 --> 00:31:22,480 that says if the plane takes off more than a half hour late, you 596 00:31:22,480 --> 00:31:24,340 automatically get paid. 597 00:31:24,340 --> 00:31:25,390 A mobile app. 598 00:31:28,560 --> 00:31:30,540 No trying to rely on the airlines 599 00:31:30,540 --> 00:31:32,610 whether they're accurately telling you 600 00:31:32,610 --> 00:31:36,580 or not that the plane took off late. 601 00:31:36,580 --> 00:31:38,230 Put it on a blockchain technology. 602 00:31:38,230 --> 00:31:40,995 Now, maybe you could do it with a central database as well, 603 00:31:40,995 --> 00:31:42,370 but there are many people looking 604 00:31:42,370 --> 00:31:44,510 to whether to use blockchain technology 605 00:31:44,510 --> 00:31:46,630 for parametric insurance. 606 00:31:46,630 --> 00:31:49,420 But the sensors and telematics, the visual computing, 607 00:31:49,420 --> 00:31:51,460 the machine learning, those are real. 608 00:31:51,460 --> 00:31:53,380 The Department of Treasury sort of also put 609 00:31:53,380 --> 00:31:58,840 something on the edge in terms of blockchain technology. 610 00:31:58,840 --> 00:32:02,580 Challenges though abound for insurtech. 611 00:32:02,580 --> 00:32:05,590 There's still the age-old challenge of funding. 612 00:32:05,590 --> 00:32:09,310 And it's particularly true in insurance 613 00:32:09,310 --> 00:32:12,580 because if you take an insurance risk onto your balance sheet, 614 00:32:12,580 --> 00:32:16,260 that's a liability of an insurance company. 615 00:32:16,260 --> 00:32:18,710 That's a liability that in the future 616 00:32:18,710 --> 00:32:24,710 they have to pay out a claim on an accident, a loss 617 00:32:24,710 --> 00:32:28,610 on your home, or on your life. 618 00:32:28,610 --> 00:32:31,030 So many companies are finding challenges. 619 00:32:31,030 --> 00:32:35,150 If they're startups, how can they access capital? 620 00:32:35,150 --> 00:32:37,180 Now in the banking sector, you can possibly 621 00:32:37,180 --> 00:32:40,240 do it through securitization. 622 00:32:40,240 --> 00:32:41,760 You can basically say I'm not going 623 00:32:41,760 --> 00:32:44,500 to build my own balance sheet. 624 00:32:44,500 --> 00:32:47,440 I'm going to sort of rent a balance sheet. 625 00:32:47,440 --> 00:32:50,500 I'm going to lay it off. 626 00:32:50,500 --> 00:32:55,390 Similarly here, you can lay off some risk in the reinsurance 627 00:32:55,390 --> 00:32:57,430 markets, but it's not quite the same 628 00:32:57,430 --> 00:32:59,890 for these insurtech startups. 629 00:32:59,890 --> 00:33:06,500 Most challenger banks partner with some bank, 630 00:33:06,500 --> 00:33:10,850 get a warehouse line of credit, and possibly also then 631 00:33:10,850 --> 00:33:15,410 sell their loans after they're initiated. 632 00:33:15,410 --> 00:33:17,360 Here, it's not as robust. 633 00:33:17,360 --> 00:33:18,590 It's not as developed. 634 00:33:18,590 --> 00:33:22,940 But again, many insurance fintech startups 635 00:33:22,940 --> 00:33:26,930 are saying do I partner with a big, dominant insurance company 636 00:33:26,930 --> 00:33:31,140 because I can't build my balance sheet as rapidly? 637 00:33:31,140 --> 00:33:32,970 If you choose to be a carrier-- 638 00:33:32,970 --> 00:33:36,300 and a carrier is the term to say you actually take the insurance 639 00:33:36,300 --> 00:33:37,600 onto your balance sheet. 640 00:33:37,600 --> 00:33:39,060 You have the liability. 641 00:33:39,060 --> 00:33:43,210 Choose to be a carrier, it's balance-sheet intensive. 642 00:33:43,210 --> 00:33:46,540 Also, just how do you find your startup run-rate loss 643 00:33:46,540 --> 00:33:49,270 because all the startups tend to have 644 00:33:49,270 --> 00:33:52,720 multiple years of building out a network 645 00:33:52,720 --> 00:33:55,620 and building the platform itself. 646 00:33:55,620 --> 00:33:57,540 Clearly the competitive landscape, 647 00:33:57,540 --> 00:33:59,730 the regulatory frameworks-- 648 00:33:59,730 --> 00:34:02,580 the regulatory frameworks, do you get licensed? 649 00:34:02,580 --> 00:34:04,650 Again, similar to the challenger banks. 650 00:34:04,650 --> 00:34:07,800 Remember we talked about there's neobanks and challenger 651 00:34:07,800 --> 00:34:12,480 banks which often people sort of say in the same breath 652 00:34:12,480 --> 00:34:13,800 they're the same. 653 00:34:13,800 --> 00:34:15,810 But more specifically, a neobank is 654 00:34:15,810 --> 00:34:17,639 something doing a lot of banking functions 655 00:34:17,639 --> 00:34:20,550 that has not yet gotten a banking license. 656 00:34:20,550 --> 00:34:24,570 Similar here, if you're thinking about starting an insurance 657 00:34:24,570 --> 00:34:28,050 fintech firm, what type of licensing will you get? 658 00:34:28,050 --> 00:34:30,239 Will it just be a brokerage licensing? 659 00:34:30,239 --> 00:34:31,710 In various jurisdictions, you have 660 00:34:31,710 --> 00:34:34,630 to register to be a broker or an agent. 661 00:34:34,630 --> 00:34:37,420 Are you actually registering to take lines onto your balance 662 00:34:37,420 --> 00:34:38,545 sheet and become a carrier? 663 00:34:41,300 --> 00:34:46,010 And then, of course, user adoption and the use of data. 664 00:34:46,010 --> 00:34:48,800 And in terms of the use of data, there 665 00:34:48,800 --> 00:34:51,260 aren't quite the same laws. 666 00:34:51,260 --> 00:34:53,630 We have the same conceptual framework 667 00:34:53,630 --> 00:34:56,360 that you have to be fair and unbiased, 668 00:34:56,360 --> 00:34:58,400 but there aren't quite the same laws 669 00:34:58,400 --> 00:35:02,150 like the Equal Credit Opportunity Act here in the US. 670 00:35:02,150 --> 00:35:05,720 And in terms of the use of data, as we'll see in a minute, 671 00:35:05,720 --> 00:35:09,530 if you sign a waiver and say you can use my driving record 672 00:35:09,530 --> 00:35:13,540 to give my insurance, is Alexandro did, 673 00:35:13,540 --> 00:35:17,850 they can use that data also on the claims-management side. 674 00:35:17,850 --> 00:35:19,680 It's not just to lower the cost. 675 00:35:19,680 --> 00:35:23,310 It's also to see, if you call up and say an accident, 676 00:35:23,310 --> 00:35:28,870 they look at the telematics and say, did this really happen? 677 00:35:28,870 --> 00:35:31,410 So the insurtech landscape-- we're not going to dive 678 00:35:31,410 --> 00:35:34,230 into this slide, but it's just each of these pieces-- 679 00:35:34,230 --> 00:35:36,480 each of these pieces, it might be 680 00:35:36,480 --> 00:35:40,320 that you're trying to go into the billing and payments, which 681 00:35:40,320 --> 00:35:42,720 is sort of rudimentary, sort of. 682 00:35:42,720 --> 00:35:46,350 You could be going all the way to another area 683 00:35:46,350 --> 00:35:49,110 and say we're going to be the best data 684 00:35:49,110 --> 00:35:51,720 analytics on wearables. 685 00:35:51,720 --> 00:35:55,430 And by I mean wearables, how many people have a Fitbit? 686 00:35:55,430 --> 00:35:57,800 Does anybody want to talk about whether you've signed up 687 00:35:57,800 --> 00:36:01,370 for insurance that your insurance company 688 00:36:01,370 --> 00:36:04,325 can get your data off of your Fitbit 689 00:36:04,325 --> 00:36:08,480 or off of your Apple watch? 690 00:36:08,480 --> 00:36:11,450 What you're using for exercise can 691 00:36:11,450 --> 00:36:12,995 lower your cost of insurance. 692 00:36:12,995 --> 00:36:14,375 I'm going to pause, Romain. 693 00:36:17,915 --> 00:36:19,790 TEACHING ASSISTANT: I don't see any hands up. 694 00:36:19,790 --> 00:36:21,680 Lindsay, go ahead. 695 00:36:21,680 --> 00:36:24,790 AUDIENCE: Well, I didn't have like the agreement 696 00:36:24,790 --> 00:36:27,370 with the insurance, but what I did have 697 00:36:27,370 --> 00:36:32,440 is that my former employer had a program where we would connect 698 00:36:32,440 --> 00:36:36,400 our tracking devices like for steps and stuff 699 00:36:36,400 --> 00:36:40,120 with this program that would give you kickbacks 700 00:36:40,120 --> 00:36:43,060 if you got so many steps a day. 701 00:36:43,060 --> 00:36:46,960 So I'd get tons of gift cards because of all the steps 702 00:36:46,960 --> 00:36:48,070 I tracked in. 703 00:36:48,070 --> 00:36:50,802 And that data I'm sure was going to the insurance company 704 00:36:50,802 --> 00:36:52,010 to lower the insurance rates. 705 00:36:52,010 --> 00:36:52,843 GARY GENSLER: Right. 706 00:36:52,843 --> 00:36:55,240 So if you got to your 10,000 steps-- or maybe 707 00:36:55,240 --> 00:36:58,390 they gave you a premium for 5,000 steps. 708 00:36:58,390 --> 00:37:02,680 You got some rewards. 709 00:37:02,680 --> 00:37:06,610 We all know about rewards programs in credit cards, 710 00:37:06,610 --> 00:37:11,830 and that's part of that 2 and 3/4% interchange fees. 711 00:37:11,830 --> 00:37:15,550 These rewards by your employer, ultimately 712 00:37:15,550 --> 00:37:17,680 the economic incentives is the employer 713 00:37:17,680 --> 00:37:20,410 can have lower cost of health insurance. 714 00:37:20,410 --> 00:37:23,770 That data through the intermediation 715 00:37:23,770 --> 00:37:28,900 of your employment contract was lowering health-insurance costs 716 00:37:28,900 --> 00:37:30,670 for some group health insurance. 717 00:37:30,670 --> 00:37:32,650 Now, I think your employer was also 718 00:37:32,650 --> 00:37:35,530 doing it because they know if you're a better health that you 719 00:37:35,530 --> 00:37:37,480 also have fewer days off. 720 00:37:37,480 --> 00:37:41,380 You'll be hopefully a better employee. 721 00:37:41,380 --> 00:37:45,640 So they're also lowering some of their costs 722 00:37:45,640 --> 00:37:48,550 in terms of their direct costs, but it also 723 00:37:48,550 --> 00:37:52,010 relates to health insurance. 724 00:37:52,010 --> 00:37:57,250 So wearables, trackables, telematics, and the internet 725 00:37:57,250 --> 00:37:57,910 of thing. 726 00:37:57,910 --> 00:38:02,820 The internet of thing is about sensors. 727 00:38:02,820 --> 00:38:09,050 And it's about sensors in many items in our home as well. 728 00:38:09,050 --> 00:38:11,960 If you have a home security system, 729 00:38:11,960 --> 00:38:14,420 in the last 5 or 10 years, you've 730 00:38:14,420 --> 00:38:19,190 been able to tie that into more data. 731 00:38:19,190 --> 00:38:23,870 Now, home security systems have for decades somehow 732 00:38:23,870 --> 00:38:28,040 had to communicate through a security company 733 00:38:28,040 --> 00:38:30,990 to the law enforcement if the alarm went off, 734 00:38:30,990 --> 00:38:34,910 but I'm talking about more fine-tune monitoring. 735 00:38:34,910 --> 00:38:37,940 And we all are aware, even though that very few of us 736 00:38:37,940 --> 00:38:43,230 might have it, that you can have apps on your phone 737 00:38:43,230 --> 00:38:46,050 that you can communicate with your home 738 00:38:46,050 --> 00:38:50,250 to turn on your heat or your air conditioning, 739 00:38:50,250 --> 00:38:54,510 monitor your home, home-monitoring devices that 740 00:38:54,510 --> 00:38:58,410 makes all of us maybe more comfortable 741 00:38:58,410 --> 00:39:02,490 that our home is secure or even to have 742 00:39:02,490 --> 00:39:06,760 the communication from the home if, for some reason, 743 00:39:06,760 --> 00:39:09,640 there is a problem in the home. 744 00:39:09,640 --> 00:39:12,220 And there's sensors on refrigerators. 745 00:39:12,220 --> 00:39:17,110 There's sensors on HVAC, heating and ventilation systems. 746 00:39:17,110 --> 00:39:19,280 There are sensors throughout houses. 747 00:39:19,280 --> 00:39:23,500 The 2020s, we're going to see this just absolutely mushroom 748 00:39:23,500 --> 00:39:25,210 in terms of sensors. 749 00:39:25,210 --> 00:39:27,760 So what's that data do for the insurance field? 750 00:39:27,760 --> 00:39:31,480 That's why I think insurtech and insurance and fintech 751 00:39:31,480 --> 00:39:34,900 is such an exciting and interesting place because we 752 00:39:34,900 --> 00:39:38,110 are basically in a transformational time 753 00:39:38,110 --> 00:39:42,610 right now in terms of sensor technology and embedding 754 00:39:42,610 --> 00:39:46,930 sensors into our economies not because of insurance 755 00:39:46,930 --> 00:39:53,570 but because many users want to sort of monitor 756 00:39:53,570 --> 00:39:56,810 either their home, their automobile, their wearable, 757 00:39:56,810 --> 00:39:58,510 their trackable. 758 00:39:58,510 --> 00:40:03,680 And now with this really challenging time 759 00:40:03,680 --> 00:40:06,860 around the coronavirus crisis, we're 760 00:40:06,860 --> 00:40:09,770 talking about contact tracing. 761 00:40:09,770 --> 00:40:12,710 And all that contact tracing could also 762 00:40:12,710 --> 00:40:16,130 be fed in, depending upon the jurisdiction, 763 00:40:16,130 --> 00:40:21,200 depending upon the regulation, into health-insurance rates, 764 00:40:21,200 --> 00:40:26,650 life-insurance rates, or any form of insurance. 765 00:40:26,650 --> 00:40:32,860 Again, largely dependent upon the various jurisdictions' 766 00:40:32,860 --> 00:40:37,000 regulatory and legal frameworks. 767 00:40:37,000 --> 00:40:39,040 So what are some of the possibilities? 768 00:40:39,040 --> 00:40:41,560 You can become a licensed insurer. 769 00:40:41,560 --> 00:40:44,770 That means actually committing balance sheet, 770 00:40:44,770 --> 00:40:48,370 becoming licensed, taking claims on as a liability, 771 00:40:48,370 --> 00:40:51,460 managing the asset side as well. 772 00:40:51,460 --> 00:40:53,530 You can be a managed general agent. 773 00:40:53,530 --> 00:40:54,610 Think broker. 774 00:40:54,610 --> 00:40:58,420 This is just a term to say you could be managing 775 00:40:58,420 --> 00:41:01,120 agents or brokerage or sales. 776 00:41:01,120 --> 00:41:04,600 Those are kind of the two big divides. 777 00:41:04,600 --> 00:41:06,910 But you could just be on the technology side 778 00:41:06,910 --> 00:41:08,620 or the data-aggregation side. 779 00:41:08,620 --> 00:41:12,310 These are the various models for insurtech. 780 00:41:12,310 --> 00:41:15,140 Some of the startups-- we won't go through each one of them, 781 00:41:15,140 --> 00:41:20,350 but you're going to see a common thing in these lists is, 782 00:41:20,350 --> 00:41:24,190 by and large, all founded in the 20-teens. 783 00:41:24,190 --> 00:41:30,060 So this is 5 or 10 years of dynamic change. 784 00:41:30,060 --> 00:41:32,590 You're going to see heavy emphasis 785 00:41:32,590 --> 00:41:35,590 on homeowners and autos. 786 00:41:35,590 --> 00:41:41,460 So it's in the consumer space, very similar to credit. 787 00:41:41,460 --> 00:41:44,670 You see a little bit moving into small business but not 788 00:41:44,670 --> 00:41:48,900 large commercial lines, though Bold Penguin 789 00:41:48,900 --> 00:41:52,140 is a commercial insurance exchange. 790 00:41:52,140 --> 00:41:53,550 A lot is on the front end. 791 00:41:53,550 --> 00:41:57,630 And on the auto side, you also get marketplace comparisons, 792 00:41:57,630 --> 00:41:59,970 just like in the credit space. 793 00:41:59,970 --> 00:42:03,900 It's basically Expedia for auto insurance, so 794 00:42:03,900 --> 00:42:06,900 to speak you could say, or Expedia for fill 795 00:42:06,900 --> 00:42:09,100 in the blank insurance. 796 00:42:09,100 --> 00:42:11,730 These are the marketplace comparisons 797 00:42:11,730 --> 00:42:15,110 like CoverHound and Goji. 798 00:42:17,700 --> 00:42:22,910 But throughout, you'll see home and auto, 799 00:42:22,910 --> 00:42:26,570 small business is kind of the dominant. 800 00:42:26,570 --> 00:42:30,560 Has anybody used the product Lemonade for renters insurance? 801 00:42:35,660 --> 00:42:36,235 Wow. 802 00:42:36,235 --> 00:42:38,110 TEACHING ASSISTANT: I don't see any hands up. 803 00:42:38,110 --> 00:42:40,760 GARY GENSLER: Nobody. 804 00:42:40,760 --> 00:42:43,010 So Lemonade got into the field basically 805 00:42:43,010 --> 00:42:48,260 to say so many people, particularly in the US, 806 00:42:48,260 --> 00:42:53,720 are renting, and we just want some insurance for our objects 807 00:42:53,720 --> 00:42:57,830 and our things in our homes in case there's a problem. 808 00:42:57,830 --> 00:43:01,400 Or maybe the landlord says you have to have renters insurance. 809 00:43:06,310 --> 00:43:11,800 PolicyBazaar-- PolicyBazaar, about 12 years old, 810 00:43:11,800 --> 00:43:17,300 one of the largest marketplace comparison in India. 811 00:43:17,300 --> 00:43:19,360 It is a remarkable platform. 812 00:43:19,360 --> 00:43:23,530 Again, think, broadly speaking, Expedia for health and life 813 00:43:23,530 --> 00:43:26,690 insurance where you can price compare-- 814 00:43:26,690 --> 00:43:27,775 PolicyBazaar. 815 00:43:31,310 --> 00:43:34,220 Of these various companies-- 816 00:43:34,220 --> 00:43:35,990 just pause a bit on Root. 817 00:43:35,990 --> 00:43:38,660 Root uses telematics to price their products. 818 00:43:38,660 --> 00:43:42,410 Almost every one of the auto companies on here 819 00:43:42,410 --> 00:43:45,830 are using telematics and smartphones. 820 00:43:45,830 --> 00:43:48,250 Some of them started on telematics, 821 00:43:48,250 --> 00:43:50,840 some started more on the smartphone side, 822 00:43:50,840 --> 00:43:52,550 but the merger of those two. 823 00:43:55,290 --> 00:43:59,220 Tractable in the auto field is about claims management, just 824 00:43:59,220 --> 00:44:01,770 solely sort of focusing there. 825 00:44:01,770 --> 00:44:04,140 And I said I was going to mention about China, 826 00:44:04,140 --> 00:44:06,510 the last on this list, and I probably 827 00:44:06,510 --> 00:44:08,460 will mispronounce how to say it. 828 00:44:08,460 --> 00:44:16,430 ZhongAn was formed by three very large companies. 829 00:44:16,430 --> 00:44:20,420 Online property and casualty in China. 830 00:44:20,420 --> 00:44:22,520 It's gone public since. 831 00:44:22,520 --> 00:44:26,180 It's a very significant company in China. 832 00:44:26,180 --> 00:44:30,830 It's the only online insurance company 833 00:44:30,830 --> 00:44:33,390 that got licensing in China. 834 00:44:33,390 --> 00:44:34,940 So it chose to get licensing. 835 00:44:34,940 --> 00:44:37,190 It had the backing of three very large companies 836 00:44:37,190 --> 00:44:38,280 seven years ago. 837 00:44:38,280 --> 00:44:43,350 It got said licensing, and it has a certain advantage. 838 00:44:43,350 --> 00:44:47,160 To my knowledge, I don't think anybody else has been there. 839 00:44:47,160 --> 00:44:48,910 So it gives you some of the smattering. 840 00:44:48,910 --> 00:44:51,667 I'm going to pause, Romain, to see if there's any questions. 841 00:44:56,337 --> 00:44:58,130 Are we good? 842 00:44:58,130 --> 00:45:00,170 TEACHING ASSISTANT: I think we're good, Gary. 843 00:45:00,170 --> 00:45:01,530 GARY GENSLER: Health care-- 844 00:45:01,530 --> 00:45:04,920 health-care fintech. 845 00:45:04,920 --> 00:45:06,570 I'd still call it insurtech. 846 00:45:06,570 --> 00:45:09,180 Some people can call it healthtech. 847 00:45:09,180 --> 00:45:12,360 This is the insurance side and how technology 848 00:45:12,360 --> 00:45:13,840 is coming in to disrupt. 849 00:45:13,840 --> 00:45:18,595 Again, you could be in any piece of this. 850 00:45:18,595 --> 00:45:22,320 I borrowed this chart FT partners, 851 00:45:22,320 --> 00:45:26,200 but you could be in any piece of this 852 00:45:26,200 --> 00:45:29,222 and say I'm going to do the payments side. 853 00:45:29,222 --> 00:45:30,555 I'm going to do the claims side. 854 00:45:30,555 --> 00:45:33,420 I'm going to be in benefits management, 855 00:45:33,420 --> 00:45:37,170 but any piece of this. 856 00:45:37,170 --> 00:45:41,340 Again, most of these are in the US but not all, just some 857 00:45:41,340 --> 00:45:43,520 of what we're seeing. 858 00:45:43,520 --> 00:45:46,400 American Well is about telehealth, a very important 859 00:45:46,400 --> 00:45:49,310 thing now that we're in a period of time 860 00:45:49,310 --> 00:45:52,430 that we can't visit our doctors in person. 861 00:45:52,430 --> 00:45:53,960 Again, you'll see most-- 862 00:45:53,960 --> 00:45:55,400 not all of these-- 863 00:45:55,400 --> 00:45:58,150 are in the last 10 years. 864 00:45:58,150 --> 00:46:00,610 Clover, which is a unicorn-- 865 00:46:00,610 --> 00:46:02,470 almost all of these-- not all of these 866 00:46:02,470 --> 00:46:06,720 are unicorns, meaning worth over a billion dollars. 867 00:46:06,720 --> 00:46:10,750 Clover is about taking our analytics about how 868 00:46:10,750 --> 00:46:17,480 we live our lives and trying to underwrite and lower 869 00:46:17,480 --> 00:46:18,950 the cost to us. 870 00:46:18,950 --> 00:46:22,490 Probably the largest ones are Bright Health, 871 00:46:22,490 --> 00:46:27,320 which is selling Medicare Advantage in the US. 872 00:46:27,320 --> 00:46:31,130 On top of Medicare, which is government provided, 873 00:46:31,130 --> 00:46:34,220 you can have an augmented program on top 874 00:46:34,220 --> 00:46:36,980 called Medicare Advantage. 875 00:46:36,980 --> 00:46:40,880 Bright Health, Clover, Gusto. 876 00:46:40,880 --> 00:46:43,880 You'll see also there's a significant number of companies 877 00:46:43,880 --> 00:46:47,420 that have got into the benefit administration. 878 00:46:47,420 --> 00:46:49,850 This is working with small businesses 879 00:46:49,850 --> 00:46:52,220 to administer their benefits. 880 00:46:52,220 --> 00:46:56,090 Zenefits that was formed in 2013-- 881 00:46:56,090 --> 00:47:00,990 by 2015, we actually used Zenefits 882 00:47:00,990 --> 00:47:05,600 on an effort I was involved with as CFO of the Hillary Clinton 883 00:47:05,600 --> 00:47:06,290 campaign. 884 00:47:06,290 --> 00:47:08,390 It was a two-year-old startup, and we 885 00:47:08,390 --> 00:47:14,010 decided to go with benefits because we could outsource 886 00:47:14,010 --> 00:47:20,175 to this vendor our benefits administration for our startup. 887 00:47:22,970 --> 00:47:25,830 Now, that startup ultimately proved unsuccessful, 888 00:47:25,830 --> 00:47:26,490 as you know. 889 00:47:26,490 --> 00:47:30,510 But over the course of time, 5,000 employees, 890 00:47:30,510 --> 00:47:35,760 all that benefit administration, the health care and so forth, 891 00:47:35,760 --> 00:47:37,200 Zenefits. 892 00:47:37,200 --> 00:47:41,100 HealthEquity is in this field through health savings 893 00:47:41,100 --> 00:47:42,210 accounts. 894 00:47:42,210 --> 00:47:45,690 HealthEquity actually bought in the last two years 895 00:47:45,690 --> 00:47:49,920 a company called WageWorks that I was-- 896 00:47:49,920 --> 00:47:54,670 when WageWorks was a startup company, I went on their board 897 00:47:54,670 --> 00:47:59,000 and managed the hiring of a CFO and helped 898 00:47:59,000 --> 00:48:02,460 WageWorks prepare to go public as a private company 899 00:48:02,460 --> 00:48:04,520 as a fintech years ago. 900 00:48:04,520 --> 00:48:09,180 HealthEquity bought that company, WageWorks. 901 00:48:09,180 --> 00:48:11,950 Gusto is very significant in this field as well. 902 00:48:11,950 --> 00:48:16,200 So you have a sense, it's the marrying of finance and health. 903 00:48:16,200 --> 00:48:17,880 So it can be through the benefits 904 00:48:17,880 --> 00:48:21,630 and payroll-administration side, which is finance, 905 00:48:21,630 --> 00:48:24,540 or it can be directly marketplace comparisons like 906 00:48:24,540 --> 00:48:29,760 GoHealth, which is similar to PolicyBazaar in India-- 907 00:48:29,760 --> 00:48:32,115 all of these little subsectors and slices. 908 00:48:36,120 --> 00:48:40,020 Data related is sort of the last little slice. 909 00:48:40,020 --> 00:48:41,850 And this goes to the earlier question 910 00:48:41,850 --> 00:48:44,220 about Google and health care. 911 00:48:44,220 --> 00:48:46,500 While we haven't seen big tech there, 912 00:48:46,500 --> 00:48:49,770 I think that we're bound to because there's so many data 913 00:48:49,770 --> 00:48:51,300 analytic. 914 00:48:51,300 --> 00:48:54,030 Cambridge Mobile Telematics do not-- please 915 00:48:54,030 --> 00:48:58,110 don't confuse with Cambridge Analytica but Cambridge Mobile 916 00:48:58,110 --> 00:48:59,080 Telematics. 917 00:48:59,080 --> 00:49:02,580 You can see there are data aggregators that 918 00:49:02,580 --> 00:49:04,410 are collecting telematic data. 919 00:49:04,410 --> 00:49:06,800 They're not alone. 920 00:49:06,800 --> 00:49:11,300 And then with that data, commercializing it to insurance 921 00:49:11,300 --> 00:49:16,380 companies, big insurance companies as well as startups. 922 00:49:16,380 --> 00:49:20,510 StrongArm Tech, one of my favorites in this field-- 923 00:49:20,510 --> 00:49:24,620 they're selling a product that in factories 924 00:49:24,620 --> 00:49:29,600 if your factory workers will wear some wearable-- not 925 00:49:29,600 --> 00:49:30,290 a Fitbit. 926 00:49:30,290 --> 00:49:36,450 Some of it's like around the midchest range. 927 00:49:36,450 --> 00:49:40,160 But if they'll wear a sensor, you 928 00:49:40,160 --> 00:49:44,060 can lower the cost of workplace insurance. 929 00:49:44,060 --> 00:49:48,230 You can also, hopefully, make the workplace safer. 930 00:49:48,230 --> 00:49:52,040 So they're selling a safety device, injury protection, 931 00:49:52,040 --> 00:49:54,950 but it's about software and hardware 932 00:49:54,950 --> 00:49:57,830 because they're selling the wearables. 933 00:49:57,830 --> 00:50:00,110 And it's called StrongArm Tech. 934 00:50:00,110 --> 00:50:01,400 Will it be successful? 935 00:50:01,400 --> 00:50:03,830 It's been around nine years. 936 00:50:03,830 --> 00:50:07,490 So what you're seeing in insurance tech, which 937 00:50:07,490 --> 00:50:13,820 I think is so fascinating and potentially transformative, is 938 00:50:13,820 --> 00:50:18,870 a whole world of sensors and collecting alternative data, 939 00:50:18,870 --> 00:50:22,790 whether it's from our cars back to the telematics, whether it's 940 00:50:22,790 --> 00:50:30,440 from our watches or our wearables, our Fitbits, 941 00:50:30,440 --> 00:50:36,880 from our smartphones, from the sensors in our homes, all that 942 00:50:36,880 --> 00:50:39,310 can sort of collect data, a little bit of machine 943 00:50:39,310 --> 00:50:41,800 learning-- not always deep machine 944 00:50:41,800 --> 00:50:45,790 learning, deep learning, but some machine learning. 945 00:50:45,790 --> 00:50:51,670 Lower the costs, broaden the inclusion potentially. 946 00:50:51,670 --> 00:50:53,470 And then on the claims-management side 947 00:50:53,470 --> 00:50:57,590 as well, sensor technology, collecting the data, 948 00:50:57,590 --> 00:51:00,590 lowering the fraud, and the like. 949 00:51:00,590 --> 00:51:03,320 There's one firm that brags that they have the shortest 950 00:51:03,320 --> 00:51:09,440 claim-settlement period, that an online app was 951 00:51:09,440 --> 00:51:14,260 able to get claims settlement down to less than minutes. 952 00:51:14,260 --> 00:51:19,190 And I think there was one claim that was settled in seconds. 953 00:51:19,190 --> 00:51:20,910 It was an automobile accident. 954 00:51:20,910 --> 00:51:24,550 It was something they could do quite quickly technologically, 955 00:51:24,550 --> 00:51:27,807 but I also think that it was a bit of a marketing thing, 956 00:51:27,807 --> 00:51:29,640 that they wanted to say you could get claims 957 00:51:29,640 --> 00:51:32,730 management down to minutes. 958 00:51:32,730 --> 00:51:35,930 So that sort of is a little bit of review.