1 00:00:05,800 --> 00:00:08,720 Speaker 1: Welcome to the Bloomberg P and L Podcast. I'm pim Fox. 2 00:00:08,760 --> 00:00:11,520 Speaker 1: Along with my co host Lisa Abramowitz. Each day we 3 00:00:11,640 --> 00:00:15,120 Speaker 1: bring you the most important, noteworthy, and useful interviews for 4 00:00:15,200 --> 00:00:17,840 Speaker 1: you and your money, whether you're at the grocery store 5 00:00:17,960 --> 00:00:20,720 Speaker 1: or the trading floor. Find the Bloomberg P M L 6 00:00:20,840 --> 00:00:32,239 Speaker 1: Podcast on Apple Podcasts, SoundCloud, and Bloomberg dot com. Let 7 00:00:32,360 --> 00:00:36,560 Speaker 1: us turn our attentions to really damage that the hurricane 8 00:00:36,680 --> 00:00:41,880 Speaker 1: could cause. Brian Sullivan covers energy and commodities for Bloomberg 9 00:00:41,920 --> 00:00:46,400 Speaker 1: and all things weather related. Jack Caskey, Chemical Companies reporter 10 00:00:46,479 --> 00:00:50,120 Speaker 1: for US based in Houston. Let's start with with you. Brian. 11 00:00:50,240 --> 00:00:54,680 Speaker 1: I'm watching a minute by minute the latest forecasts for 12 00:00:54,760 --> 00:00:59,400 Speaker 1: the hurricane, and much will depend on exactly when it 13 00:00:59,440 --> 00:01:04,399 Speaker 1: turns north. The low pressure over the Mississippi Valley may 14 00:01:04,600 --> 00:01:08,399 Speaker 1: will pull it north, but the current forecast track is 15 00:01:08,440 --> 00:01:12,640 Speaker 1: about as bad a scenario as you could come up with. Yeah, 16 00:01:12,680 --> 00:01:15,360 Speaker 1: one analyst this morning told me it was the nightmare 17 00:01:15,400 --> 00:01:20,360 Speaker 1: scenario for insurance companies. UM. So, the current thinking is 18 00:01:20,400 --> 00:01:22,480 Speaker 1: it's going to go straight up the Florida Peninsula, whether 19 00:01:22,520 --> 00:01:23,840 Speaker 1: it goes up a little to the west or a 20 00:01:23,840 --> 00:01:25,600 Speaker 1: little to the East. I think at this point doesn't 21 00:01:25,640 --> 00:01:30,720 Speaker 1: matter just simply because um Irma is getting large. I mean, 22 00:01:30,880 --> 00:01:34,320 Speaker 1: it underwent what they call it eyewall replacement UM this 23 00:01:34,360 --> 00:01:36,760 Speaker 1: morning and that actually makes the physical size of the 24 00:01:36,800 --> 00:01:39,560 Speaker 1: storm get larger than the physical windfield of the most 25 00:01:39,640 --> 00:01:42,800 Speaker 1: damaging winds get larger. And this is this is where 26 00:01:42,800 --> 00:01:45,320 Speaker 1: the damage is going to happen UM, and it's going 27 00:01:45,360 --> 00:01:47,400 Speaker 1: to be different from Harvey. With Harvey, you had a 28 00:01:47,440 --> 00:01:50,120 Speaker 1: lot of flooding, which insurance companies aren't necessarily on the 29 00:01:50,160 --> 00:01:55,520 Speaker 1: hook for. But with um IRMA, you may see every 30 00:01:55,600 --> 00:01:58,400 Speaker 1: county in Florida having some kind of damage and these 31 00:01:58,440 --> 00:02:00,480 Speaker 1: are gonna this is gonna be roof damage and trees 32 00:02:00,520 --> 00:02:03,720 Speaker 1: through windows and houses and on cars, and UM the 33 00:02:03,760 --> 00:02:06,320 Speaker 1: insurance companies around the hook for this. So this UM 34 00:02:07,040 --> 00:02:09,240 Speaker 1: you're talking about a price tag maybe in a hundred 35 00:02:09,240 --> 00:02:12,959 Speaker 1: and thirty billion dollar range, and then with secondary economic 36 00:02:13,000 --> 00:02:15,519 Speaker 1: impact spreading that out to about two and of course 37 00:02:15,520 --> 00:02:20,440 Speaker 1: that's the worst case scenario. The latest forecast also suggesting 38 00:02:20,480 --> 00:02:23,400 Speaker 1: that the eyewall replacement cycle nearly complete, which means it 39 00:02:23,440 --> 00:02:26,280 Speaker 1: could go back up to a five uh as as 40 00:02:26,320 --> 00:02:29,160 Speaker 1: its strengthens. Jack Caskey, one of the stories that caught 41 00:02:29,160 --> 00:02:33,440 Speaker 1: everybody's attention obviously was uh the chemical plants in Houston 42 00:02:33,520 --> 00:02:37,200 Speaker 1: and the toxic stew that went into the floodwaters, and 43 00:02:37,200 --> 00:02:40,760 Speaker 1: of course the one plant that had the chemicals explode. 44 00:02:41,639 --> 00:02:44,640 Speaker 1: And the bad news from you is that this could 45 00:02:44,720 --> 00:02:48,520 Speaker 1: it could be worse in Florida. UM. Well, I mean 46 00:02:48,520 --> 00:02:51,920 Speaker 1: there's potentially, Yeah, there's Um there's not the heavy uh 47 00:02:52,080 --> 00:02:55,959 Speaker 1: petrochemical industry in Florida that there isn't in Texas and Louisiana. 48 00:02:56,600 --> 00:02:59,520 Speaker 1: But there is a nuclear plant that's from the seventh 49 00:02:59,520 --> 00:03:01,560 Speaker 1: early seven and the use that's right in the path 50 00:03:02,320 --> 00:03:06,960 Speaker 1: um at the tip of Florida. Um. Uh. Now there 51 00:03:06,960 --> 00:03:09,680 Speaker 1: the plant is, you know, twenty ft above sea level. 52 00:03:09,680 --> 00:03:12,440 Speaker 1: They tell me, so storms it should be stayed safe 53 00:03:12,480 --> 00:03:18,240 Speaker 1: from storm surge. Um. There's facilities. Um, there's a large 54 00:03:18,720 --> 00:03:24,560 Speaker 1: fast state mining facilities with slightly radioactive waste piles that 55 00:03:24,600 --> 00:03:28,720 Speaker 1: need to be protected from wind and and water uh 56 00:03:29,000 --> 00:03:32,680 Speaker 1: owned by the company mosaic Um that that polluted water 57 00:03:32,760 --> 00:03:37,320 Speaker 1: and killed marine mammals in the past. Um. And there's 58 00:03:37,440 --> 00:03:39,720 Speaker 1: you know, there's a whole in every state has got 59 00:03:39,800 --> 00:03:45,600 Speaker 1: its uh chemical uh distribution Uh facilities that need to 60 00:03:45,640 --> 00:03:47,760 Speaker 1: be protected from storm search. I mean one of the 61 00:03:47,800 --> 00:03:53,400 Speaker 1: lessons from UH UH from a party was that uh 62 00:03:53,640 --> 00:03:56,560 Speaker 1: some chemical operators just didn't expect the amount of water 63 00:03:56,920 --> 00:03:59,680 Speaker 1: that they were deluged with. So the case of Crosby 64 00:03:59,760 --> 00:04:03,320 Speaker 1: text is the company Arkhama out of France UH had 65 00:04:03,320 --> 00:04:06,920 Speaker 1: its generators fail, causing UM a fire of of the 66 00:04:07,280 --> 00:04:10,920 Speaker 1: organic peroxides that were stored there. Um. There was no 67 00:04:11,120 --> 00:04:15,240 Speaker 1: serious injuries, although actually the first responders are now suing 68 00:04:15,720 --> 00:04:19,919 Speaker 1: the company over their exposure to the smoke at the fire. 69 00:04:20,360 --> 00:04:23,480 Speaker 1: Let me, um, let let me ask you something here, UM, 70 00:04:23,680 --> 00:04:27,720 Speaker 1: nuclear plants shut down as the storm approaches. We're going 71 00:04:27,760 --> 00:04:31,760 Speaker 1: to have, according to all the meteorologists, storm search, big 72 00:04:31,800 --> 00:04:35,320 Speaker 1: storm search from this hurricane. And that's what happened hurts 73 00:04:35,680 --> 00:04:38,440 Speaker 1: the nuclear plants in Japan after the earthquake. So how 74 00:04:38,520 --> 00:04:41,839 Speaker 1: much danger is there from that? They say, it's not 75 00:04:41,920 --> 00:04:47,080 Speaker 1: a fair comparison that they that the that Fukushima. Um, 76 00:04:47,680 --> 00:04:51,880 Speaker 1: you know that meltdown was caused by a tsunami which 77 00:04:52,000 --> 00:04:55,000 Speaker 1: hit the plant rather quickly. They have a lot more 78 00:04:55,000 --> 00:04:57,520 Speaker 1: warning here to shut down this plant. Um, they've learned 79 00:04:57,520 --> 00:05:02,279 Speaker 1: from Fukushima. They have more flooding anti flooding mechanisms in place. 80 00:05:02,320 --> 00:05:05,880 Speaker 1: They've got backup systems upon backup systems to keep The 81 00:05:05,960 --> 00:05:09,200 Speaker 1: issue is keeping the cooling waters around the nuclear rods 82 00:05:09,400 --> 00:05:13,040 Speaker 1: UH from from getting too hot and molding down. They 83 00:05:13,040 --> 00:05:15,920 Speaker 1: say that's not going to happen. That plant is elevated 84 00:05:16,440 --> 00:05:19,560 Speaker 1: well above sea level, so you know we'll see the 85 00:05:19,960 --> 00:05:23,200 Speaker 1: Like I said, the lesson from from UH the Arkhama 86 00:05:23,200 --> 00:05:26,240 Speaker 1: plant explosion is that they just didn't expect that much 87 00:05:26,279 --> 00:05:29,520 Speaker 1: water and that's why the plant burned up. Brian Sullivan, 88 00:05:29,560 --> 00:05:34,120 Speaker 1: I got thirty seconds left. What's the best damage estimate 89 00:05:34,240 --> 00:05:37,200 Speaker 1: you're hearing right now from the companies you talked to 90 00:05:38,520 --> 00:05:44,120 Speaker 1: in the thirty five billion dollar range um, and that 91 00:05:44,320 --> 00:05:47,640 Speaker 1: isn't counting what happened in the Caribbean. There's already some 92 00:05:47,680 --> 00:05:49,560 Speaker 1: early estimates coming out of the Caribbean that you have 93 00:05:49,680 --> 00:05:52,440 Speaker 1: five billion dollars damage there, and that's certainly going to 94 00:05:52,520 --> 00:05:56,120 Speaker 1: go up, all right. Brian Sullivan coming to us from 95 00:05:56,120 --> 00:05:59,680 Speaker 1: our Boston bureau. He is the weather guy for US 96 00:05:59,800 --> 00:06:03,680 Speaker 1: and covers energy and commodities. Jack Caskey in Houston Chemical 97 00:06:03,720 --> 00:06:10,240 Speaker 1: Companies reporter, Obviously, this is UH a potentially catastrophic storm. 98 00:06:10,480 --> 00:06:14,119 Speaker 1: We don't have a lot of good information about uh, 99 00:06:14,160 --> 00:06:16,680 Speaker 1: exactly what's gonna happen. But we have some guesses and 100 00:06:16,720 --> 00:06:19,680 Speaker 1: they are not good. So stay tuned to Bluemberg RADI 101 00:06:19,720 --> 00:06:22,239 Speaker 1: all weekend. We'll keep you on top of the latest 102 00:06:22,320 --> 00:06:38,640 Speaker 1: from Hurricane Ran. Here's an idea. Uh. You want to 103 00:06:38,720 --> 00:06:40,719 Speaker 1: get a car, but you don't want to have a car. 104 00:06:41,480 --> 00:06:43,960 Speaker 1: You just want to use it when you want it, Uh, 105 00:06:44,200 --> 00:06:46,680 Speaker 1: for as long as you want, but and drive the 106 00:06:46,680 --> 00:06:51,279 Speaker 1: car you want. Uh. Scott Painter is the former CEO 107 00:06:51,360 --> 00:06:54,640 Speaker 1: and founder of True Car. He's got a new startup 108 00:06:55,000 --> 00:06:59,000 Speaker 1: car as a service. Uh. It's called Fair. Uh tell 109 00:06:59,080 --> 00:07:02,160 Speaker 1: us about how this works. I mean, I'm a guy 110 00:07:02,200 --> 00:07:03,920 Speaker 1: who lives in New York City. I don't own a car, 111 00:07:04,520 --> 00:07:07,279 Speaker 1: but I go skiing in the winter, or I go 112 00:07:07,440 --> 00:07:09,320 Speaker 1: out to the beach in the summer, and so maybe 113 00:07:09,360 --> 00:07:12,120 Speaker 1: for a while I want a car. Well. Fair is 114 00:07:12,120 --> 00:07:16,440 Speaker 1: a pretty simple concept. It really is an app that 115 00:07:16,520 --> 00:07:18,800 Speaker 1: represents a totally new way to get a car. So 116 00:07:18,920 --> 00:07:22,320 Speaker 1: you simply download the app, scan your driver's license, it 117 00:07:22,360 --> 00:07:25,000 Speaker 1: shows you all the cars you can afford, and then 118 00:07:27,120 --> 00:07:30,360 Speaker 1: and it gives you a single all in, low cost 119 00:07:30,440 --> 00:07:33,080 Speaker 1: of ownership that's designed to be sort of an iTunes 120 00:07:33,080 --> 00:07:37,080 Speaker 1: account for your car. But this isn't like renting, though 121 00:07:37,720 --> 00:07:39,640 Speaker 1: you know, it's all the benefits of renting. So one 122 00:07:39,640 --> 00:07:41,240 Speaker 1: of the great parts about renting a car is that 123 00:07:41,240 --> 00:07:42,440 Speaker 1: you can turn it in and be done with it 124 00:07:42,520 --> 00:07:45,760 Speaker 1: anytime you want. The problem is it's quite expensive. So 125 00:07:45,800 --> 00:07:47,880 Speaker 1: this is about half the cost of a rental, which 126 00:07:47,920 --> 00:07:51,000 Speaker 1: is important. But at the savings of a lease. We 127 00:07:51,080 --> 00:07:53,400 Speaker 1: are actually the owner of the car and we actually 128 00:07:53,480 --> 00:07:56,280 Speaker 1: leased the vehicle to you, all simultaneously inside the app. 129 00:07:56,320 --> 00:08:00,160 Speaker 1: But more importantly, you're not picking a car all of 130 00:08:00,160 --> 00:08:03,640 Speaker 1: a rental lot. You're picking the car you want that's 131 00:08:03,680 --> 00:08:08,360 Speaker 1: for sale at any dealer. What's the minimum and maximum 132 00:08:08,360 --> 00:08:11,400 Speaker 1: times for your what do you call? You? Don do 133 00:08:11,440 --> 00:08:14,800 Speaker 1: you call it at least what? It's effectively a used 134 00:08:14,880 --> 00:08:18,400 Speaker 1: vehicle lease, but we're not calling it at lease. Um. Actually, 135 00:08:18,480 --> 00:08:21,600 Speaker 1: the fair contract has no term in it, which is 136 00:08:21,640 --> 00:08:24,800 Speaker 1: really one of the things we found that young buyers, millennials, 137 00:08:24,840 --> 00:08:27,960 Speaker 1: the modern sort of car buyer really doesn't want a 138 00:08:27,960 --> 00:08:30,600 Speaker 1: long contract or a long commitment. A car represents a 139 00:08:30,640 --> 00:08:33,800 Speaker 1: big commitment. So the fair contract gives you the ability 140 00:08:33,840 --> 00:08:35,959 Speaker 1: to walk away or turn in the car at any 141 00:08:36,000 --> 00:08:38,319 Speaker 1: time without penalty. You can keep it for six days, 142 00:08:38,720 --> 00:08:42,080 Speaker 1: six months, six years. Where do you get the cars? 143 00:08:42,679 --> 00:08:45,760 Speaker 1: So you pick the car using the app, But those 144 00:08:45,760 --> 00:08:49,400 Speaker 1: cars are at at local dealers all around you, and 145 00:08:49,440 --> 00:08:51,760 Speaker 1: those are the cars that are traditionally for sales. Fair 146 00:08:51,840 --> 00:08:54,600 Speaker 1: is simply a different way to get that car. So 147 00:08:54,679 --> 00:08:56,679 Speaker 1: you look at their inventory and you can say I 148 00:08:57,520 --> 00:09:01,360 Speaker 1: want a Ford X whatever. Um, you go to a 149 00:09:01,400 --> 00:09:04,640 Speaker 1: local Ford dealer and what they have an inventory is 150 00:09:04,640 --> 00:09:07,120 Speaker 1: what you can get that's correct. And instead of the 151 00:09:07,160 --> 00:09:09,960 Speaker 1: prices of the car being what you're shopping on, you're 152 00:09:09,960 --> 00:09:12,840 Speaker 1: looking at monthly payments. So everything in the inventory that 153 00:09:12,880 --> 00:09:16,000 Speaker 1: you see is giving to you with a monthly payment 154 00:09:16,080 --> 00:09:22,280 Speaker 1: that includes maintenance, warranty and roadside assistance standard. That that 155 00:09:22,320 --> 00:09:23,760 Speaker 1: brings up the question. You said you could get it 156 00:09:23,800 --> 00:09:27,280 Speaker 1: as long as six days. You know six days? Do 157 00:09:27,280 --> 00:09:30,960 Speaker 1: you pro rent the monthly payment debt? We don't, So 158 00:09:31,000 --> 00:09:32,800 Speaker 1: it is a month to month pay as you go. 159 00:09:33,280 --> 00:09:34,719 Speaker 1: So if I have it for six days, I'm still 160 00:09:34,720 --> 00:09:37,520 Speaker 1: gonna pay for a month. You are, Okay, that sounds 161 00:09:37,520 --> 00:09:41,560 Speaker 1: sound too bad though, Um, and uh, who you I 162 00:09:41,559 --> 00:09:44,239 Speaker 1: assume with this you've got to carry your own insurance 163 00:09:44,600 --> 00:09:46,600 Speaker 1: for the car in order to drive a car on 164 00:09:46,640 --> 00:09:48,679 Speaker 1: the road, you do need to have insurance, but Fair 165 00:09:48,760 --> 00:09:51,880 Speaker 1: also offers a month to month insurance for collision and liability, 166 00:09:51,960 --> 00:09:54,200 Speaker 1: and you can also buy excess wear and tear insurance 167 00:09:54,240 --> 00:09:57,080 Speaker 1: if you feel you need it. Um, and what do 168 00:09:57,120 --> 00:09:58,680 Speaker 1: you do with cars when you When I give it 169 00:09:58,679 --> 00:10:01,280 Speaker 1: back to you, those cars go back to the dealership. 170 00:10:01,559 --> 00:10:04,679 Speaker 1: So the lifeblood of the modern car dealership is a 171 00:10:04,760 --> 00:10:07,040 Speaker 1: late model used car. So that car coming back into 172 00:10:07,080 --> 00:10:09,560 Speaker 1: their inventory and being sold to another customer is really 173 00:10:09,559 --> 00:10:14,200 Speaker 1: the goal. Now I've been reading that, Um, there is 174 00:10:14,240 --> 00:10:17,439 Speaker 1: a glut of late model used cars coming out of 175 00:10:17,440 --> 00:10:20,960 Speaker 1: the markets these days, but we've been at peak production 176 00:10:21,160 --> 00:10:24,600 Speaker 1: in terms of manufacturing. We're approaching eighteen million cars being 177 00:10:24,600 --> 00:10:27,680 Speaker 1: produced in this country, which creates an over supply of 178 00:10:27,840 --> 00:10:30,760 Speaker 1: used cars to three years down the road. And we've 179 00:10:30,800 --> 00:10:32,800 Speaker 1: been at peak production for a couple of years now. 180 00:10:32,840 --> 00:10:36,320 Speaker 1: So you've got five or six million late model used 181 00:10:36,320 --> 00:10:38,480 Speaker 1: cars that are one, two and three years old coming 182 00:10:38,559 --> 00:10:41,000 Speaker 1: back to the market in the next year to eighteen months. 183 00:10:41,760 --> 00:10:44,640 Speaker 1: That represents a huge opportunity for the consumer to not 184 00:10:44,679 --> 00:10:46,520 Speaker 1: only save money, but get a great value in a 185 00:10:46,520 --> 00:10:50,640 Speaker 1: great vehicle. So you're are you are you selling renting 186 00:10:50,760 --> 00:10:53,400 Speaker 1: to me UH used cars or new cars. These are 187 00:10:53,400 --> 00:10:56,920 Speaker 1: pre owned cars. And all of the vehicles that you're 188 00:10:56,920 --> 00:10:59,600 Speaker 1: going to see on fair represent a vehicle that has 189 00:10:59,600 --> 00:11:02,880 Speaker 1: gone through our inspection process, that has a warranty, roadside 190 00:11:02,880 --> 00:11:05,280 Speaker 1: assistance and standard maintenance, so you can actually get the 191 00:11:05,280 --> 00:11:07,079 Speaker 1: car with peace of mind. So these are going to 192 00:11:07,200 --> 00:11:10,440 Speaker 1: be UH. This is gonna be useful for the dealer 193 00:11:10,440 --> 00:11:13,320 Speaker 1: who can make some money off a vehicle rather than 194 00:11:13,320 --> 00:11:16,160 Speaker 1: have it just sitting inventory. That's correct for the dealer. 195 00:11:16,200 --> 00:11:19,560 Speaker 1: This is a transaction. They're selling the car, you're buying it, 196 00:11:19,640 --> 00:11:22,960 Speaker 1: so they don't care who's buying right fair Fair operates 197 00:11:23,000 --> 00:11:26,280 Speaker 1: under all the necessary licenses and depending on the jurisdiction, 198 00:11:26,320 --> 00:11:29,319 Speaker 1: we are a dealer and we actually buy the car, 199 00:11:29,720 --> 00:11:32,319 Speaker 1: put it on our balance sheet, and simultaneously give you 200 00:11:32,360 --> 00:11:34,920 Speaker 1: a contract to use that vehicle. What's the difference between 201 00:11:34,920 --> 00:11:38,120 Speaker 1: you and and Tesla in in your sales model? In 202 00:11:38,200 --> 00:11:41,040 Speaker 1: that UH dealers have gone to court saying if you 203 00:11:41,040 --> 00:11:43,160 Speaker 1: don't have a lot and you don't have cars on 204 00:11:43,200 --> 00:11:45,320 Speaker 1: that lot, you're not a dealer and you can't be 205 00:11:45,400 --> 00:11:48,199 Speaker 1: selling this stuff. We're not actually selling the car, We're 206 00:11:48,240 --> 00:11:50,280 Speaker 1: just giving you access to the vehicle to use it 207 00:11:50,320 --> 00:11:52,360 Speaker 1: as you want, when you want, but we take all 208 00:11:52,360 --> 00:11:55,839 Speaker 1: of the risk around depreciation and managing and owning that vehicle. 209 00:11:55,880 --> 00:11:58,680 Speaker 1: So it's our capital that's being used to buy the car. 210 00:11:59,000 --> 00:12:02,040 Speaker 1: You're not borrowing money when you get a car from Fair, 211 00:12:02,280 --> 00:12:04,760 Speaker 1: You're paying for access to that vehicle. And so this 212 00:12:04,800 --> 00:12:07,360 Speaker 1: notion that the car is becoming a service is really 213 00:12:07,400 --> 00:12:10,040 Speaker 1: a subscription. That you have the car you want at 214 00:12:10,120 --> 00:12:12,760 Speaker 1: any dealership as long as you want it, when you 215 00:12:12,800 --> 00:12:15,160 Speaker 1: want it, and then you can leave it and be 216 00:12:15,240 --> 00:12:17,240 Speaker 1: done with it. So I don't take title to it. 217 00:12:17,440 --> 00:12:21,720 Speaker 1: You don't. Uh. If I have an app and I 218 00:12:21,800 --> 00:12:25,520 Speaker 1: decide I want to get a ferrari Um, how do 219 00:12:25,559 --> 00:12:29,840 Speaker 1: you know I am? I am credit worthy for a 220 00:12:29,880 --> 00:12:31,920 Speaker 1: car like that? How do you how do you know 221 00:12:32,760 --> 00:12:36,480 Speaker 1: whether you can trust somebody to make their payments? The 222 00:12:36,520 --> 00:12:38,600 Speaker 1: goal of the app is to really understand what you 223 00:12:38,640 --> 00:12:42,520 Speaker 1: can afford. The average American today spends about four of 224 00:12:42,559 --> 00:12:48,160 Speaker 1: their gross income on mobility, which breaks down to the car, insurance, maintenance, repair, fuel, 225 00:12:48,520 --> 00:12:51,760 Speaker 1: and in many cases accessories. We're actually beginning there, and 226 00:12:51,880 --> 00:12:54,440 Speaker 1: the first thing that the app does by scanning your 227 00:12:54,480 --> 00:12:57,720 Speaker 1: driver's license, we're looking for your income and your disposable income, 228 00:12:57,760 --> 00:13:00,760 Speaker 1: so we can understand what you can genuinely afford. But 229 00:13:00,840 --> 00:13:03,680 Speaker 1: the two things we need the driver's license and a 230 00:13:03,760 --> 00:13:05,959 Speaker 1: form of digital payment, and in most cases that's the 231 00:13:06,000 --> 00:13:09,160 Speaker 1: bank account or a credit card. So, uh, do you 232 00:13:09,240 --> 00:13:12,160 Speaker 1: care about whether my credit is good? I mean, do 233 00:13:12,200 --> 00:13:16,240 Speaker 1: you run a credit check on me through say an Equifax? 234 00:13:16,600 --> 00:13:19,120 Speaker 1: Maybe you don't want to do that, right right, Well, 235 00:13:19,160 --> 00:13:21,120 Speaker 1: we do do a soft credit bull but it does 236 00:13:21,160 --> 00:13:24,200 Speaker 1: not affect your credit, and we're not underwriting you in 237 00:13:24,200 --> 00:13:25,720 Speaker 1: the same way that a bank would have for a 238 00:13:25,720 --> 00:13:29,800 Speaker 1: car loan. So you can have actually bad credit. You can, 239 00:13:29,800 --> 00:13:32,120 Speaker 1: in fit many cases, have no credit. What we're we're 240 00:13:32,160 --> 00:13:35,560 Speaker 1: looking really looking for is ability to pay. However, if 241 00:13:35,600 --> 00:13:37,720 Speaker 1: for whatever reason, you go through a change in work 242 00:13:37,760 --> 00:13:40,840 Speaker 1: where you don't have a consistent source of income, you 243 00:13:40,920 --> 00:13:44,520 Speaker 1: simply return the car. Where does this? Uh? When do 244 00:13:44,559 --> 00:13:46,640 Speaker 1: we see this? When? When can I start downloading the 245 00:13:46,640 --> 00:13:48,920 Speaker 1: app and getting the car? So the app is live 246 00:13:48,960 --> 00:13:50,720 Speaker 1: today in the app Store if you go to fair 247 00:13:50,760 --> 00:13:55,840 Speaker 1: and type in either auto or financial financial. So Fair 248 00:13:56,480 --> 00:13:58,400 Speaker 1: is an app that you can download today. But it 249 00:13:58,480 --> 00:14:01,520 Speaker 1: is currently live and working in southern California. We've got 250 00:14:01,520 --> 00:14:04,599 Speaker 1: seventy three franchise dealers over a thousand cars in inventory 251 00:14:04,800 --> 00:14:08,280 Speaker 1: because nobody drives in southern California. It's the car capital. Well, 252 00:14:08,880 --> 00:14:11,800 Speaker 1: I mean yeah, but I'm kidding. But people they're like 253 00:14:11,880 --> 00:14:14,520 Speaker 1: to have cars. What about in a place when does 254 00:14:14,559 --> 00:14:17,120 Speaker 1: it come to New York City or Boston, places where 255 00:14:17,120 --> 00:14:20,200 Speaker 1: people don't necessarily want a to wear house a car, 256 00:14:20,320 --> 00:14:22,440 Speaker 1: but you know, want cars on a regular basis. Sure, 257 00:14:22,520 --> 00:14:25,240 Speaker 1: So it's going to be throughout the state of California 258 00:14:25,280 --> 00:14:26,840 Speaker 1: before the end of the year, and we'll roll out 259 00:14:26,920 --> 00:14:29,680 Speaker 1: nationally in about a dozen cities in two thousand and eighteen. 260 00:14:30,480 --> 00:14:32,880 Speaker 1: But from an ownership point of view, the benefits of 261 00:14:32,880 --> 00:14:37,960 Speaker 1: ownership are really focused on security. Well, we'll talk about that, 262 00:14:38,120 --> 00:14:40,520 Speaker 1: but fair is the is the app. Thank you very 263 00:14:40,600 --> 00:14:43,800 Speaker 1: much for joining us. We're time, unfortunately, but it sounds 264 00:14:43,800 --> 00:14:59,760 Speaker 1: like a great idea Scott paid fair Well. News out 265 00:14:59,760 --> 00:15:04,200 Speaker 1: of Dada today Canadian marijuana producers shares are rising. Reports 266 00:15:04,280 --> 00:15:07,840 Speaker 1: Ontario is going to open government controlled storefronts for legalized 267 00:15:07,960 --> 00:15:13,320 Speaker 1: recreational sales. Uh. That should make it a lot easier 268 00:15:13,440 --> 00:15:18,080 Speaker 1: for people once Canada legalizes marijuana next year to purchase 269 00:15:18,800 --> 00:15:23,600 Speaker 1: the product. Because the Canadian government probably recognizing it needs 270 00:15:23,640 --> 00:15:29,800 Speaker 1: a way to safely handle the financial transactions. Created may 271 00:15:30,040 --> 00:15:33,800 Speaker 1: make it easier for people in Canada to um use 272 00:15:33,840 --> 00:15:36,440 Speaker 1: banks and the banking system to pay for their pot. 273 00:15:36,800 --> 00:15:40,120 Speaker 1: That is not the case in the United States, especially 274 00:15:40,120 --> 00:15:43,680 Speaker 1: in the states where we have seen legalization, like in Colorado, 275 00:15:44,080 --> 00:15:49,000 Speaker 1: banks don't really want to deal with people who do business, 276 00:15:49,000 --> 00:15:52,480 Speaker 1: even though those businesses can take in an awful lot 277 00:15:52,480 --> 00:15:58,440 Speaker 1: of cash. Banks worry about money laundering bills, UH, legislation rather, 278 00:15:58,720 --> 00:16:02,480 Speaker 1: and they also it sounds silly, but it's true. UM, 279 00:16:02,800 --> 00:16:05,400 Speaker 1: they don't want a lot of cash coming into the 280 00:16:05,400 --> 00:16:10,120 Speaker 1: bank that smells like marijuana. Company that has been founded 281 00:16:10,120 --> 00:16:13,520 Speaker 1: to try to deal with that is called Token and 282 00:16:13,960 --> 00:16:16,280 Speaker 1: the CEO and founder is Lea means Aarad and he 283 00:16:16,400 --> 00:16:21,080 Speaker 1: joins us now from Denver. Uh. Basically, what you're doing 284 00:16:21,360 --> 00:16:26,280 Speaker 1: is using the concept of a cryptocurrency to become the 285 00:16:26,320 --> 00:16:32,400 Speaker 1: middleman between the cash and the pot shop. Correct. Well, 286 00:16:32,400 --> 00:16:35,200 Speaker 1: thanks for having me, Mike. UM. To clarify, we use 287 00:16:35,240 --> 00:16:40,480 Speaker 1: the blockchain that's divorced from its cryptocurrency. The blockchain the 288 00:16:40,480 --> 00:16:44,240 Speaker 1: way we employ it is a secondary redundancy ledger for us, 289 00:16:44,440 --> 00:16:47,320 Speaker 1: and it ensures indelibility of all data in our system 290 00:16:47,720 --> 00:16:50,680 Speaker 1: and as a consequence, makes far financial institution partners and 291 00:16:50,960 --> 00:16:54,280 Speaker 1: by extension, the regulators feel comfortable with extending services to 292 00:16:54,360 --> 00:16:58,200 Speaker 1: the industry. How does it work? Yeah? Absolutely. Before I 293 00:16:58,240 --> 00:17:00,360 Speaker 1: tell you how it works, uh, I think it's important 294 00:17:00,360 --> 00:17:02,560 Speaker 1: for me to explain the context and UH in kind 295 00:17:02,560 --> 00:17:05,240 Speaker 1: of my own personal background in the genesis of Token, 296 00:17:05,560 --> 00:17:08,040 Speaker 1: I'm an immigrant came to the United States from former 297 00:17:08,160 --> 00:17:12,280 Speaker 1: USS are and where my family spent uh potentially approximately 298 00:17:12,320 --> 00:17:15,399 Speaker 1: six years as refugees, which impacted my view on banking 299 00:17:15,480 --> 00:17:18,480 Speaker 1: and unbanked. So that's very personal to me. Expending services 300 00:17:18,520 --> 00:17:21,440 Speaker 1: to what to those who who deserve it? Uh. I 301 00:17:21,560 --> 00:17:23,760 Speaker 1: later spend time in the military and intelligence community in 302 00:17:23,760 --> 00:17:26,200 Speaker 1: the United States, and then Wall Street will Mary Will 303 00:17:26,200 --> 00:17:28,920 Speaker 1: Mary Lynch, and later with US Treasuries off to the 304 00:17:28,960 --> 00:17:32,520 Speaker 1: control occurrency as a bank regulator. So I I you know, 305 00:17:32,600 --> 00:17:35,280 Speaker 1: Token is an amalgam of all those experiences, and what 306 00:17:35,400 --> 00:17:39,600 Speaker 1: Token is essentially is as an intelligence platform. It creates 307 00:17:39,600 --> 00:17:43,200 Speaker 1: accountability transparency. Uh. The way it works, consumers download the 308 00:17:43,200 --> 00:17:46,760 Speaker 1: application to create an account. Under a second we identify 309 00:17:46,880 --> 00:17:48,600 Speaker 1: who they are, so if it's John Smith, we know 310 00:17:48,640 --> 00:17:50,640 Speaker 1: that it's John Smith before they tie a payment. Source 311 00:17:50,640 --> 00:17:53,840 Speaker 1: to that application, the mobile app, but the experience is 312 00:17:53,920 --> 00:17:57,560 Speaker 1: somewhats your Apple Pay for instance, they're walking a participating retailer, 313 00:17:57,640 --> 00:18:00,560 Speaker 1: press pay and walk away with the product. It's as simple. 314 00:18:00,920 --> 00:18:04,040 Speaker 1: On the other hand, retailers have access to their portal 315 00:18:04,080 --> 00:18:06,239 Speaker 1: that looks just like an online banking portal. They can 316 00:18:06,240 --> 00:18:09,600 Speaker 1: see a transaction as it happens. They can also move 317 00:18:09,640 --> 00:18:11,480 Speaker 1: the funds now they can move the funds token to 318 00:18:11,560 --> 00:18:14,920 Speaker 1: token thinks PayPal, or they can move the funds using 319 00:18:14,920 --> 00:18:17,919 Speaker 1: a cate or traditional payments rails that sort of provided 320 00:18:17,920 --> 00:18:21,119 Speaker 1: to them through our partner institutions. So that solves both 321 00:18:21,200 --> 00:18:24,080 Speaker 1: the banking problem and the payments problem on both ends. 322 00:18:24,600 --> 00:18:27,080 Speaker 1: And then of course there are regulators and law enforcement 323 00:18:27,320 --> 00:18:29,639 Speaker 1: UH and UH and as you mentioned, in the United States, 324 00:18:29,800 --> 00:18:33,359 Speaker 1: we have a very interesting, if not convoluted system where 325 00:18:33,520 --> 00:18:37,199 Speaker 1: cannabis is illegal federally and legal and certain jurisdictions. So 326 00:18:37,240 --> 00:18:40,359 Speaker 1: as a consequence of federal government took a unique stance 327 00:18:40,480 --> 00:18:44,560 Speaker 1: that took an agnostic stance through issuing several guidances. There 328 00:18:44,560 --> 00:18:48,080 Speaker 1: are three memorandums from Department of Justice. There's a guidance 329 00:18:48,080 --> 00:18:51,359 Speaker 1: from a Financial Crimes Enforcement network part of yours Treasury 330 00:18:51,400 --> 00:18:54,040 Speaker 1: and and essentially there's a legal framework called the Bank 331 00:18:54,160 --> 00:18:57,240 Speaker 1: Secrecy Act and the federal governments. If you took a 332 00:18:57,240 --> 00:19:00,119 Speaker 1: position by stating that we are agnostic as long as 333 00:19:00,119 --> 00:19:02,920 Speaker 1: you follow the rules and comply with these items and 334 00:19:02,960 --> 00:19:06,520 Speaker 1: these regulatory guidance, UH, there was there was issue. And 335 00:19:06,600 --> 00:19:09,760 Speaker 1: what what happens that it creates uh an incredible burden 336 00:19:09,800 --> 00:19:12,840 Speaker 1: on small financial institutions. They're willing to provide services to 337 00:19:12,840 --> 00:19:15,280 Speaker 1: cannabis industry, and that's what we help them with. We 338 00:19:15,440 --> 00:19:20,040 Speaker 1: help them with creating transparency, tracking every transaction and and 339 00:19:20,200 --> 00:19:23,679 Speaker 1: ensuring that everything that happens in that system is indelible. 340 00:19:23,880 --> 00:19:26,440 Speaker 1: And that's where blockchain FETs and blockchain is the first 341 00:19:26,520 --> 00:19:29,879 Speaker 1: ledger UH known to UH humanity so to speak. But 342 00:19:29,960 --> 00:19:33,280 Speaker 1: it's cryptographically and mathematically indelible. What's your kind of this? 343 00:19:33,359 --> 00:19:36,840 Speaker 1: How do you make money? Right now? It's very simple. 344 00:19:36,840 --> 00:19:39,560 Speaker 1: We charge per transaction fees to merchants the same way 345 00:19:39,720 --> 00:19:44,720 Speaker 1: UH say First Data would or Navisa master cards. And 346 00:19:44,760 --> 00:19:47,159 Speaker 1: how has it been accepted so far? What's what's your 347 00:19:47,200 --> 00:19:51,080 Speaker 1: growth rate? Yeah? So we we had a control pilot 348 00:19:51,119 --> 00:19:54,080 Speaker 1: in Denver and it was done on purpose with with 349 00:19:54,160 --> 00:19:57,200 Speaker 1: cannabis and non cannabis relationships, to to establish a control 350 00:19:57,240 --> 00:20:01,359 Speaker 1: group and to really observe behaviors and the industry. Uh. 351 00:20:01,440 --> 00:20:05,840 Speaker 1: In that pilot, Uh, surprisingly, our wallet had incredible adoption. 352 00:20:05,920 --> 00:20:09,320 Speaker 1: Thirty five percent of old transactions at each locations were 353 00:20:09,359 --> 00:20:13,520 Speaker 1: converted without any marketing. That's uh, that's some precedented because 354 00:20:13,800 --> 00:20:16,879 Speaker 1: Apple failed and Google failed with their wallets. But I 355 00:20:16,920 --> 00:20:20,199 Speaker 1: think it's demonstrative to the need in the industry and 356 00:20:20,200 --> 00:20:22,720 Speaker 1: to the fact that consumers don't want to pay in cash. Uh. 357 00:20:22,720 --> 00:20:25,800 Speaker 1: There's some really interesting statistics. For instance, over over six 358 00:20:26,280 --> 00:20:29,880 Speaker 1: of our users in our system made three or more purchases, 359 00:20:29,920 --> 00:20:33,200 Speaker 1: So it's sticky and pun intended here and uh, and 360 00:20:33,240 --> 00:20:36,280 Speaker 1: we've increased average ticket sized by eighteen percent. I mean, 361 00:20:36,280 --> 00:20:39,199 Speaker 1: it's it's incredible value add to a business. What do 362 00:20:39,240 --> 00:20:43,719 Speaker 1: you think the growth possibilities for you are? You know, 363 00:20:43,840 --> 00:20:47,520 Speaker 1: you've got states that are doing medical marijuana, but the 364 00:20:47,560 --> 00:20:51,119 Speaker 1: states have just totally legalized it. Uh, there aren't that many, 365 00:20:51,359 --> 00:20:54,560 Speaker 1: Um do you have? Do you do you need to 366 00:20:54,640 --> 00:20:58,400 Speaker 1: grow or can you maintain a successful business with just 367 00:20:58,960 --> 00:21:04,359 Speaker 1: a smaller operation that focuses on those states. Now, we 368 00:21:04,400 --> 00:21:06,840 Speaker 1: absolutely not only want to grow, but need to grow. 369 00:21:07,240 --> 00:21:10,959 Speaker 1: And the reason is the bigger our ecosystem. More visibility 370 00:21:10,960 --> 00:21:14,280 Speaker 1: we have into the familio of the transaction world. Right, 371 00:21:14,600 --> 00:21:17,360 Speaker 1: so we know, for instance, if you engage in what's 372 00:21:17,359 --> 00:21:19,360 Speaker 1: called dispensary hopping. If you're a consumer and you want 373 00:21:19,359 --> 00:21:23,080 Speaker 1: to make a purchase and multiple dispensaries that an exceeding 374 00:21:23,119 --> 00:21:25,160 Speaker 1: your daily amounts, that's a lot of to you. By 375 00:21:25,200 --> 00:21:28,280 Speaker 1: stay called out a law. For us to have that visibility, 376 00:21:28,280 --> 00:21:31,879 Speaker 1: we need to have a multiple dispensaries, a multiple retail locations. 377 00:21:32,200 --> 00:21:34,480 Speaker 1: We also need to have access to the entire vertical 378 00:21:34,520 --> 00:21:37,560 Speaker 1: to streamline and experience. So if you are a retail 379 00:21:37,600 --> 00:21:39,840 Speaker 1: facility and you want to purchase from a cultivator or 380 00:21:40,000 --> 00:21:43,200 Speaker 1: a manufacturer, if there are on a system, then onboarded. 381 00:21:43,440 --> 00:21:45,560 Speaker 1: Not only do they have access to banking at this point, 382 00:21:45,600 --> 00:21:48,520 Speaker 1: but they're also you can also transact and incredibly low 383 00:21:48,680 --> 00:21:51,480 Speaker 1: rates in our system token to token so to speak. 384 00:21:51,800 --> 00:21:54,840 Speaker 1: So yes, we are growing. We're going to be growing nationally, 385 00:21:56,040 --> 00:21:58,280 Speaker 1: so you know, keep an eye on it out. That 386 00:21:58,359 --> 00:22:00,320 Speaker 1: means thank you very much to see you know of 387 00:22:00,880 --> 00:22:05,840 Speaker 1: token an interesting new business, uh combining the marijuana industry 388 00:22:05,960 --> 00:22:22,320 Speaker 1: and technology. Well, a big story in financial world that 389 00:22:23,040 --> 00:22:26,240 Speaker 1: is getting a lot of attention, even with the hurricane coming. 390 00:22:26,359 --> 00:22:30,000 Speaker 1: Is this security breach at Equifax? A lot of people 391 00:22:30,080 --> 00:22:34,560 Speaker 1: wondering how the heck this could have happened. Am and 392 00:22:34,600 --> 00:22:37,800 Speaker 1: Barlav is the president of Checkpoints Software. He's based in 393 00:22:37,840 --> 00:22:42,320 Speaker 1: Tel Aviv. Michael Riley is Bloomberg News cybersecurity reporter in Washington, 394 00:22:42,400 --> 00:22:47,640 Speaker 1: d C. They're joining us now and uh, Michael, Uh, 395 00:22:47,800 --> 00:22:52,680 Speaker 1: is there anything new that we that hasn't been reported yet? 396 00:22:52,680 --> 00:22:55,120 Speaker 1: And I asked that question sort of not and it's 397 00:22:55,160 --> 00:22:58,280 Speaker 1: not really tongue in cheek, But they sat on this 398 00:22:58,400 --> 00:23:01,520 Speaker 1: information as many companies do for a while, and there's 399 00:23:01,560 --> 00:23:04,520 Speaker 1: this story about the executive selling stock in between and everything. 400 00:23:04,560 --> 00:23:06,879 Speaker 1: But um, do we know everything we need to know 401 00:23:06,920 --> 00:23:09,160 Speaker 1: about this at this point? Uh? No. In fact, this 402 00:23:09,240 --> 00:23:11,720 Speaker 1: is going to be the start of a very long process. 403 00:23:11,760 --> 00:23:14,480 Speaker 1: If you take other examples like this, say Target, for example, 404 00:23:14,560 --> 00:23:19,160 Speaker 1: where consumers got really angry because of a set of 405 00:23:19,160 --> 00:23:22,760 Speaker 1: of uh circumstances. In this case, you have a consumer 406 00:23:22,800 --> 00:23:25,520 Speaker 1: credit agency, which consumers don't tend to like to begin with, 407 00:23:25,600 --> 00:23:27,639 Speaker 1: because they're the ones who tell them whether or not 408 00:23:27,720 --> 00:23:29,240 Speaker 1: their credit it's good, and whether or not to get 409 00:23:29,240 --> 00:23:32,199 Speaker 1: a credit card or mortgage and then you add to 410 00:23:32,280 --> 00:23:35,800 Speaker 1: that what looks like a loss of massive amounts of 411 00:23:35,960 --> 00:23:38,840 Speaker 1: of consumer data for a company that's supposed to know 412 00:23:38,880 --> 00:23:41,119 Speaker 1: how to protect our data. And then you add to 413 00:23:41,160 --> 00:23:45,840 Speaker 1: that this uh, you know, potential branding problem where three 414 00:23:45,840 --> 00:23:50,800 Speaker 1: executives sell sell offs shares in an unscheduled way just 415 00:23:50,840 --> 00:23:54,120 Speaker 1: a few days after the breach is discovered. You're gonna 416 00:23:54,119 --> 00:23:55,600 Speaker 1: get a lot of anger. And with a lot of 417 00:23:55,600 --> 00:23:59,359 Speaker 1: consumer anger usually comes soil lawsuits. Those lawsuits will demand 418 00:23:59,359 --> 00:24:01,080 Speaker 1: a lot more in form nation that we have now, 419 00:24:01,400 --> 00:24:05,640 Speaker 1: including an exact timeline. Who was informed about the breach? When, 420 00:24:06,119 --> 00:24:10,680 Speaker 1: what did how? They definitely defining when they discovered the breach, 421 00:24:10,720 --> 00:24:13,639 Speaker 1: for example, is it when they discovered malware on their systems? 422 00:24:14,119 --> 00:24:18,439 Speaker 1: Is it when they discovered data left the network? Or 423 00:24:18,520 --> 00:24:21,119 Speaker 1: is it when the US government came to them and 424 00:24:21,200 --> 00:24:23,800 Speaker 1: told them about the bridge, which happens a lot. We 425 00:24:23,840 --> 00:24:26,160 Speaker 1: don't know exactly how the discovery worked in this case 426 00:24:26,359 --> 00:24:30,480 Speaker 1: because we don't know a lot and how, how does this? 427 00:24:30,680 --> 00:24:33,960 Speaker 1: How does something like this happened? A company that their 428 00:24:34,000 --> 00:24:38,840 Speaker 1: whole goal should be to protect the most sensitive data 429 00:24:38,880 --> 00:24:43,520 Speaker 1: that they have, and yet this can happen. So I 430 00:24:43,680 --> 00:24:46,160 Speaker 1: was in Bloomer gradual like a week ago. And what 431 00:24:46,200 --> 00:24:49,479 Speaker 1: I said that there is that every system, every system 432 00:24:49,480 --> 00:24:51,600 Speaker 1: and the work can be hacked. I just didn't have 433 00:24:51,640 --> 00:24:54,320 Speaker 1: a lot of code inside, and system can be hacked, 434 00:24:54,440 --> 00:24:56,600 Speaker 1: you know, and a saving hacked like a year ago, 435 00:24:56,760 --> 00:25:01,040 Speaker 1: and other very confidential system can be happy to complex environment. 436 00:25:01,400 --> 00:25:03,600 Speaker 1: So that's the bad news. I think the good news 437 00:25:03,640 --> 00:25:06,440 Speaker 1: is that to do a couple of very basic things, 438 00:25:06,440 --> 00:25:10,760 Speaker 1: if you use existing technology correctly, you can prevent most 439 00:25:10,760 --> 00:25:14,800 Speaker 1: of the other attacks if you do it correctly. Did 440 00:25:14,800 --> 00:25:18,359 Speaker 1: they do it correctly in this case? So I really 441 00:25:18,359 --> 00:25:21,960 Speaker 1: don't know, as it's just stated before, we don't We 442 00:25:21,960 --> 00:25:25,639 Speaker 1: don't have all this knowledge right now. We don't know 443 00:25:25,680 --> 00:25:28,320 Speaker 1: the exact system that was breacher and stardents a website 444 00:25:29,080 --> 00:25:32,720 Speaker 1: that contain confidential data. We don't know if it was segmented, 445 00:25:32,760 --> 00:25:35,800 Speaker 1: We don't know if it was protected correctly or not. 446 00:25:35,880 --> 00:25:39,520 Speaker 1: So we don't have specific information for that systems. But 447 00:25:40,160 --> 00:25:42,879 Speaker 1: as you as as you know, many other systems that 448 00:25:43,000 --> 00:25:48,040 Speaker 1: contain private information for consumer like targets and other been 449 00:25:48,080 --> 00:25:52,639 Speaker 1: hacked before. What is the danger to people whose data 450 00:25:52,760 --> 00:25:57,240 Speaker 1: may have been taken and what at this point can 451 00:25:57,280 --> 00:26:01,480 Speaker 1: you do about it? So it's it's most about identity tests. 452 00:26:02,440 --> 00:26:05,119 Speaker 1: People actually can pretend to be you and and the 453 00:26:05,240 --> 00:26:08,040 Speaker 1: transaction on your behalf from credit cards and other things. 454 00:26:08,119 --> 00:26:11,439 Speaker 1: Think UM, and it is complex to know, and it 455 00:26:11,680 --> 00:26:15,240 Speaker 1: is complex to identify, but you need to be It's 456 00:26:15,240 --> 00:26:17,280 Speaker 1: a bit cautious to look at your bank records and 457 00:26:17,320 --> 00:26:19,119 Speaker 1: any other records gets to you. We tend not to 458 00:26:19,160 --> 00:26:21,760 Speaker 1: look at our credit card line by line, but maybe 459 00:26:21,800 --> 00:26:27,000 Speaker 1: it's a good time to do so, Brian, I mean bright, Michael. Uh, 460 00:26:27,280 --> 00:26:29,199 Speaker 1: this is obviously not going to go down well with 461 00:26:29,240 --> 00:26:33,800 Speaker 1: people who have been critics of the credit reporting agencies, 462 00:26:33,840 --> 00:26:37,240 Speaker 1: like an Elizabeth Warren on the Banking Committee and Capitol Hill. 463 00:26:37,760 --> 00:26:41,480 Speaker 1: What happens, Uh, what's the reaction going to be in 464 00:26:41,520 --> 00:26:45,480 Speaker 1: Washington to all this? Well? Almost certainly this is gonna 465 00:26:45,720 --> 00:26:51,439 Speaker 1: get the UM lawmakers involved more directly in this debate 466 00:26:51,840 --> 00:26:54,320 Speaker 1: because it raises a fundamental question about whether the system 467 00:26:54,480 --> 00:26:58,160 Speaker 1: as it exists now is the right one, which goes 468 00:26:58,160 --> 00:27:02,200 Speaker 1: to the fundamental business model of these companies. They store 469 00:27:02,200 --> 00:27:05,400 Speaker 1: a huge amount of consumer data, and very important consumer data. 470 00:27:05,600 --> 00:27:07,919 Speaker 1: They have divisions that do lots of different things, and 471 00:27:08,440 --> 00:27:12,520 Speaker 1: equifaxes case, for example, they have a service that that 472 00:27:12,760 --> 00:27:16,639 Speaker 1: stores the kind of security questions and answers that people 473 00:27:16,760 --> 00:27:19,920 Speaker 1: give to validate their identity to credit card companies, into 474 00:27:20,040 --> 00:27:25,320 Speaker 1: others online, and so they have Basically they are storehouse 475 00:27:25,359 --> 00:27:27,119 Speaker 1: that kind of fort knocks of all the sorts of 476 00:27:27,160 --> 00:27:29,680 Speaker 1: digital data that a cyber thief would want to get 477 00:27:29,680 --> 00:27:32,000 Speaker 1: their hands on. That means that they ought to have 478 00:27:32,560 --> 00:27:37,000 Speaker 1: um uh that's the strongest protections possible so that they 479 00:27:37,000 --> 00:27:39,680 Speaker 1: don't lose that data. Equifaxes this is not the first 480 00:27:39,680 --> 00:27:42,679 Speaker 1: problem they've had recently. They've they've uh this is in 481 00:27:42,720 --> 00:27:44,480 Speaker 1: fact the third problem they've had in the last year, 482 00:27:45,000 --> 00:27:47,640 Speaker 1: but by far the largest. But it does suggest that 483 00:27:47,760 --> 00:27:50,160 Speaker 1: as much money as they're making doing this, they're not 484 00:27:50,560 --> 00:27:54,280 Speaker 1: um doing enough to secure that data. And I think 485 00:27:54,320 --> 00:28:00,000 Speaker 1: that there will be questions about their uh particular UH 486 00:28:00,000 --> 00:28:02,680 Speaker 1: appropriate their safeguards and whether they're appropriate, But there'll be 487 00:28:02,720 --> 00:28:05,520 Speaker 1: a set of larger questions that the lawmakers will get 488 00:28:05,520 --> 00:28:07,200 Speaker 1: to ask about whether the model is the right one 489 00:28:07,359 --> 00:28:09,760 Speaker 1: in the first place. And that's going to be a 490 00:28:09,800 --> 00:28:13,200 Speaker 1: major problem if UH Congress begins to think that there 491 00:28:13,240 --> 00:28:16,640 Speaker 1: needs to be either regulation in this sector or a 492 00:28:16,680 --> 00:28:20,240 Speaker 1: different model, or if companies who provide their data to 493 00:28:20,240 --> 00:28:23,280 Speaker 1: Aquifax and the other company, the other credit rating agencies, 494 00:28:23,720 --> 00:28:27,359 Speaker 1: if they decide to to to stop giving them as 495 00:28:27,440 --> 00:28:31,120 Speaker 1: much data or access to the broad range of data 496 00:28:31,160 --> 00:28:33,280 Speaker 1: that they have now, all right, thank you very much. 497 00:28:33,320 --> 00:28:36,240 Speaker 1: Michael Riley, cybersecurity reporter for Bloomberg News and and On 498 00:28:36,400 --> 00:28:40,000 Speaker 1: bar lev the president of Checkpoints Software, thanks for joining 499 00:28:40,120 --> 00:28:46,080 Speaker 1: us today. Thanks for listening to the Bloomberg P and 500 00:28:46,160 --> 00:28:49,200 Speaker 1: L podcast. You can subscribe and listen to interviews at 501 00:28:49,280 --> 00:28:53,720 Speaker 1: Apple Podcasts, SoundCloud, or whatever podcast platform you prefer. I'm 502 00:28:53,760 --> 00:28:57,200 Speaker 1: pim Fox. I'm on Twitter at pim Fox. I'm on 503 00:28:57,200 --> 00:29:00,520 Speaker 1: Twitter at Lisa Abramo wits one. Before the podcast, you 504 00:29:00,520 --> 00:29:03,040 Speaker 1: can always catch us worldwide on Bloomberg Radio