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 and L 6 00:00:20,840 --> 00:00:31,840 Speaker 1: Podcast on Apple Podcasts, SoundCloud, and Bloomberg dot com. Well, 7 00:00:31,880 --> 00:00:36,200 Speaker 1: if real fraud were not enough, how about synthetic identity fraud. 8 00:00:36,240 --> 00:00:38,720 Speaker 1: Here to tell us what it is is Jeffrey Finstein. 9 00:00:38,840 --> 00:00:42,080 Speaker 1: He is a vice president of Global Analytics at Alexis 10 00:00:42,240 --> 00:00:46,040 Speaker 1: Nexus and an expert on synthetic identity fraud. Jeffrey, thank 11 00:00:46,080 --> 00:00:47,879 Speaker 1: you for being with us. What is it? What is 12 00:00:47,920 --> 00:00:51,880 Speaker 1: synthetic identity fraud? Hello, I'm glad to be here. Synthetic 13 00:00:51,920 --> 00:00:54,440 Speaker 1: identity fraud of a really complicated type of fraud that 14 00:00:54,440 --> 00:00:57,280 Speaker 1: we're seeing emerged since roughly two and twelve, and it 15 00:00:57,800 --> 00:01:01,120 Speaker 1: requires a great deal of coordination. And what really happens 16 00:01:01,200 --> 00:01:04,560 Speaker 1: is synthetic identities are these Frankenstein like identities that are 17 00:01:04,600 --> 00:01:08,440 Speaker 1: created by fraudsters, and these identities don't correspond to real people. 18 00:01:08,880 --> 00:01:12,200 Speaker 1: They basically create false identities and they register them, register 19 00:01:12,319 --> 00:01:15,320 Speaker 1: these identities and the credit system with the purpose of 20 00:01:15,360 --> 00:01:18,319 Speaker 1: stealing from companies. And it's not just banks that they're 21 00:01:18,319 --> 00:01:20,839 Speaker 1: stealing from the they steal from all sorts of types 22 00:01:20,880 --> 00:01:24,560 Speaker 1: of companies in particular telecoms, utilities, thin texts, and even 23 00:01:24,600 --> 00:01:27,800 Speaker 1: the government. So, Jeffrey, just to get a sense of 24 00:01:27,840 --> 00:01:30,360 Speaker 1: what this would be, uh, if you played it out, 25 00:01:30,480 --> 00:01:32,760 Speaker 1: a person would create a false identity that they would 26 00:01:32,800 --> 00:01:36,080 Speaker 1: have no qualms about walking away from, leaving a lot 27 00:01:36,080 --> 00:01:39,720 Speaker 1: of bills unpaid for services that they took under their 28 00:01:39,720 --> 00:01:44,360 Speaker 1: real identities. That's exactly right. So what what the fraudster does? 29 00:01:44,480 --> 00:01:47,720 Speaker 1: They create a new identity that doesn't really correspond to 30 00:01:47,760 --> 00:01:52,000 Speaker 1: their true identity, and they curate that identity to make 31 00:01:52,040 --> 00:01:54,160 Speaker 1: them look like better and better customers. So they pay 32 00:01:54,200 --> 00:01:57,280 Speaker 1: their bills on time, and they let that relationship with 33 00:01:57,320 --> 00:02:01,040 Speaker 1: their financial institutions evolve so they're sure rating credit and 34 00:02:01,080 --> 00:02:04,680 Speaker 1: they're becoming good customers. And then what happens is, over 35 00:02:04,720 --> 00:02:08,079 Speaker 1: a period of weeks or months, or it could even 36 00:02:08,120 --> 00:02:12,600 Speaker 1: go on as years, they simply poof disappear and that 37 00:02:12,600 --> 00:02:17,240 Speaker 1: that negative activity doesn't really correspond to a true identity. 38 00:02:17,320 --> 00:02:20,720 Speaker 1: And in fact, that's the big challenge for financial institutions 39 00:02:20,760 --> 00:02:23,560 Speaker 1: that there's no true victim so well, except that they 40 00:02:23,639 --> 00:02:26,520 Speaker 1: are a victim, because that means that there are bills 41 00:02:26,560 --> 00:02:29,600 Speaker 1: that are unpaid out that are outstanding, presumably at the 42 00:02:29,720 --> 00:02:34,280 Speaker 1: end uh, that somebody walks away without paying. But I'm wondering, Jeffrey, 43 00:02:34,280 --> 00:02:36,560 Speaker 1: can you give a sense of what kind of scope 44 00:02:36,560 --> 00:02:38,480 Speaker 1: we're talking about. How big of a problem is this. 45 00:02:39,160 --> 00:02:42,480 Speaker 1: It's hard to say for sure, because financial institutions don't 46 00:02:42,480 --> 00:02:45,239 Speaker 1: always know they've been they've been a victim. So what's 47 00:02:45,280 --> 00:02:47,440 Speaker 1: really happening is they see a really good customer they 48 00:02:47,440 --> 00:02:50,839 Speaker 1: just stop paying their bills, and many financial institutions might 49 00:02:50,880 --> 00:02:55,000 Speaker 1: assume that that's just a consumer that's fell on hard times. Uh. 50 00:02:55,040 --> 00:02:57,880 Speaker 1: And also it's pretty recent, which means we don't have 51 00:02:57,919 --> 00:02:59,880 Speaker 1: a whole lot of time to look at macrowick and 52 00:03:00,040 --> 00:03:02,280 Speaker 1: mc trend's. But let me share a couple of numbers 53 00:03:02,280 --> 00:03:05,440 Speaker 1: with you. For the last ten to fifteen years, around 54 00:03:05,520 --> 00:03:09,560 Speaker 1: seven million new identities enter into the credit system, and 55 00:03:09,600 --> 00:03:12,880 Speaker 1: a surprisingly consistent over that ten to fifteen years. The 56 00:03:12,960 --> 00:03:15,840 Speaker 1: little bit of fluctuation we tend to see is usually 57 00:03:15,880 --> 00:03:19,680 Speaker 1: related to either fluctuations and birth rate about fifteen years 58 00:03:19,680 --> 00:03:23,320 Speaker 1: prior or immigration rate, and and so what we see 59 00:03:23,320 --> 00:03:25,760 Speaker 1: is that stable number. Whoever, since two thousand and twelve 60 00:03:25,840 --> 00:03:29,440 Speaker 1: to about today, we've seen a ten percent increase in 61 00:03:29,560 --> 00:03:32,919 Speaker 1: that number that had been historically stable. And in fact, 62 00:03:32,919 --> 00:03:36,120 Speaker 1: when you start digging into these new identities, we've seen 63 00:03:36,480 --> 00:03:41,120 Speaker 1: almost an eight increase in the number of suspicious identities, 64 00:03:41,440 --> 00:03:45,840 Speaker 1: and that is suspicious synthetic identities entering that system. And 65 00:03:45,880 --> 00:03:49,440 Speaker 1: in fact, just speaking to a risk manager at a bank, 66 00:03:49,840 --> 00:03:52,200 Speaker 1: uh this was a week or two ago, they actually 67 00:03:52,200 --> 00:03:55,240 Speaker 1: indicated this was an eight digit problem. That's tens of 68 00:03:55,320 --> 00:04:00,160 Speaker 1: millions of dollars of meaningful losses to that financial institution. So, 69 00:04:00,240 --> 00:04:03,680 Speaker 1: is there a situation where you have collection agencies that 70 00:04:03,760 --> 00:04:07,600 Speaker 1: are trying to collect money that is due a company, 71 00:04:07,640 --> 00:04:10,720 Speaker 1: whether it's a telco or a bank, they're trying to 72 00:04:10,840 --> 00:04:14,520 Speaker 1: collect from people who do not exist. That's a that's 73 00:04:14,560 --> 00:04:16,760 Speaker 1: a really good point and it's part of the problem. 74 00:04:17,240 --> 00:04:20,240 Speaker 1: So in true name fraud, what happens is that the 75 00:04:20,279 --> 00:04:22,599 Speaker 1: consumer victim will raise their hand and work with the 76 00:04:22,600 --> 00:04:26,000 Speaker 1: bank and essentially that gets wiped from the system as 77 00:04:26,040 --> 00:04:30,120 Speaker 1: part of their fraud operations. But because these these synthetic 78 00:04:30,240 --> 00:04:34,720 Speaker 1: or these Frankenstein consumers just disappear and they're treated as 79 00:04:34,760 --> 00:04:37,560 Speaker 1: credit risk. You will see them all through the life cycle, 80 00:04:37,640 --> 00:04:41,440 Speaker 1: and we do see them on collection files when we 81 00:04:41,520 --> 00:04:44,800 Speaker 1: help customers figure out, Hey, why why this collection program 82 00:04:44,839 --> 00:04:47,720 Speaker 1: not working? It's pretty common to see that there are 83 00:04:47,720 --> 00:04:52,480 Speaker 1: synthetic identities drifting into those collections programs. Jeffrey, has there 84 00:04:52,520 --> 00:04:56,279 Speaker 1: been any progress made in identifying the types of people 85 00:04:56,279 --> 00:04:58,480 Speaker 1: who create these identities? I mean, it seems like it's 86 00:04:58,520 --> 00:05:02,960 Speaker 1: a relatively sophistic CADD scheme in that people are curating 87 00:05:03,520 --> 00:05:06,559 Speaker 1: uh their credit worthiness over a particular period of time. 88 00:05:06,600 --> 00:05:10,039 Speaker 1: This doesn't this takes This takes quite a bit of effort. Yeah, 89 00:05:10,120 --> 00:05:13,280 Speaker 1: this is definitely a full time job for those fraudsters 90 00:05:13,279 --> 00:05:16,400 Speaker 1: who are committing these sorts of fraud. And while I 91 00:05:16,440 --> 00:05:18,040 Speaker 1: can go through it, I don't want to say too 92 00:05:18,160 --> 00:05:21,080 Speaker 1: much because I also don't want to give instructions over 93 00:05:21,080 --> 00:05:23,960 Speaker 1: the radio and how to create these identities. But what 94 00:05:24,000 --> 00:05:27,920 Speaker 1: we see is these fraudsters working in identity factories. It's 95 00:05:28,000 --> 00:05:30,760 Speaker 1: almost like an assembly line of smoke and mirrors to 96 00:05:30,880 --> 00:05:34,039 Speaker 1: create these Frankenstein identities. And in fact, we know of 97 00:05:34,120 --> 00:05:37,240 Speaker 1: one in the state of Pennsylvania that created seven thousand 98 00:05:37,279 --> 00:05:41,960 Speaker 1: of these fake identities. And what's interesting about that is 99 00:05:42,160 --> 00:05:46,320 Speaker 1: when you look at your own history, your your identity 100 00:05:46,360 --> 00:05:49,000 Speaker 1: has really evolved at the speed of life. And what 101 00:05:49,040 --> 00:05:51,760 Speaker 1: I mean by that is there are key milestones when 102 00:05:51,760 --> 00:05:54,160 Speaker 1: you register a car, You've got a driver's license, your 103 00:05:54,160 --> 00:05:56,960 Speaker 1: registered to vote, you got credit and so forth, and 104 00:05:57,160 --> 00:06:02,360 Speaker 1: that's a relatively slow my grace. In a true identity, however, 105 00:06:02,920 --> 00:06:06,520 Speaker 1: these identities factories are really scaled for time, and what 106 00:06:06,560 --> 00:06:10,080 Speaker 1: that means is they don't evolve that quickly. They evolved 107 00:06:10,120 --> 00:06:13,600 Speaker 1: for a profit motive as opposed to a true life motive. 108 00:06:14,000 --> 00:06:16,600 Speaker 1: That makes sense, That makes perfect sense. Jeffrey Finestein, thank 109 00:06:16,600 --> 00:06:21,080 Speaker 1: you so much for joining us. Fascinating synthetic synthetic identity fraud. Uh, 110 00:06:21,120 --> 00:06:24,719 Speaker 1: definitely an interesting development. Jeffrey Finstein is PhD and Vice 111 00:06:24,720 --> 00:06:40,320 Speaker 1: president of Global Analytics at Lexis Nexus. We are still 112 00:06:40,360 --> 00:06:45,040 Speaker 1: digesting all the details of the horrific mass shooting at 113 00:06:45,040 --> 00:06:49,520 Speaker 1: a country music festival near Las Vegas on October two yesterday. 114 00:06:50,000 --> 00:06:53,120 Speaker 1: Joining us as Chris Paul Mary who is in Las Vegas. 115 00:06:53,160 --> 00:06:57,200 Speaker 1: He is the Beera chief for Bloomberg in Los Angeles. Chris, 116 00:06:57,240 --> 00:07:00,159 Speaker 1: can you just give us first a sense of the 117 00:07:00,240 --> 00:07:03,680 Speaker 1: mood right now in Las Vegas. I'm sure you've been 118 00:07:03,720 --> 00:07:09,640 Speaker 1: there before. Has it changed, Yeah, it's it's definitely very somber. 119 00:07:09,800 --> 00:07:12,720 Speaker 1: This is just coincidentally the start of the Global Gaming Expo, 120 00:07:12,800 --> 00:07:15,480 Speaker 1: which is the largest trade show for the casino industry. 121 00:07:15,520 --> 00:07:17,920 Speaker 1: Twenty six thousand people from all around the world coming in. 122 00:07:18,520 --> 00:07:23,000 Speaker 1: So uh, some speakers had canceled, some spoken were you know, 123 00:07:23,760 --> 00:07:28,480 Speaker 1: very choked up. There were you know, signs of increased 124 00:07:28,520 --> 00:07:33,280 Speaker 1: security everywhere. Uh, it's it's definitely had taken a sort 125 00:07:33,280 --> 00:07:38,800 Speaker 1: of a toll overall, and usually a party city. Chris, 126 00:07:38,840 --> 00:07:42,480 Speaker 1: what about the increased security measures? We know that for 127 00:07:42,520 --> 00:07:47,720 Speaker 1: example worldwide sporting events, whether it's the FIFA World Cup, 128 00:07:47,840 --> 00:07:49,960 Speaker 1: you know, you go to an event like that, you're 129 00:07:49,960 --> 00:07:53,120 Speaker 1: gonna be scanned and there's gonna be someone with a 130 00:07:53,200 --> 00:07:58,200 Speaker 1: wand do you see those kinds of products in evidence today? 131 00:07:58,320 --> 00:08:01,080 Speaker 1: I just saw that for the first on yesterday at 132 00:08:01,120 --> 00:08:04,480 Speaker 1: the Wind Resort, where anybody that wanted to walk into 133 00:08:04,480 --> 00:08:08,360 Speaker 1: the hotel had to get their bags checked and and 134 00:08:08,440 --> 00:08:12,080 Speaker 1: be wanted, you know with the metal detector. Uh, you 135 00:08:12,120 --> 00:08:15,560 Speaker 1: know that, you know, we're in immediate aftermath the some 136 00:08:15,600 --> 00:08:19,120 Speaker 1: of the MGM properties were on lockdown. And they were 137 00:08:19,200 --> 00:08:21,480 Speaker 1: checking ideas of people who wanted to come in to 138 00:08:21,480 --> 00:08:23,720 Speaker 1: make sure they were guests. But I haven't seen any 139 00:08:23,720 --> 00:08:27,360 Speaker 1: other patinos he had adopted what Win had done. Steve 140 00:08:27,360 --> 00:08:29,680 Speaker 1: Win has always been a leader in the industry. Uh. 141 00:08:29,880 --> 00:08:32,120 Speaker 1: You know, I did speak to some casino as actually 142 00:08:32,120 --> 00:08:34,360 Speaker 1: said that that very may wear it very well be 143 00:08:34,400 --> 00:08:37,880 Speaker 1: the future of anybody that comes into building, because that 144 00:08:38,200 --> 00:08:40,640 Speaker 1: that there was really no other way to prevent the 145 00:08:40,720 --> 00:08:44,320 Speaker 1: situation with the shooter, with the guns in the hotel room. 146 00:08:44,520 --> 00:08:47,800 Speaker 1: He wasn't attending the concert. Yeah, well, I mean, Chris 147 00:08:47,840 --> 00:08:50,120 Speaker 1: that that was sort of what struck me is how 148 00:08:50,200 --> 00:08:54,120 Speaker 1: does a person how many guns did he have? Eighteen? 149 00:08:54,200 --> 00:08:56,640 Speaker 1: How many he had? A whole arsenal that he brought 150 00:08:56,760 --> 00:09:00,040 Speaker 1: up to a high floor of a hotel and and 151 00:09:00,520 --> 00:09:04,440 Speaker 1: from that perch uh massacred fifty nine people. I mean, 152 00:09:04,480 --> 00:09:07,600 Speaker 1: that's it's sort of amazing, frankly, that that could happen 153 00:09:07,640 --> 00:09:11,760 Speaker 1: at all, amazing amount of planning to on his end. Uh. 154 00:09:12,080 --> 00:09:14,080 Speaker 1: You know, I think he checked in on Thursday. You know, 155 00:09:14,120 --> 00:09:16,319 Speaker 1: he had more gear than just all those guns, and 156 00:09:16,440 --> 00:09:18,080 Speaker 1: he hit some sort of hammer that he could break 157 00:09:18,080 --> 00:09:20,680 Speaker 1: those windows which are very thick. Uh. He had a 158 00:09:20,760 --> 00:09:23,480 Speaker 1: stand that he could rest the weapons on to get 159 00:09:23,520 --> 00:09:29,480 Speaker 1: a better shot. Just you know, amazing and it's diabolical nature. So, Chris, 160 00:09:29,520 --> 00:09:34,040 Speaker 1: are we getting any more details behind the motivation? I 161 00:09:34,080 --> 00:09:36,240 Speaker 1: have read a number of stories saying that he was 162 00:09:36,280 --> 00:09:39,800 Speaker 1: not connected with anybody else, that the threat has come down. 163 00:09:40,040 --> 00:09:44,800 Speaker 1: But are there any more details behind what actually transpired? Yes? Um, 164 00:09:44,800 --> 00:09:48,319 Speaker 1: it's um it's a big, you know question mark because 165 00:09:48,440 --> 00:09:52,880 Speaker 1: you know, I spoke to security people and there were uh, 166 00:09:53,120 --> 00:09:56,120 Speaker 1: you know, no signs really uh, you know, typically on 167 00:09:56,160 --> 00:09:59,440 Speaker 1: these active shooters of them, if some sort of warning 168 00:09:59,480 --> 00:10:03,040 Speaker 1: speak this body leave the manifesto whatever. Uh, this guy 169 00:10:03,080 --> 00:10:07,280 Speaker 1: seemingly came out of nowhere. Chris, Uh is Nevada is 170 00:10:07,320 --> 00:10:09,719 Speaker 1: an open carry state? Correct? I think the thirty one 171 00:10:09,760 --> 00:10:12,480 Speaker 1: states that allow open carrying of handguns. And this was 172 00:10:12,520 --> 00:10:16,080 Speaker 1: not a handgun, of course. Uh, there was a measure 173 00:10:16,120 --> 00:10:19,319 Speaker 1: just recently trying to get the background checks in tighter 174 00:10:19,360 --> 00:10:22,880 Speaker 1: gun control. Everyone here are sort of focus on the 175 00:10:22,920 --> 00:10:26,200 Speaker 1: morning and the events and immediate security. But I would 176 00:10:26,280 --> 00:10:29,800 Speaker 1: imagine if the days come and president visits will be 177 00:10:29,840 --> 00:10:33,440 Speaker 1: a much greater focus on on gun control. I wonder 178 00:10:33,679 --> 00:10:37,120 Speaker 1: what the mood is among some of the casino operators. 179 00:10:37,160 --> 00:10:39,360 Speaker 1: I mean, this has to be a huge blow for them. 180 00:10:39,400 --> 00:10:42,520 Speaker 1: I mean, on a personal level, it's always just devastating 181 00:10:42,800 --> 00:10:46,440 Speaker 1: to see this type of death toll. But aside from that, 182 00:10:46,640 --> 00:10:49,840 Speaker 1: going forward, I would imagine that people would rethink some 183 00:10:49,920 --> 00:10:52,440 Speaker 1: of their plans to Las Vegas unless there's some very 184 00:10:52,480 --> 00:10:57,480 Speaker 1: clear measures to prevent this type of thing from happening again. No, uh, 185 00:10:57,520 --> 00:11:01,280 Speaker 1: you know, I it's it's to school intern analyzed. I 186 00:11:01,280 --> 00:11:04,079 Speaker 1: said that that it happened during the start of this convention, 187 00:11:04,160 --> 00:11:07,600 Speaker 1: but I think it definitely sends a message because you know, executives, 188 00:11:07,600 --> 00:11:09,840 Speaker 1: you know what's happened before, because you knows there's been 189 00:11:09,880 --> 00:11:13,120 Speaker 1: a massive shift here in Las Vegas to create you know, 190 00:11:13,559 --> 00:11:14,880 Speaker 1: you used to be they try to keep people in 191 00:11:14,920 --> 00:11:19,360 Speaker 1: the casinos. Now they're opening the doors literally with events, concerts, uh, 192 00:11:19,559 --> 00:11:22,000 Speaker 1: you know, creating a pedestrian friendly areas in front of 193 00:11:22,000 --> 00:11:25,600 Speaker 1: the hotels. So as they focused on you know, these 194 00:11:25,679 --> 00:11:29,800 Speaker 1: more experiential as they call them, you know, experiences that 195 00:11:31,080 --> 00:11:34,240 Speaker 1: you know, they've got to take consecurity a lot more 196 00:11:34,280 --> 00:11:36,520 Speaker 1: into consideration. They've got to look at things from all 197 00:11:36,559 --> 00:11:39,240 Speaker 1: angles and act like the Secret Service and think about 198 00:11:39,720 --> 00:11:44,240 Speaker 1: you know, tall buildings, and constant vigilance during events, and 199 00:11:45,120 --> 00:11:47,480 Speaker 1: it's going to be a different world. You know, Chris 200 00:11:47,760 --> 00:11:50,800 Speaker 1: as l a bureau chief. You go from California where 201 00:11:50,840 --> 00:11:54,560 Speaker 1: they have a strict uncontrol rules and regulations, but you know, 202 00:11:54,640 --> 00:11:59,760 Speaker 1: firearms are allowed, and to uh Las Vegas, which is 203 00:11:59,800 --> 00:12:04,680 Speaker 1: really an international tourist destination. Is there any conversation or 204 00:12:04,720 --> 00:12:09,800 Speaker 1: any debate that you've heard about limiting the access of 205 00:12:09,880 --> 00:12:16,800 Speaker 1: people who carry firearms legally to various private properties. Not immediately, 206 00:12:16,880 --> 00:12:19,480 Speaker 1: but you're just a little color. I went yesterday to 207 00:12:19,520 --> 00:12:22,040 Speaker 1: this place called Battlefield, Vegas, which is one of these places, 208 00:12:22,080 --> 00:12:23,520 Speaker 1: and they are quite a number of them, very close 209 00:12:23,559 --> 00:12:25,680 Speaker 1: to the strip where you can go fire machine guns, 210 00:12:25,720 --> 00:12:29,400 Speaker 1: and in this particular place looked like a natural guard arsenal. 211 00:12:29,480 --> 00:12:32,520 Speaker 1: I mean they had tanks and the employees were wearing 212 00:12:32,800 --> 00:12:36,520 Speaker 1: fatigues and there were people there. There were there was 213 00:12:36,360 --> 00:12:38,160 Speaker 1: I was, I don't know what the regular day is like, 214 00:12:38,280 --> 00:12:39,880 Speaker 1: but you know, I talked to some guys who said, hey, 215 00:12:39,920 --> 00:12:42,240 Speaker 1: we scheduled this before you know, they're walking out with 216 00:12:42,320 --> 00:12:46,400 Speaker 1: these targets that are full of machine gun bullet holes. Uh. 217 00:12:46,440 --> 00:12:48,280 Speaker 1: You know, there were tourists from me as you know, 218 00:12:48,280 --> 00:12:51,160 Speaker 1: getting a picture taken on tanks. Uh uh, you know, 219 00:12:51,240 --> 00:12:53,800 Speaker 1: the manager I didn't want to talk to me, but 220 00:12:54,440 --> 00:12:56,600 Speaker 1: you know, there is very much, uh you know, a 221 00:12:56,640 --> 00:12:59,200 Speaker 1: desire in some communities to have this kind of access. 222 00:12:59,280 --> 00:13:01,920 Speaker 1: So it's gonna be the tether of work going forward. Well, 223 00:13:02,040 --> 00:13:05,560 Speaker 1: is that the subject of discussion just among people who 224 00:13:05,720 --> 00:13:12,040 Speaker 1: you encounter as you walk around Las Vegas. Initially, I 225 00:13:12,080 --> 00:13:14,439 Speaker 1: think it was really focused on on just the shock 226 00:13:14,600 --> 00:13:17,480 Speaker 1: and the UH and the and the enhanced security. I 227 00:13:17,480 --> 00:13:21,040 Speaker 1: did speak to the visit the former Boston Police commissioner 228 00:13:21,040 --> 00:13:24,040 Speaker 1: who was there during the Boston UH marathon attack, and 229 00:13:24,520 --> 00:13:26,360 Speaker 1: you know, he did bring up the subject of gun control. 230 00:13:26,400 --> 00:13:28,040 Speaker 1: He said he would have thought would been an issue 231 00:13:28,080 --> 00:13:30,680 Speaker 1: a lot earlier with Sandy Hook and that. And he's 232 00:13:30,679 --> 00:13:33,920 Speaker 1: actually speaking this morning to the to the convention, so 233 00:13:33,960 --> 00:13:36,800 Speaker 1: we'll see what he has to say there. But you know, 234 00:13:38,240 --> 00:13:41,240 Speaker 1: I'm sure it'll be uh more of a conversation as 235 00:13:41,280 --> 00:13:45,000 Speaker 1: that as the initial shock ward off. Well, apparently, you know, 236 00:13:45,280 --> 00:13:47,920 Speaker 1: and this's just coming from Greg Jared who says, you know, 237 00:13:47,960 --> 00:13:52,600 Speaker 1: you cannot carry a firearm into an establishment that has gambling, 238 00:13:52,880 --> 00:13:57,000 Speaker 1: and most casinos do not allow concealed weapons even if 239 00:13:57,000 --> 00:13:59,960 Speaker 1: you have a concealed weapons permit, you can be refus 240 00:14:00,040 --> 00:14:03,319 Speaker 1: used entry because, as I said earlier, it's a private business. 241 00:14:03,320 --> 00:14:04,840 Speaker 1: I want to thank you very much for joining us. 242 00:14:04,920 --> 00:14:08,440 Speaker 1: Chris Palmry he is our Los Angeles Bureau chief, reporting 243 00:14:08,520 --> 00:14:23,360 Speaker 1: from Las Vegas. Well, we want to visit right now 244 00:14:23,360 --> 00:14:26,960 Speaker 1: with Rebecca LENLN, executive analysts for Cox Automotive, because of 245 00:14:26,960 --> 00:14:29,760 Speaker 1: course we got many of the sales reports this morning, 246 00:14:29,800 --> 00:14:32,880 Speaker 1: Toyota GM posting the biggest US sales gains on the 247 00:14:33,200 --> 00:14:36,360 Speaker 1: post Harvey hurricane demand, and she joins us here in 248 00:14:36,440 --> 00:14:39,080 Speaker 1: our eleven three oh studios. Rebecca, always a pleasure, Thanks 249 00:14:39,080 --> 00:14:41,040 Speaker 1: for being with us. What do you make What do 250 00:14:41,080 --> 00:14:43,880 Speaker 1: you make of this report? I think that we've seen 251 00:14:44,280 --> 00:14:47,160 Speaker 1: some fantastic sales coming out. They basically have beat all 252 00:14:47,160 --> 00:14:50,480 Speaker 1: of our analyst estimates. And I think it's a combination 253 00:14:50,560 --> 00:14:54,400 Speaker 1: of things. It's really pent up demand from the hurricane 254 00:14:54,400 --> 00:14:56,840 Speaker 1: because a lot of people didn't shop in the last 255 00:14:56,880 --> 00:15:00,400 Speaker 1: few weeks both of August and September, and so we 256 00:15:00,480 --> 00:15:03,400 Speaker 1: have some pent up demand there. And then we also 257 00:15:03,560 --> 00:15:07,600 Speaker 1: have fantastic incentives. We've got great new products coming out, 258 00:15:07,960 --> 00:15:12,800 Speaker 1: We've got a stock market at record levels, high consumer confidence, 259 00:15:13,160 --> 00:15:16,040 Speaker 1: there's all sorts of fantastic reasons to go out and 260 00:15:16,080 --> 00:15:18,320 Speaker 1: buy a car, and there's great new product coming out 261 00:15:18,360 --> 00:15:22,240 Speaker 1: as well. Well. This is such a shift in tone 262 00:15:22,480 --> 00:15:25,800 Speaker 1: because just a few months ago, a lot of automakers 263 00:15:25,840 --> 00:15:29,560 Speaker 1: were talking about how they're expecting to see sales declines. 264 00:15:29,600 --> 00:15:33,560 Speaker 1: They were seeing sales declines, resale values of cars were 265 00:15:33,600 --> 00:15:37,040 Speaker 1: going down all of a sudden. Now, is this really 266 00:15:37,080 --> 00:15:41,440 Speaker 1: a shift in the overall landscape landscape for automakers, or 267 00:15:41,520 --> 00:15:44,640 Speaker 1: is this really a blip that is due to the hurricanes. 268 00:15:45,160 --> 00:15:47,400 Speaker 1: It's more of a blip, And I don't think that 269 00:15:47,440 --> 00:15:52,400 Speaker 1: we're necessarily going to see this continue, you know. So 270 00:15:52,400 --> 00:15:55,520 Speaker 1: so come as we report in on October sales on 271 00:15:55,560 --> 00:16:00,240 Speaker 1: November one, I think we could see some decline. But 272 00:16:00,320 --> 00:16:03,119 Speaker 1: at the same time, people that are getting insurance payouts 273 00:16:03,560 --> 00:16:07,320 Speaker 1: may still be shopping in October. So I think it's 274 00:16:07,360 --> 00:16:11,160 Speaker 1: hard to say now the full impact of the hurricanes 275 00:16:11,440 --> 00:16:13,840 Speaker 1: and how that plays out over the next few months. 276 00:16:14,440 --> 00:16:16,840 Speaker 1: And again, if we think about product, we've got a 277 00:16:16,880 --> 00:16:19,200 Speaker 1: new Cameray, We've got a new Honda Chord that I 278 00:16:19,280 --> 00:16:23,160 Speaker 1: just drove last week. So we've got some really high volume, 279 00:16:23,640 --> 00:16:29,600 Speaker 1: really good vehicles coming out The difficulty through withen is 280 00:16:29,640 --> 00:16:34,280 Speaker 1: that because we had a record setting every month, it's 281 00:16:34,320 --> 00:16:37,280 Speaker 1: gonna you know, it's hard to beat that record. So 282 00:16:37,320 --> 00:16:39,760 Speaker 1: we're always going to be down a little bit. Yes, 283 00:16:39,840 --> 00:16:42,760 Speaker 1: although you know you said they're such great incentives. Incentives 284 00:16:42,760 --> 00:16:44,880 Speaker 1: are great for the buyers, they're not that great for 285 00:16:45,080 --> 00:16:48,720 Speaker 1: the manufacturers because that means they're expect accepting a lower 286 00:16:48,800 --> 00:16:51,600 Speaker 1: profit margin. And yes, they are coming off of an 287 00:16:51,640 --> 00:16:55,000 Speaker 1: exceptionally good year, but they've seen weakness in a lot 288 00:16:55,040 --> 00:17:00,160 Speaker 1: of areas and their profits have sort of have reflected that. Well, 289 00:17:00,160 --> 00:17:05,040 Speaker 1: General Motors still making nine billion dollars in profits fair enough. 290 00:17:05,520 --> 00:17:07,520 Speaker 1: I mean, I think it's important to remember that this 291 00:17:07,600 --> 00:17:10,800 Speaker 1: is not the industry of two thousand seven, eight and nine. 292 00:17:11,160 --> 00:17:15,840 Speaker 1: This is a much more disciplined, much trimmer industry. And 293 00:17:15,920 --> 00:17:18,800 Speaker 1: so you know, a coming like GM has shed brands, 294 00:17:19,119 --> 00:17:22,960 Speaker 1: uh Chryslers shed models. So we're in a situation where 295 00:17:23,119 --> 00:17:27,600 Speaker 1: even if they incentivize, we are still making some really 296 00:17:27,600 --> 00:17:30,680 Speaker 1: healthy profit numbers. Let me just give you an example, 297 00:17:30,680 --> 00:17:32,600 Speaker 1: and I want your reaction to this. If you want 298 00:17:32,640 --> 00:17:36,280 Speaker 1: to buy in camera and you want to finance it, 299 00:17:36,320 --> 00:17:40,800 Speaker 1: they'll give you five dollars bonus cash zero percent a 300 00:17:40,960 --> 00:17:45,880 Speaker 1: p R and seventy two months. Yes, So as a 301 00:17:45,920 --> 00:17:49,679 Speaker 1: former employee of the f D i C, I do 302 00:17:50,160 --> 00:17:54,280 Speaker 1: look carefully at some of these credits and credit worthiness 303 00:17:54,280 --> 00:17:57,040 Speaker 1: and such. You know, one of the we own Kelly 304 00:17:57,080 --> 00:18:00,000 Speaker 1: blue Book Dealer dot com Auto Trader, so we're able 305 00:18:00,080 --> 00:18:03,560 Speaker 1: to track a lot of different parts of the industry. 306 00:18:04,160 --> 00:18:06,960 Speaker 1: And the number one thing that I that I caution 307 00:18:07,040 --> 00:18:10,680 Speaker 1: consumers against is over extending themselves. You do not want 308 00:18:10,680 --> 00:18:13,480 Speaker 1: to go for a seventy two or eighty four month 309 00:18:13,560 --> 00:18:17,080 Speaker 1: loan because then when you trade that vehicle in, you 310 00:18:17,160 --> 00:18:19,680 Speaker 1: probably will owe more than it's worth. So I would 311 00:18:19,720 --> 00:18:24,119 Speaker 1: just caution people. Let's talk Tesla talk that everyone loves 312 00:18:24,160 --> 00:18:27,440 Speaker 1: to hate and yet that just won't die. We saw 313 00:18:27,480 --> 00:18:33,000 Speaker 1: them having manufacturing difficulties, unable to fulfill uh the production 314 00:18:33,040 --> 00:18:36,680 Speaker 1: amounts that they had expected. Shares are down a bit 315 00:18:36,840 --> 00:18:39,600 Speaker 1: knocked down as much as money people would like. Um, 316 00:18:39,760 --> 00:18:41,639 Speaker 1: do you think that they have a chance given the 317 00:18:41,680 --> 00:18:45,520 Speaker 1: fact that the Detroit automakers are ramping up their electric 318 00:18:45,560 --> 00:18:48,760 Speaker 1: and automotive car units. So you know, a Lisa, It's 319 00:18:49,080 --> 00:18:51,080 Speaker 1: Tesla is First of all, I'm a huge fan of 320 00:18:51,119 --> 00:18:53,960 Speaker 1: Elon Musk. I think that we need people like him 321 00:18:54,040 --> 00:18:57,440 Speaker 1: to push our industry. So so you said something nice 322 00:18:57,440 --> 00:19:01,680 Speaker 1: about him, Yes, but I do. I mean, I think 323 00:19:01,720 --> 00:19:05,080 Speaker 1: his technology is amazing. I think that his and you know, 324 00:19:05,119 --> 00:19:08,240 Speaker 1: the people that buy into his dream of changing the 325 00:19:08,320 --> 00:19:10,920 Speaker 1: face of mobility. He has given people a very clear 326 00:19:11,320 --> 00:19:13,439 Speaker 1: why why does he want to do this? And people 327 00:19:13,480 --> 00:19:17,480 Speaker 1: have reacted to that, and I think that his products 328 00:19:17,480 --> 00:19:21,200 Speaker 1: are amazing as well. The challenges that once he goes 329 00:19:21,240 --> 00:19:23,720 Speaker 1: through those five thousand people that have a deposit on 330 00:19:23,760 --> 00:19:28,800 Speaker 1: the Model three, is he adding two thousand new deposits, 331 00:19:29,200 --> 00:19:32,280 Speaker 1: you know, to make to to as that demand gets filled. 332 00:19:32,920 --> 00:19:36,520 Speaker 1: So I think that that's the challenge, is getting people 333 00:19:36,600 --> 00:19:40,600 Speaker 1: to buy his electric vehicle, and not just by his dream. 334 00:19:42,080 --> 00:19:44,480 Speaker 1: Dream is costly, right, because I mean he's not making 335 00:19:44,520 --> 00:19:47,280 Speaker 1: any money. He is not making any money, but apparently 336 00:19:47,320 --> 00:19:49,480 Speaker 1: that doesn't matter to Wall Street because they're buying into 337 00:19:49,520 --> 00:19:51,720 Speaker 1: the dream as well well. And he owns a lot 338 00:19:51,720 --> 00:19:53,439 Speaker 1: of the shares too, So that's one thing that a 339 00:19:53,440 --> 00:19:56,159 Speaker 1: lot of short silus will raise because it's hard to 340 00:19:56,359 --> 00:20:00,080 Speaker 1: uh to borrow very expensively more than a little it. 341 00:20:01,200 --> 00:20:03,600 Speaker 1: And you know, somebody very much smarter than me had 342 00:20:03,640 --> 00:20:06,600 Speaker 1: mentioned that they're wondering with auto sales. You know, let's 343 00:20:06,640 --> 00:20:09,840 Speaker 1: say those five thousand people, those people are kind of 344 00:20:09,880 --> 00:20:12,240 Speaker 1: out of the market a little bit, like they're waiting, 345 00:20:12,400 --> 00:20:15,840 Speaker 1: you know, so there's some pent up demand for people 346 00:20:15,920 --> 00:20:18,000 Speaker 1: that have a deposit on the Model three that are 347 00:20:18,040 --> 00:20:20,760 Speaker 1: saying I'm not going to buy Maybe I'm not going 348 00:20:20,800 --> 00:20:23,760 Speaker 1: to buy that Chevy Vault or Bolt because I'm waiting 349 00:20:23,800 --> 00:20:26,840 Speaker 1: for the Model three. So you know, it could be 350 00:20:26,920 --> 00:20:30,359 Speaker 1: impacting the market a little bit as well. Rebecca Linlin, 351 00:20:30,440 --> 00:20:33,160 Speaker 1: thank you so much for joining us. Truly of wonderful 352 00:20:33,200 --> 00:20:36,080 Speaker 1: to speak with you. Rebecca Linlin is an executive analystic 353 00:20:36,200 --> 00:20:39,320 Speaker 1: Cox automate of talking about the fantastic sales numbers that 354 00:20:39,359 --> 00:20:44,639 Speaker 1: we've been getting out. Thanks for listening to the Bloomberg 355 00:20:44,680 --> 00:20:47,320 Speaker 1: p m L podcast. You can subscribe and listen to 356 00:20:47,359 --> 00:20:51,919 Speaker 1: interviews at Apple Podcasts, SoundCloud, or whatever podcast platform you prefer. 357 00:20:52,320 --> 00:20:55,880 Speaker 1: I'm pim Fox. I'm on Twitter at pim Fox. I'm 358 00:20:55,920 --> 00:20:59,200 Speaker 1: on Twitter at Lisa Abramo. It's one before the podcast. 359 00:20:59,240 --> 00:21:05,000 Speaker 1: You can always cat just worldwide on Bloomberg RADIOH