1 00:00:09,920 --> 00:00:14,120 Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast. 2 00:00:14,200 --> 00:00:19,200 Speaker 1: I'm Joe Wisenthal and I'm Tracy. Allowit, Tracy, I think 3 00:00:19,440 --> 00:00:23,560 Speaker 1: you're really gonna like today's episode. Oh okay, what is it? 4 00:00:23,800 --> 00:00:26,400 Speaker 1: I mean? I guess I would hope that you like 5 00:00:26,520 --> 00:00:29,040 Speaker 1: all of the episodes. But I feel like this is 6 00:00:29,040 --> 00:00:31,560 Speaker 1: a topic that, just from what I know about you 7 00:00:31,640 --> 00:00:35,479 Speaker 1: really animate too. But we are going to be talking 8 00:00:35,520 --> 00:00:40,559 Speaker 1: about fraud and financial fraud. Fraud. Wait. Should I take 9 00:00:40,600 --> 00:00:43,080 Speaker 1: that as like a personal slight that you think I'm 10 00:00:43,080 --> 00:00:46,080 Speaker 1: really interested in financial fraud? No, it's not a slight. 11 00:00:46,240 --> 00:00:49,560 Speaker 1: I just feel like that you have a mind that 12 00:00:49,920 --> 00:00:55,040 Speaker 1: likes to figure out frauds and leans towards fraud. Yeah, exactly, 13 00:00:55,080 --> 00:00:58,880 Speaker 1: and figures them out and understands how they happen and 14 00:00:58,920 --> 00:01:02,960 Speaker 1: the conditions it allow frauds to take place, and how 15 00:01:03,000 --> 00:01:05,880 Speaker 1: people missed everything. I feel like that's a youth thing. 16 00:01:05,920 --> 00:01:09,520 Speaker 1: Am I wrong? No, you're right. Uh, you actually nailed 17 00:01:09,520 --> 00:01:11,680 Speaker 1: it on the head in um what you just said. 18 00:01:11,760 --> 00:01:15,399 Speaker 1: It's the fact that people miss these things when they're happening, 19 00:01:15,520 --> 00:01:18,479 Speaker 1: and then as they unfold, you kind of go back 20 00:01:18,480 --> 00:01:21,280 Speaker 1: and you see all the warning signs and you think, 21 00:01:21,440 --> 00:01:24,640 Speaker 1: how could people not have seen this coming. That's what 22 00:01:24,720 --> 00:01:27,920 Speaker 1: fascinates me the most. It's it's people missing out on 23 00:01:28,040 --> 00:01:32,199 Speaker 1: something that is quite clear in hindsight. Yeah, it's pretty 24 00:01:32,200 --> 00:01:35,760 Speaker 1: incredible when we have these stories about big financial frauds, 25 00:01:35,760 --> 00:01:40,399 Speaker 1: how egregious they seem always in retrospect, and how they're 26 00:01:40,400 --> 00:01:43,800 Speaker 1: just always seemed to be numerous red flags that anyone 27 00:01:43,880 --> 00:01:46,080 Speaker 1: with half a brain should have been able to pick 28 00:01:46,160 --> 00:01:50,000 Speaker 1: up on. And yet somehow we have this suer delusion 29 00:01:50,080 --> 00:01:52,920 Speaker 1: where nobody sees it, or maybe people who saw it 30 00:01:53,240 --> 00:01:56,400 Speaker 1: decided that it wasn't worth pointing it out, and then 31 00:01:56,520 --> 00:01:59,920 Speaker 1: we have to relearn human history and human behavior all 32 00:02:00,000 --> 00:02:03,240 Speaker 1: over again for the next time. Yeah, exactly. And there's 33 00:02:03,280 --> 00:02:06,880 Speaker 1: an example that springs immediately to mind, probably one of 34 00:02:06,920 --> 00:02:12,040 Speaker 1: the most famous recent financial frauds, Bernie made Off and 35 00:02:12,080 --> 00:02:16,120 Speaker 1: you remember, um, after he got arrested, people started circulating 36 00:02:16,400 --> 00:02:20,160 Speaker 1: charts that showed the historical performance of the fund that 37 00:02:20,240 --> 00:02:23,960 Speaker 1: we're coming from, uh, the made Off funds marketing materials, 38 00:02:23,960 --> 00:02:27,760 Speaker 1: and they were just a straight, smooth line pointing upwards. 39 00:02:27,800 --> 00:02:31,400 Speaker 1: And of course, in hindsight, everyone thought that was deeply, 40 00:02:31,480 --> 00:02:35,120 Speaker 1: deeply suspicious. But at the time, people some of them anyway, 41 00:02:35,440 --> 00:02:39,200 Speaker 1: seemed more than happy to just accept that as fact. Yes, 42 00:02:39,360 --> 00:02:42,080 Speaker 1: it's it's perfect example, because it's one of these things 43 00:02:42,080 --> 00:02:44,760 Speaker 1: that in hindsight, as you say, it's like, oh, of 44 00:02:44,800 --> 00:02:47,760 Speaker 1: course that could never have been realistic. Where was everyone 45 00:02:47,800 --> 00:02:51,400 Speaker 1: at the time saying how impossible it was. It's a 46 00:02:51,480 --> 00:02:55,040 Speaker 1: perfect example of how something happens and the exact same 47 00:02:55,080 --> 00:02:58,320 Speaker 1: fact goes from being oh, this is very impressive to oh, 48 00:02:58,360 --> 00:03:03,080 Speaker 1: this is obviously a scamp. Yeah. How quickly things change. Yeah, 49 00:03:03,120 --> 00:03:07,600 Speaker 1: So maybe we could avoid frauds in the future, or 50 00:03:07,720 --> 00:03:12,160 Speaker 1: spot them quicker if we really studied the patterns and 51 00:03:12,320 --> 00:03:17,400 Speaker 1: instituted some good practices about the weak spots in our 52 00:03:17,440 --> 00:03:21,800 Speaker 1: behavior and in organizational behavior that fraudsters like to exploit. 53 00:03:22,480 --> 00:03:24,919 Speaker 1: I think that would make a lot of sense. So 54 00:03:24,960 --> 00:03:27,600 Speaker 1: maybe we'll start on that road with our guest today. 55 00:03:28,160 --> 00:03:31,200 Speaker 1: His name is Dan Davies, and he is the author 56 00:03:31,320 --> 00:03:34,839 Speaker 1: of a new book called Lying for Money, How Legendary 57 00:03:34,920 --> 00:03:39,320 Speaker 1: Frauds Reveal the Workings of our World. And it is 58 00:03:39,360 --> 00:03:43,280 Speaker 1: a fascinating book, and it looks at lots of frauds 59 00:03:43,280 --> 00:03:47,280 Speaker 1: and historical patterns and how they come about. And uh, 60 00:03:47,360 --> 00:03:59,440 Speaker 1: Dan joined us. Now, Dan, thank you very much for 61 00:03:59,520 --> 00:04:02,880 Speaker 1: joining us, Thanks very much for having me Dan. Before 62 00:04:02,960 --> 00:04:06,840 Speaker 1: we get into the frauds and the patterns of fraudsters 63 00:04:06,960 --> 00:04:08,440 Speaker 1: and why we miss them and all this stuff that 64 00:04:08,480 --> 00:04:12,400 Speaker 1: we're talking about, let's just talk about your career. I 65 00:04:12,760 --> 00:04:15,040 Speaker 1: kind of think of you as someone who just knows 66 00:04:15,200 --> 00:04:20,560 Speaker 1: about everything, and you, uh, you can talk about political referendums, 67 00:04:20,600 --> 00:04:23,480 Speaker 1: and you can talk about technical things, and tell us 68 00:04:23,520 --> 00:04:27,920 Speaker 1: a little bit about your career background and why you 69 00:04:28,040 --> 00:04:30,920 Speaker 1: wanted to write a book on this topic. Sure, well, 70 00:04:31,080 --> 00:04:35,640 Speaker 1: I was basically acquisi analyst for fifteen years. I started 71 00:04:35,640 --> 00:04:38,480 Speaker 1: out at the Bank of England as a regulatory economist 72 00:04:38,520 --> 00:04:41,720 Speaker 1: and I used to be in the office next door 73 00:04:41,760 --> 00:04:44,880 Speaker 1: to the guy who was the elite supervisor of Barings 74 00:04:44,880 --> 00:04:48,080 Speaker 1: Bank at the time of the Nick Leason crisis, which 75 00:04:48,200 --> 00:04:50,799 Speaker 1: was kind of an interesting set of office traffics watch. 76 00:04:51,680 --> 00:04:54,400 Speaker 1: Then I was lucky enough, over the course of about 77 00:04:54,480 --> 00:04:58,200 Speaker 1: fifteen years in equity analysis to have a succession of 78 00:04:58,279 --> 00:05:03,080 Speaker 1: busses who allow old me to mess around looking at 79 00:05:03,240 --> 00:05:08,040 Speaker 1: quirky things rather than doing something about the usually disgraceful 80 00:05:08,120 --> 00:05:11,159 Speaker 1: quality of my own ex forecasts. And so, you know, 81 00:05:11,240 --> 00:05:13,240 Speaker 1: you pick up a little bit of everything because I 82 00:05:13,279 --> 00:05:17,760 Speaker 1: was covering the financial sector, and financials are relevant to 83 00:05:18,000 --> 00:05:19,960 Speaker 1: more or less anything you want to get interested in, 84 00:05:20,600 --> 00:05:23,960 Speaker 1: which was something that I took full advantage of. Then 85 00:05:24,000 --> 00:05:26,480 Speaker 1: about four or five years ago, I kind of felt 86 00:05:26,480 --> 00:05:28,480 Speaker 1: that I had had as much fun as it was 87 00:05:28,520 --> 00:05:32,440 Speaker 1: possible to have out of equity analysis, so I quit 88 00:05:32,839 --> 00:05:38,800 Speaker 1: and well started writing articles for newspapers and websites and 89 00:05:38,880 --> 00:05:42,440 Speaker 1: eventually started writing this book. And you know, frauds. It's 90 00:05:42,480 --> 00:05:46,880 Speaker 1: just really interesting because it's like where the system breaks down. 91 00:05:47,440 --> 00:05:49,760 Speaker 1: You know, you can study the economics all the time, 92 00:05:49,880 --> 00:05:53,640 Speaker 1: but if you're only studying successful companies and economies, then 93 00:05:53,680 --> 00:05:56,239 Speaker 1: it's like trying to learn about medicine by only studying 94 00:05:56,279 --> 00:06:00,840 Speaker 1: healthy people. Where you really see how the structure works 95 00:06:01,360 --> 00:06:04,240 Speaker 1: is in cases like bankruptcies and frauds and all these 96 00:06:04,320 --> 00:06:07,320 Speaker 1: nasty things that people don't like to think about, where 97 00:06:07,360 --> 00:06:10,240 Speaker 1: the whole system breaks down and where key assumptions turn 98 00:06:10,279 --> 00:06:13,440 Speaker 1: out not to be true. I like that description. You're 99 00:06:13,480 --> 00:06:17,160 Speaker 1: sort of probing the system for its weak spots. So 100 00:06:17,320 --> 00:06:19,400 Speaker 1: I'm just going to jump right into it because, as 101 00:06:19,680 --> 00:06:22,320 Speaker 1: Joe has already said, this is a topic that is 102 00:06:22,360 --> 00:06:26,039 Speaker 1: dear to my heart. So what's your favorite example of 103 00:06:26,080 --> 00:06:29,600 Speaker 1: fraud that you chronicle in your book. Oh, it kind 104 00:06:29,640 --> 00:06:33,160 Speaker 1: of changes every day, because the thing is that obviously 105 00:06:33,160 --> 00:06:35,120 Speaker 1: every day I'll come up with a new one that 106 00:06:35,160 --> 00:06:37,760 Speaker 1: I wish i'd put in the book but didn't, or 107 00:06:37,839 --> 00:06:40,320 Speaker 1: someone asks whether they're favorites in there, and I'm just 108 00:06:40,360 --> 00:06:42,480 Speaker 1: like to do half of my favorites aren't in there. 109 00:06:42,960 --> 00:06:46,120 Speaker 1: But I think the classic one is the Great salad 110 00:06:46,160 --> 00:06:49,600 Speaker 1: oil scam. It's just such a beauty. Yeah. I mean, 111 00:06:49,640 --> 00:06:54,080 Speaker 1: it's just basically oil floats on water. And because of 112 00:06:54,120 --> 00:06:58,920 Speaker 1: this fact, it is surprisingly difficult to tell the difference 113 00:06:59,520 --> 00:07:03,600 Speaker 1: between a tank full of valuable soybean oil and a 114 00:07:03,600 --> 00:07:07,000 Speaker 1: tank that is basically full of seawater with a few 115 00:07:07,000 --> 00:07:10,400 Speaker 1: gallons of soybean oil floating on top. And there was 116 00:07:10,440 --> 00:07:13,480 Speaker 1: a guy called Tino de Angelis who was the salad 117 00:07:13,520 --> 00:07:18,200 Speaker 1: oil king and really knew more about soybean markets than 118 00:07:18,400 --> 00:07:21,280 Speaker 1: anyone else in the world at that time. But he 119 00:07:21,520 --> 00:07:25,040 Speaker 1: was an absolute crook, and one of the ways that 120 00:07:25,120 --> 00:07:29,160 Speaker 1: he managed to extract fraugil in cash from his company, 121 00:07:29,200 --> 00:07:34,400 Speaker 1: American Crude Vegetable Oils, was to borrow money collateralized against 122 00:07:34,400 --> 00:07:39,360 Speaker 1: the soybean oil in his tanks, but to have vastly 123 00:07:39,440 --> 00:07:42,640 Speaker 1: more warehouse receipts, as the kind of short term ones 124 00:07:42,680 --> 00:07:47,800 Speaker 1: were called outstanding, than there was soybean oil cartalized against them. 125 00:07:47,840 --> 00:07:50,040 Speaker 1: He was a hell of a character. But what he 126 00:07:50,120 --> 00:07:53,520 Speaker 1: used to do was let the lenders come out take 127 00:07:53,520 --> 00:07:56,120 Speaker 1: a dip sample into one of his tanks, and it 128 00:07:56,240 --> 00:07:59,080 Speaker 1: either had oil floating on water, or there was a 129 00:07:59,160 --> 00:08:02,320 Speaker 1: length of pipe welded into its full of soybean oil 130 00:08:02,360 --> 00:08:04,600 Speaker 1: and the rest of the tank was empty, or they 131 00:08:04,640 --> 00:08:07,360 Speaker 1: even had a plumbing system to pump it around so 132 00:08:07,440 --> 00:08:11,120 Speaker 1: that the same soybean oil could appear in different tanks. 133 00:08:11,200 --> 00:08:13,200 Speaker 1: In the end, he was claiming to have more in 134 00:08:13,240 --> 00:08:16,200 Speaker 1: his one tank form than the USA produced in an 135 00:08:16,320 --> 00:08:23,280 Speaker 1: entire year. Now, obviously there's a scientific lesson here that okay, 136 00:08:23,400 --> 00:08:27,080 Speaker 1: oil floats on water, and just for various technical reasons, 137 00:08:27,200 --> 00:08:30,880 Speaker 1: it's difficult to verify that a big tanker is actually 138 00:08:30,920 --> 00:08:34,679 Speaker 1: filled with the soybean oil that the owner claims it is. 139 00:08:35,440 --> 00:08:38,520 Speaker 1: What is the sort of human lesson though? So what 140 00:08:38,679 --> 00:08:44,120 Speaker 1: did he exploit with regards to human systems that allowed 141 00:08:44,200 --> 00:08:49,000 Speaker 1: him to perpetrate this highly profitable scam. Well, yeah, that's it. 142 00:08:49,000 --> 00:08:51,280 Speaker 1: It is all about the human systems. Because at the 143 00:08:51,360 --> 00:08:54,200 Speaker 1: end of the day, if it hadn't been that, he'd 144 00:08:54,240 --> 00:08:56,240 Speaker 1: have thought of something else. That was just the kind 145 00:08:56,240 --> 00:08:59,800 Speaker 1: of guy he was. At the time, American Express was 146 00:08:59,840 --> 00:09:03,320 Speaker 1: an independent financial institution rather than just a credit card brand, 147 00:09:03,760 --> 00:09:07,480 Speaker 1: and it had a brand new corporate lending business, and 148 00:09:07,640 --> 00:09:10,280 Speaker 1: it had a set of targets whereby every one of 149 00:09:10,320 --> 00:09:14,200 Speaker 1: its divisions had to make at least a million dollars 150 00:09:14,200 --> 00:09:16,880 Speaker 1: worth of profit every quarter, back in the days when 151 00:09:16,880 --> 00:09:19,240 Speaker 1: that was a lot of money. And so there were 152 00:09:19,280 --> 00:09:23,600 Speaker 1: people out there looking to gain market share rapidly, and 153 00:09:23,640 --> 00:09:26,800 Speaker 1: if you're looking to gain market share rapidly, you tend 154 00:09:26,960 --> 00:09:29,320 Speaker 1: to not be choosing off about the kind of customers 155 00:09:29,400 --> 00:09:32,760 Speaker 1: that you take on. Now, everyone wanted to believe him 156 00:09:32,800 --> 00:09:36,240 Speaker 1: he was a larger than life character. He actually used 157 00:09:36,280 --> 00:09:41,400 Speaker 1: to allow rumors to circulate that, you know, D'Angelis was 158 00:09:41,520 --> 00:09:44,760 Speaker 1: in the mafia because he felt like, you know, he wasn't, 159 00:09:45,080 --> 00:09:47,760 Speaker 1: but he felt like if people believed that, then they 160 00:09:47,760 --> 00:09:49,959 Speaker 1: would think that he had extra sources of cash flows 161 00:09:49,960 --> 00:09:54,160 Speaker 1: and paid back his loans. And so, really, the thing 162 00:09:54,200 --> 00:09:56,079 Speaker 1: that I keep on coming back to in these things 163 00:09:56,360 --> 00:10:01,719 Speaker 1: is it's surprisingly difficult to challenge someone who's really determined 164 00:10:02,400 --> 00:10:05,960 Speaker 1: to be a big time fraudster. If you've got someone 165 00:10:06,000 --> 00:10:11,600 Speaker 1: who's really got into running a dishonest business, then actually 166 00:10:12,080 --> 00:10:14,800 Speaker 1: they're going to design their scam around all the controls 167 00:10:14,840 --> 00:10:18,000 Speaker 1: that you have, and they're going to look like a 168 00:10:18,080 --> 00:10:21,400 Speaker 1: really successful operator. And you know, as we know from 169 00:10:21,840 --> 00:10:24,959 Speaker 1: the dot com era, from all sorts of examples from 170 00:10:24,960 --> 00:10:28,880 Speaker 1: the very recent past, if something's a success story, it's 171 00:10:29,080 --> 00:10:34,840 Speaker 1: very psychologically, sociologically, and institutionally difficult to be the person 172 00:10:35,000 --> 00:10:36,920 Speaker 1: who stands up to that guy. You know, It's the 173 00:10:36,920 --> 00:10:40,280 Speaker 1: whole Emperor's New Clothes phenomenon. So I'm curious from the 174 00:10:40,400 --> 00:10:45,479 Speaker 1: fraudsters perspective. I mean, there's clearly a lot of intellectual 175 00:10:45,880 --> 00:10:48,720 Speaker 1: ingenuity that goes into a scam like the one that 176 00:10:48,800 --> 00:10:52,760 Speaker 1: you just described. Why do you think they sort of 177 00:10:52,880 --> 00:10:57,640 Speaker 1: channel their creative energies towards fraud and scamming people rather 178 00:10:57,720 --> 00:11:01,960 Speaker 1: than actually working within the system to create a legitimate business. 179 00:11:03,000 --> 00:11:06,960 Speaker 1: It's very weird, actually, Tracy. I mean, I'm not sure 180 00:11:06,360 --> 00:11:10,760 Speaker 1: I understand why these people do this. I think the 181 00:11:11,120 --> 00:11:14,040 Speaker 1: classic model is what they call the fraud triangle, Like 182 00:11:14,240 --> 00:11:18,480 Speaker 1: you have the murder triangle of means, motive and opportunity 183 00:11:18,720 --> 00:11:21,720 Speaker 1: for fraud. What it seems to be is that you 184 00:11:21,800 --> 00:11:25,840 Speaker 1: have an opportunity which is a deficient control or a 185 00:11:25,840 --> 00:11:30,280 Speaker 1: way around the existing control systems. You have a mead, 186 00:11:31,000 --> 00:11:33,439 Speaker 1: which is just someone you know. As I say in 187 00:11:33,480 --> 00:11:36,160 Speaker 1: the book, bankers in the end steal for the same 188 00:11:36,200 --> 00:11:38,640 Speaker 1: reason that heroin addicts do. They've been put in a 189 00:11:38,679 --> 00:11:40,880 Speaker 1: position where they have to get hold of more money 190 00:11:40,880 --> 00:11:43,240 Speaker 1: than they can get hold of immediately by honest means. 191 00:11:44,120 --> 00:11:48,520 Speaker 1: And then you have what Kressy called a rationalization, because 192 00:11:48,559 --> 00:11:52,720 Speaker 1: the interesting thing about these fraudsters is in general, they 193 00:11:52,760 --> 00:11:57,120 Speaker 1: don't regard themselves as deviance individuals in the way that 194 00:11:57,360 --> 00:12:02,440 Speaker 1: other criminals basically do. Are read about two dozen autobiographies 195 00:12:02,440 --> 00:12:05,240 Speaker 1: of people who had been convicted of famous frauds, and 196 00:12:05,320 --> 00:12:08,760 Speaker 1: the absolute constant refrain through all of them is that 197 00:12:08,840 --> 00:12:12,360 Speaker 1: it wasn't really my fault. They always find some way 198 00:12:12,440 --> 00:12:16,679 Speaker 1: of distancing themselves and putting a psychological barrier between their 199 00:12:16,720 --> 00:12:20,760 Speaker 1: self image as basically an honest and successful business person 200 00:12:21,200 --> 00:12:24,280 Speaker 1: and the reality of what they're doing. So even to 201 00:12:24,320 --> 00:12:27,600 Speaker 1: you know, the Angelus, who was clearly stealing money from 202 00:12:27,640 --> 00:12:32,800 Speaker 1: his company, kept on rationalizing it that all he was 203 00:12:32,880 --> 00:12:37,439 Speaker 1: doing was borrowing money in order to finance later transactions. 204 00:12:37,760 --> 00:12:40,800 Speaker 1: There's usually some fantasy that there's going to be a 205 00:12:40,800 --> 00:12:42,920 Speaker 1: big score at the end of it which will allow 206 00:12:43,000 --> 00:12:46,240 Speaker 1: them to pay everything back and make everything right as 207 00:12:46,240 --> 00:12:49,000 Speaker 1: if the fraud had never happened, and then we only 208 00:12:49,000 --> 00:12:53,160 Speaker 1: find out about them when they collapse. One thing that 209 00:12:53,240 --> 00:12:56,600 Speaker 1: interests me is how many nick Leason's there are out 210 00:12:56,640 --> 00:12:59,480 Speaker 1: there who actually did happen to have a good day 211 00:12:59,480 --> 00:13:01,319 Speaker 1: on the market and managed to make cool of the 212 00:13:01,440 --> 00:13:04,840 Speaker 1: hitting account's balance. Daniel said something that I thought was 213 00:13:04,960 --> 00:13:08,360 Speaker 1: very funny when you said that this character, you know, de' 214 00:13:08,440 --> 00:13:12,560 Speaker 1: angelis like to have the rumor out there that he 215 00:13:12,800 --> 00:13:16,160 Speaker 1: was linked to the mafia even though he wasn't, because 216 00:13:16,160 --> 00:13:19,480 Speaker 1: it put the idea in prospective business partners had that 217 00:13:20,000 --> 00:13:22,680 Speaker 1: maybe had actually made him more credit worthy because he 218 00:13:22,720 --> 00:13:25,520 Speaker 1: had other sources of income coming in. It kind of 219 00:13:25,559 --> 00:13:30,120 Speaker 1: reminded me of with made Off. Weren't there people who 220 00:13:30,360 --> 00:13:34,559 Speaker 1: suspected something was amiss, but that they thought it was 221 00:13:34,600 --> 00:13:36,800 Speaker 1: something else, so they thought, oh, maybe he's doing some 222 00:13:36,920 --> 00:13:41,400 Speaker 1: sort of insider trading or doing something else that sort 223 00:13:41,400 --> 00:13:44,480 Speaker 1: of scam me with the uh, you know, market maker 224 00:13:44,559 --> 00:13:48,319 Speaker 1: side of his company. And although you'd think that should 225 00:13:48,360 --> 00:13:50,960 Speaker 1: be a red flag to anyone that actually made them 226 00:13:50,960 --> 00:13:54,920 Speaker 1: more confident that he would have the money to keep 227 00:13:54,960 --> 00:13:57,800 Speaker 1: supporting his fund. Yeah. Yeah, yeah, quite a lot of 228 00:13:57,800 --> 00:14:01,439 Speaker 1: people thought exactly. Thus, also with some Israel that you 229 00:14:01,600 --> 00:14:05,440 Speaker 1: capital and some Israel actually wasn't inside its radiator as 230 00:14:05,440 --> 00:14:10,640 Speaker 1: well as a conci scheme operator. But yeah, people thoughts, well, 231 00:14:10,760 --> 00:14:13,880 Speaker 1: he's a crook, but he's not necessarily going to scam me, 232 00:14:14,760 --> 00:14:18,000 Speaker 1: which turns out usually to be quite about way to 233 00:14:18,360 --> 00:14:24,640 Speaker 1: run your relationships. So how can organizations, I mean, you've 234 00:14:24,640 --> 00:14:30,160 Speaker 1: identified a couple of obvious weaknesses or blind spots that 235 00:14:30,240 --> 00:14:33,960 Speaker 1: happened when organizations they set out a specific target for 236 00:14:34,160 --> 00:14:38,040 Speaker 1: making money or market share that causes them to drop 237 00:14:38,080 --> 00:14:41,520 Speaker 1: their guard. They think, well, I'm I'm okay getting into 238 00:14:41,560 --> 00:14:43,840 Speaker 1: business with the scammer because I'm not the one being 239 00:14:44,080 --> 00:14:49,920 Speaker 1: scammed here. What are some practices that companies can actually 240 00:14:50,000 --> 00:14:53,120 Speaker 1: implement to sort of guard against these sort of obvious 241 00:14:53,400 --> 00:14:56,840 Speaker 1: human failures. Well, I'm going to give two answers to that, 242 00:14:56,880 --> 00:14:59,680 Speaker 1: I mean, directly answering the question. I think it comes 243 00:14:59,680 --> 00:15:03,920 Speaker 1: back to the fraud triangle. You want to avoid creating 244 00:15:03,920 --> 00:15:08,840 Speaker 1: a need so you don't want to create situations in 245 00:15:08,880 --> 00:15:12,680 Speaker 1: which you're setting unrealistic targets for your staff, because if 246 00:15:12,720 --> 00:15:15,360 Speaker 1: you're setting on realistic targets, then you're putting them in 247 00:15:15,360 --> 00:15:19,520 Speaker 1: a position where they can't do things legitimately, and that's 248 00:15:19,520 --> 00:15:23,720 Speaker 1: where you're getting what they call criminergenic in sensuves so 249 00:15:23,840 --> 00:15:29,160 Speaker 1: in sense of structurally making people more likely to commit crimes. 250 00:15:30,080 --> 00:15:34,920 Speaker 1: You can then look at the opportunity side of the triangle, 251 00:15:35,400 --> 00:15:38,120 Speaker 1: and an opportunity to commit fraud is just a weakness 252 00:15:38,120 --> 00:15:41,840 Speaker 1: in controls. As we say in the book, every time 253 00:15:41,960 --> 00:15:44,800 Speaker 1: you decide what you're going to check up on, you 254 00:15:44,840 --> 00:15:47,520 Speaker 1: are also deciding what you're not going to check up on. 255 00:15:48,000 --> 00:15:50,200 Speaker 1: And that's how the fraud gets into the system, because 256 00:15:50,240 --> 00:15:54,000 Speaker 1: you simply can't check up on everything. And so, you know, 257 00:15:54,160 --> 00:15:58,800 Speaker 1: for catching the bulk of frauds, it's really you know, 258 00:15:58,920 --> 00:16:02,680 Speaker 1: a like a return except it's say fraud return trade off, 259 00:16:03,040 --> 00:16:06,320 Speaker 1: where you start beefing up the controls until you think 260 00:16:06,360 --> 00:16:09,800 Speaker 1: that the marginal value of the frauds you're preventing is 261 00:16:09,840 --> 00:16:14,360 Speaker 1: equal to the marginal cost of doing so. Having said that, 262 00:16:14,360 --> 00:16:18,320 Speaker 1: that's a really difficult thing to investigate because the definition 263 00:16:18,320 --> 00:16:21,200 Speaker 1: of a fraud is it's something that happens outside your 264 00:16:21,240 --> 00:16:26,440 Speaker 1: normal manufacturements information systems, so you can really badly get 265 00:16:26,480 --> 00:16:29,320 Speaker 1: that optimization calculation wrong. In the classic example of that 266 00:16:29,360 --> 00:16:34,920 Speaker 1: is Medicare, where in the end, credible people like non 267 00:16:34,960 --> 00:16:40,960 Speaker 1: politically say that in the nighties, between twenty and a 268 00:16:41,040 --> 00:16:45,840 Speaker 1: third of all payments made under the Medicare system were fraudulent, 269 00:16:46,680 --> 00:16:49,880 Speaker 1: which was probably hundreds of billions of dollars, probably the 270 00:16:49,880 --> 00:16:52,640 Speaker 1: biggest fraud until the financial sector took the title back. 271 00:16:53,480 --> 00:16:56,400 Speaker 1: And what was going on there is that it was 272 00:16:56,600 --> 00:17:00,800 Speaker 1: absolutely assumed that the main cost problem in Medicare was 273 00:17:00,960 --> 00:17:04,040 Speaker 1: over treatments, and that the main driver of over treatment 274 00:17:04,359 --> 00:17:07,439 Speaker 1: was defensive medicine from doctors who are scared of getting sued, 275 00:17:08,400 --> 00:17:12,320 Speaker 1: and so they had systems that were meant to catch 276 00:17:12,720 --> 00:17:18,919 Speaker 1: unusual treatments but minimize otherwise the cost per claim processed. 277 00:17:19,359 --> 00:17:21,560 Speaker 1: And so what they built was a system that couldn't 278 00:17:22,000 --> 00:17:26,919 Speaker 1: detect four thousand identical hip replacements coming in from a 279 00:17:26,960 --> 00:17:30,240 Speaker 1: clinic in Florida that just simply didn't exist because it 280 00:17:30,280 --> 00:17:33,480 Speaker 1: was a paper creation of some fraudsters. So you can 281 00:17:33,560 --> 00:17:37,639 Speaker 1: think about things like that. On the other hand, the 282 00:17:37,680 --> 00:17:40,000 Speaker 1: second answer I'd give is almost to turn around the 283 00:17:40,080 --> 00:17:44,120 Speaker 1: question and say, are you sure that minimizing your exposure 284 00:17:44,160 --> 00:17:45,960 Speaker 1: to fraud risk is actually what you want to do, 285 00:17:46,920 --> 00:17:50,080 Speaker 1: because you know, the question is do you want to 286 00:17:50,280 --> 00:17:52,920 Speaker 1: make yourself bulletproof against fraud or do you want to 287 00:17:52,960 --> 00:17:58,320 Speaker 1: get rich? And actually, it turns out, as far as 288 00:17:58,320 --> 00:18:02,600 Speaker 1: I can sell, the optimum level of fraud is certainly 289 00:18:02,640 --> 00:18:05,800 Speaker 1: not zero, and it could be very high. We could 290 00:18:05,800 --> 00:18:09,040 Speaker 1: look in Silicon Valley and take a look at the 291 00:18:09,040 --> 00:18:13,520 Speaker 1: Frans case and Elizabeth Holmes and say, well, this is 292 00:18:13,520 --> 00:18:18,160 Speaker 1: pretty easy. You avoid a thranos by really checking out 293 00:18:18,520 --> 00:18:23,520 Speaker 1: all your tech demos and then not doing any business 294 00:18:23,560 --> 00:18:27,159 Speaker 1: at all with people who fake their demos, which is 295 00:18:27,200 --> 00:18:30,920 Speaker 1: a great way of avoiding frauds. Unfortunately, it also means 296 00:18:30,960 --> 00:18:35,880 Speaker 1: that you miss investing in Oracle because Larry Ellison totally 297 00:18:35,880 --> 00:18:40,600 Speaker 1: fake demos at crucial elite stages. It means you probably 298 00:18:40,720 --> 00:18:43,840 Speaker 1: miss investing in Apple around the time of the iPhone 299 00:18:44,240 --> 00:18:48,399 Speaker 1: because some of those demos there were definite differences between 300 00:18:48,400 --> 00:18:50,119 Speaker 1: what was happening on the big screen and what was 301 00:18:50,160 --> 00:18:54,880 Speaker 1: being sent from the device. In general, a system that's 302 00:18:54,880 --> 00:18:58,320 Speaker 1: set up to eliminate fraud risk is going to eliminate 303 00:18:58,560 --> 00:19:01,880 Speaker 1: so much legit some business that I kind of find 304 00:19:01,920 --> 00:19:04,920 Speaker 1: myself wondering if what you should be looking for is 305 00:19:04,960 --> 00:19:07,280 Speaker 1: almost a rule of thumb to stop you trusting too 306 00:19:07,280 --> 00:19:10,920 Speaker 1: little rather than trusting too much. H Um. I want 307 00:19:10,960 --> 00:19:13,240 Speaker 1: to press you on this point because it's a really 308 00:19:13,280 --> 00:19:16,520 Speaker 1: interesting one. And you know, if you read another very 309 00:19:16,520 --> 00:19:19,840 Speaker 1: good book, Bad Blood, about the Sarin Nos case, one 310 00:19:19,840 --> 00:19:22,320 Speaker 1: thing that comes out is that Elizabeth Holmes was sort 311 00:19:22,359 --> 00:19:26,480 Speaker 1: of pursuing strategies that she thought had been pursued by 312 00:19:26,560 --> 00:19:30,159 Speaker 1: Apple and Microsoft before, so sort of a fake it 313 00:19:30,200 --> 00:19:33,440 Speaker 1: till you make it approach. How many of the frauds 314 00:19:33,480 --> 00:19:38,520 Speaker 1: that you look at actually start out as legitimate business activities, 315 00:19:38,560 --> 00:19:42,840 Speaker 1: maybe even promising business activities, and then encounter some trouble 316 00:19:43,000 --> 00:19:47,480 Speaker 1: and then turn into actual frauds. I think it's about 317 00:19:47,600 --> 00:19:50,520 Speaker 1: fifty fifty between that sort of thing of being a 318 00:19:50,560 --> 00:19:54,120 Speaker 1: legitimate business that drifts and something that was clearly set 319 00:19:54,240 --> 00:19:56,600 Speaker 1: up as a fraud from day one. I mean, the 320 00:19:56,720 --> 00:19:59,960 Speaker 1: interesting thing about the whole Soranus and the fake it's 321 00:20:00,080 --> 00:20:03,639 Speaker 1: or you make it model is that it's a history 322 00:20:03,640 --> 00:20:08,719 Speaker 1: of that comes from mining fraud. Gold miners always thought 323 00:20:09,200 --> 00:20:12,200 Speaker 1: that there was a great big load just a few 324 00:20:12,280 --> 00:20:16,440 Speaker 1: feet away for digging rock, and that all they needed 325 00:20:16,960 --> 00:20:19,280 Speaker 1: was a little bit more investors cash in order to 326 00:20:20,520 --> 00:20:24,359 Speaker 1: achieve it. And that's how gold miners started getting into 327 00:20:24,359 --> 00:20:28,600 Speaker 1: the habit of faking essay results, which is a tradition 328 00:20:28,640 --> 00:20:30,639 Speaker 1: that goes way back to the first days of the 329 00:20:30,680 --> 00:20:35,800 Speaker 1: Californian gold Rush. And you get these things which start 330 00:20:35,840 --> 00:20:41,000 Speaker 1: out as having an honest intention, but then when you 331 00:20:41,080 --> 00:20:44,000 Speaker 1: realize that you can do that, there's some people who 332 00:20:44,000 --> 00:20:47,720 Speaker 1: realize that actually faking it until you make it is 333 00:20:47,760 --> 00:20:51,119 Speaker 1: one business model, but it's in many ways cheaper to 334 00:20:51,240 --> 00:20:55,359 Speaker 1: just start straight out by faking it. Dan, what is 335 00:20:55,560 --> 00:21:00,119 Speaker 1: the oldest type of pride? The very earliest one I 336 00:21:00,240 --> 00:21:07,240 Speaker 1: found as public procurement fraud. There is a public procurement 337 00:21:07,280 --> 00:21:11,040 Speaker 1: fraud in the Bible, and it's a case of skimming 338 00:21:11,119 --> 00:21:16,160 Speaker 1: profits of a maintenance contract in the Book of Kings 339 00:21:16,240 --> 00:21:22,919 Speaker 1: for anyone who's counting. And that's really because governments developed 340 00:21:23,200 --> 00:21:26,880 Speaker 1: earlier in the history of civilization than corporations did. And 341 00:21:27,160 --> 00:21:31,040 Speaker 1: what is actually quite interesting is that modern commercial fraud, 342 00:21:31,480 --> 00:21:36,240 Speaker 1: as opposed to just generally stealing by lying, is surprisingly modern. 343 00:21:36,560 --> 00:21:39,960 Speaker 1: You don't find that many of them before about eighteen hundred, 344 00:21:40,320 --> 00:21:42,080 Speaker 1: and the reason for that is that you don't find 345 00:21:42,160 --> 00:21:47,240 Speaker 1: that many recognizably capitalist enterprises before eighteen hundred. Actually, I 346 00:21:47,320 --> 00:21:50,440 Speaker 1: tell a lie because I've got my ancient history wrong. 347 00:21:51,119 --> 00:21:53,919 Speaker 1: The ancient Greeks had a loss of shipping fraud, and 348 00:21:54,000 --> 00:21:56,720 Speaker 1: that would presumably have predated the ev answer of the 349 00:21:56,720 --> 00:22:01,240 Speaker 1: Book of Kings. Getting a ship, pretending to load it 350 00:22:01,280 --> 00:22:05,280 Speaker 1: with a valuable cargo, scuppering it or hiding it, and 351 00:22:05,320 --> 00:22:08,440 Speaker 1: then telling your investors that they've lost their money. That's 352 00:22:08,480 --> 00:22:11,840 Speaker 1: probably the oldest form of fraud. Um. I have what 353 00:22:12,080 --> 00:22:14,720 Speaker 1: might be a strange question, but I'm going to ask 354 00:22:14,720 --> 00:22:18,200 Speaker 1: it anyway. Do you think as the world grows more 355 00:22:18,240 --> 00:22:23,560 Speaker 1: complex and as we get more regulation, more supervisors, you know, 356 00:22:23,840 --> 00:22:29,520 Speaker 1: big supervisory departments situated within companies, whether they're financial or 357 00:22:29,560 --> 00:22:33,199 Speaker 1: something else, do you think that makes the potential to 358 00:22:33,240 --> 00:22:37,880 Speaker 1: have frauds more likely or less likely? So, for instance, 359 00:22:39,080 --> 00:22:41,479 Speaker 1: the fact that we have insurers is what allows us 360 00:22:41,520 --> 00:22:46,640 Speaker 1: to have insurance frauds. Right, So do the opportunities expand 361 00:22:46,840 --> 00:22:49,720 Speaker 1: as we have more regulation and more supervision or do 362 00:22:49,760 --> 00:22:55,080 Speaker 1: they shrink. I think it's an arms race basically, so 363 00:22:55,359 --> 00:22:59,720 Speaker 1: locally they could be growing or shrinking. Globally, I think 364 00:22:59,720 --> 00:23:04,080 Speaker 1: they probably stay at a reasonably stable long term proportion 365 00:23:04,119 --> 00:23:07,720 Speaker 1: of the economy. I'm writing some more at the moments 366 00:23:07,720 --> 00:23:09,720 Speaker 1: because there's a U S edition of the book coming out, 367 00:23:10,520 --> 00:23:13,560 Speaker 1: and looking back through the book, I realized that it's 368 00:23:13,600 --> 00:23:16,399 Speaker 1: time after time that I'm talking about something that happened 369 00:23:16,400 --> 00:23:19,240 Speaker 1: in the a C. S or nineties and saying, of course, 370 00:23:19,320 --> 00:23:21,520 Speaker 1: you couldn't get away with this today because they changed 371 00:23:21,560 --> 00:23:24,760 Speaker 1: the rules. And it makes me worry that I'm portraying 372 00:23:24,800 --> 00:23:27,919 Speaker 1: this view of an almost whig version of history of fraud, 373 00:23:28,480 --> 00:23:31,240 Speaker 1: that all these bad people did bad things in the past, 374 00:23:31,400 --> 00:23:33,800 Speaker 1: but we got wise to them and we've shot that 375 00:23:34,880 --> 00:23:39,040 Speaker 1: particular loophole, and so that couldn't happen anymore. And then 376 00:23:39,080 --> 00:23:42,880 Speaker 1: you get something coming along like sub primal libel. So 377 00:23:43,600 --> 00:23:48,880 Speaker 1: what you get is technology and algorithms, I think are 378 00:23:49,080 --> 00:23:55,600 Speaker 1: very good at detecting the general run of fraud, and 379 00:23:55,640 --> 00:24:01,160 Speaker 1: you might actually even see genuine structural declines in just 380 00:24:01,760 --> 00:24:06,200 Speaker 1: simple credit card skimming, check kiting and fairly straightforward small 381 00:24:06,200 --> 00:24:10,960 Speaker 1: time stuff like that. But the nature of the really 382 00:24:11,040 --> 00:24:16,160 Speaker 1: big frauds is that they're designed around the systems. They're 383 00:24:16,200 --> 00:24:19,560 Speaker 1: designed around the guy who sets the algorithms in place, 384 00:24:19,960 --> 00:24:24,679 Speaker 1: and you have the whole company controlled by a either 385 00:24:24,760 --> 00:24:28,200 Speaker 1: by a fraudster or by a system of incentives that's 386 00:24:28,200 --> 00:24:30,160 Speaker 1: so bad that it might as well be a fraudster. 387 00:24:30,880 --> 00:24:34,320 Speaker 1: And the thing is that the amount of loss and 388 00:24:34,400 --> 00:24:38,639 Speaker 1: the amount of damage is just totally driven by the 389 00:24:38,760 --> 00:24:42,560 Speaker 1: very big frauds. If we consider something like payment protection 390 00:24:42,560 --> 00:24:47,320 Speaker 1: insurance in the UK, or libor or forex, every single 391 00:24:47,440 --> 00:24:50,200 Speaker 1: small time fraudster in the USA could work for five 392 00:24:50,280 --> 00:24:52,840 Speaker 1: years and not get to be the tenth part of 393 00:24:52,840 --> 00:24:56,720 Speaker 1: what the size of one of the really big financial scandals. 394 00:24:56,760 --> 00:25:01,280 Speaker 1: Someone like you know, d'angelus, the salad oil king, if 395 00:25:01,359 --> 00:25:05,280 Speaker 1: he were in his prime today, is he still alive? 396 00:25:05,800 --> 00:25:09,199 Speaker 1: He is still alive. Yes, he was arrested for another 397 00:25:09,200 --> 00:25:13,600 Speaker 1: fraud in the nine hasn't done anything since. That's if 398 00:25:13,640 --> 00:25:16,560 Speaker 1: you were in his prime today. Coming up with the 399 00:25:16,680 --> 00:25:21,400 Speaker 1: skills that enabled him to identify the selled oil opportunity, 400 00:25:21,400 --> 00:25:26,440 Speaker 1: in your view, lend themselves to spotting opportunities for fraud today. 401 00:25:27,200 --> 00:25:33,000 Speaker 1: Oh yeah, absolutely, because it's just picking. It's a combination 402 00:25:33,080 --> 00:25:37,040 Speaker 1: of a very deep understanding of one industry and the 403 00:25:37,080 --> 00:25:41,840 Speaker 1: way that it goes about. Because in general, if you 404 00:25:41,840 --> 00:25:44,639 Speaker 1: were able to defraud something, then you have exactly the 405 00:25:44,680 --> 00:25:47,000 Speaker 1: same knowledge as someone who's really good at managing it. 406 00:25:47,560 --> 00:25:50,679 Speaker 1: If Tina was around today, though he'd been doing initial 407 00:25:50,720 --> 00:25:53,960 Speaker 1: coin offerings. As far as I can see, with the 408 00:25:54,000 --> 00:25:56,680 Speaker 1: amount of money that I c o s are capable 409 00:25:56,720 --> 00:26:00,439 Speaker 1: of raising and the amounts of direct a site and 410 00:26:00,480 --> 00:26:03,680 Speaker 1: regulation that goes into them, anyone who's doing any other 411 00:26:03,760 --> 00:26:07,240 Speaker 1: kind of securities fraud today is just a damn fall. 412 00:26:08,920 --> 00:26:11,480 Speaker 1: That brings me to the sort of obvious question. To 413 00:26:11,640 --> 00:26:16,040 Speaker 1: identify initial coin offerings is sort of a ripe opportunity 414 00:26:16,080 --> 00:26:19,960 Speaker 1: for fraud. Are the rules or rules of thumb that 415 00:26:20,000 --> 00:26:23,119 Speaker 1: you could set up for look again an economy, looking 416 00:26:23,160 --> 00:26:26,920 Speaker 1: at a situation and saying, okay, this is the area 417 00:26:27,640 --> 00:26:29,960 Speaker 1: a generalizable rules to be able to say this is 418 00:26:30,000 --> 00:26:33,119 Speaker 1: the area where right now we're probably seeing a lot 419 00:26:33,160 --> 00:26:38,000 Speaker 1: of fraud. I think the big rule is exponential growth, 420 00:26:38,800 --> 00:26:43,680 Speaker 1: because the absolute signature of a fraud is that firstly 421 00:26:43,760 --> 00:26:47,480 Speaker 1: it has to grow as a compound rate because it's 422 00:26:47,520 --> 00:26:51,280 Speaker 1: based on a business units that's showing positive growth. And 423 00:26:51,320 --> 00:26:54,440 Speaker 1: then on top of that, you've got a wedge reflecting 424 00:26:54,520 --> 00:26:58,000 Speaker 1: the money removed by the fraudster, which also has to 425 00:26:58,040 --> 00:27:01,160 Speaker 1: show compound growth, but us you've got those two things 426 00:27:01,200 --> 00:27:06,400 Speaker 1: acting together. In general, a fraud has to grow unusually quickly, 427 00:27:06,800 --> 00:27:09,520 Speaker 1: which is why they always look like great, big success stories. 428 00:27:10,040 --> 00:27:12,000 Speaker 1: So in the book, I think the golden rule that 429 00:27:12,040 --> 00:27:18,919 Speaker 1: we suggest is if something's growing unusually quickly, then it 430 00:27:18,960 --> 00:27:21,560 Speaker 1: needs to be checked out. And then the second part 431 00:27:21,600 --> 00:27:24,040 Speaker 1: of the test is it needs to be checked out 432 00:27:24,240 --> 00:27:26,840 Speaker 1: in a way that it hasn't been checked out before, 433 00:27:27,720 --> 00:27:32,000 Speaker 1: because if it's a megafraud, then it's being designed around 434 00:27:32,240 --> 00:27:37,000 Speaker 1: all of the usual qualifications. And probably something I'd add 435 00:27:37,040 --> 00:27:40,440 Speaker 1: to that is if you try to check something out 436 00:27:40,760 --> 00:27:43,480 Speaker 1: and the person in charge won't give you any of 437 00:27:43,480 --> 00:27:46,480 Speaker 1: the information that you're looking for, then that ought to 438 00:27:46,520 --> 00:27:51,040 Speaker 1: be really suspicious. Well, Dan, that was really fascinating, and 439 00:27:51,400 --> 00:27:54,000 Speaker 1: I feel like I could keep asking you for more 440 00:27:54,000 --> 00:27:56,800 Speaker 1: examples of famous financial frauds, but then we'll be here 441 00:27:56,840 --> 00:27:59,800 Speaker 1: for about four hours, so we're gonna leave it there, 442 00:28:00,080 --> 00:28:04,080 Speaker 1: and we don't want to spoil the book. Yeah, and 443 00:28:04,160 --> 00:28:07,160 Speaker 1: you have the U S edition coming out right, Well, yes, 444 00:28:07,520 --> 00:28:11,840 Speaker 1: that's going to be coming out in Basically, we realized 445 00:28:11,880 --> 00:28:15,480 Speaker 1: with the US publisher Scritner, that in the book, I've 446 00:28:15,520 --> 00:28:20,399 Speaker 1: got value added tax payment, protection, insurance, Ronnie and Reggie Cray, 447 00:28:21,040 --> 00:28:24,720 Speaker 1: loads of kind of British things that despite the best 448 00:28:24,760 --> 00:28:28,399 Speaker 1: efforts of guy Richie, Americans don't care about. So I'm 449 00:28:28,440 --> 00:28:32,800 Speaker 1: rewriting that. Also, it turns out that Americans have slightly 450 00:28:32,840 --> 00:28:36,639 Speaker 1: stronger forearms, so they won't mind a book that's longer. 451 00:28:36,880 --> 00:28:38,800 Speaker 1: So I'm really excited about that working on that at 452 00:28:38,800 --> 00:28:41,600 Speaker 1: the moment. All Right, Dan Davis, thank you very much 453 00:28:41,600 --> 00:29:02,080 Speaker 1: for joining us. Thanks very much, well, Joe, you are 454 00:29:02,120 --> 00:29:05,400 Speaker 1: absolutely correct in your assessment. I did very much enjoy 455 00:29:05,480 --> 00:29:08,600 Speaker 1: that conversation, and I think the thing that I found 456 00:29:08,600 --> 00:29:12,680 Speaker 1: the most interesting is the concept of this idea that 457 00:29:12,720 --> 00:29:16,800 Speaker 1: there's actually a really fine line between genius, you know, 458 00:29:16,920 --> 00:29:19,920 Speaker 1: someone who sees an opening in the economy or in 459 00:29:19,960 --> 00:29:23,120 Speaker 1: the system to make a bunch of money legitimately, and 460 00:29:23,320 --> 00:29:28,560 Speaker 1: someone who pursues fraud and basically sees an opening in 461 00:29:28,560 --> 00:29:32,720 Speaker 1: the economy or in the system and pursues it illegitimately. So, 462 00:29:33,360 --> 00:29:36,680 Speaker 1: you know, Dan gave that example of Apple and Steve 463 00:29:36,800 --> 00:29:40,680 Speaker 1: Jobs and embellishing some of their reports early on in 464 00:29:40,720 --> 00:29:43,200 Speaker 1: their history, and of course you Jobs now is widely 465 00:29:43,280 --> 00:29:46,080 Speaker 1: lauded as a genius, but it could have turned out 466 00:29:46,240 --> 00:29:50,080 Speaker 1: so very differently. Yeah, I think that idea that to 467 00:29:50,480 --> 00:29:56,760 Speaker 1: commit fraud within an industry requires a deep, granular knowledge 468 00:29:56,920 --> 00:30:00,880 Speaker 1: of the industry itself is a really sendating one. And 469 00:30:00,960 --> 00:30:04,520 Speaker 1: you know, someone who just came to mind without regards 470 00:30:04,560 --> 00:30:09,800 Speaker 1: to this is Martin Schrilly and the farmo bro who's 471 00:30:09,880 --> 00:30:13,880 Speaker 1: currently in prison. But the thing is, he really does 472 00:30:14,040 --> 00:30:18,320 Speaker 1: know a lot about pharma, Like, he is extremely knowledgeable 473 00:30:18,800 --> 00:30:22,960 Speaker 1: about how the pharmaceutical industry works. And of course he's 474 00:30:23,040 --> 00:30:25,680 Speaker 1: argued that he was not a fraud stir and so on, 475 00:30:25,760 --> 00:30:29,560 Speaker 1: but regardless, he's someone with an unusually high level of 476 00:30:29,600 --> 00:30:33,600 Speaker 1: just the actual mechanics of how the business works. Yeah, 477 00:30:33,640 --> 00:30:36,240 Speaker 1: and there's plenty of dumb frauds out there, for sure, 478 00:30:36,280 --> 00:30:40,600 Speaker 1: but some of them are legitimately ingeniously crafted and you 479 00:30:40,720 --> 00:30:45,240 Speaker 1: just think, Wow, this person is clearly smart, clearly knows 480 00:30:45,280 --> 00:30:48,680 Speaker 1: the business, has clearly analyzed it, put the effort in 481 00:30:48,800 --> 00:30:52,640 Speaker 1: to find the holes in the system. Why couldn't they 482 00:30:52,720 --> 00:30:58,840 Speaker 1: have pursued, you know, a legitimate business absolutely and this, uh, 483 00:30:59,000 --> 00:31:01,720 Speaker 1: you know, this idea to that, if you're going to 484 00:31:01,840 --> 00:31:04,800 Speaker 1: catch a fraud, you better come up with some test 485 00:31:05,040 --> 00:31:09,160 Speaker 1: that's never been devised before or never been applied to 486 00:31:09,240 --> 00:31:12,960 Speaker 1: this company before, because if the fraud had gotten to 487 00:31:13,400 --> 00:31:15,880 Speaker 1: point X where you're thinking about it, it was almost 488 00:31:15,920 --> 00:31:21,040 Speaker 1: certainly designed to to fool the standard tests. Yeah, and 489 00:31:21,160 --> 00:31:23,959 Speaker 1: there's one other interesting thing down said, which was do 490 00:31:24,080 --> 00:31:27,360 Speaker 1: you want to aim to have no frauds ever in 491 00:31:27,400 --> 00:31:29,560 Speaker 1: your system? And you know, he pointed out to the 492 00:31:29,640 --> 00:31:33,160 Speaker 1: idea that a lot of frauds either end up as 493 00:31:33,240 --> 00:31:36,840 Speaker 1: legitimate businesses or end up creating a lot of wealth 494 00:31:36,920 --> 00:31:39,640 Speaker 1: for the people involved with them. And that kind of 495 00:31:39,680 --> 00:31:42,600 Speaker 1: goes back to some of the bubble episodes that we've had, Joe, Like, 496 00:31:42,920 --> 00:31:46,440 Speaker 1: clearly we all went insane when we thought beanie babies 497 00:31:46,480 --> 00:31:49,880 Speaker 1: were worth thousands and thousands of dollars, but it did 498 00:31:50,000 --> 00:31:52,240 Speaker 1: make a lot of people wealthy if they were able 499 00:31:52,280 --> 00:31:55,920 Speaker 1: to pull out at exactly the right time. Well, and furthermore, 500 00:31:56,240 --> 00:31:58,680 Speaker 1: this idea that if you were to construct a set 501 00:31:58,760 --> 00:32:02,880 Speaker 1: of behaviors is that to ensure that you never got 502 00:32:02,920 --> 00:32:05,680 Speaker 1: caught up in a fraud, you would have to also 503 00:32:06,440 --> 00:32:09,200 Speaker 1: guarantee that you miss a lot of things that don't 504 00:32:09,200 --> 00:32:11,080 Speaker 1: turn out to be frauds that turned out to be 505 00:32:11,600 --> 00:32:15,920 Speaker 1: wildly profitable. So you really sort of want to calibrate 506 00:32:15,960 --> 00:32:20,280 Speaker 1: your fraud detection level, your approach at a level that 507 00:32:20,520 --> 00:32:23,640 Speaker 1: maybe is per is not too onerous. Yeah, there's so 508 00:32:23,720 --> 00:32:26,800 Speaker 1: much to unpack in this entire topic. It's such a 509 00:32:26,800 --> 00:32:29,920 Speaker 1: good topic, which gets back to Dan's point in the 510 00:32:29,960 --> 00:32:34,080 Speaker 1: beginning that frauds are just a great entry point to 511 00:32:34,520 --> 00:32:38,920 Speaker 1: understanding business or human systems. Yeah, and human nature. Um, 512 00:32:38,920 --> 00:32:41,280 Speaker 1: but for the avoidance of doubt, this is the p 513 00:32:41,480 --> 00:32:44,160 Speaker 1: s a moment of odd lots. Don't do fraud, kids, 514 00:32:44,600 --> 00:32:48,960 Speaker 1: don't do it. This isn't an endorsement, I have to say, 515 00:32:50,120 --> 00:32:54,200 Speaker 1: so that, uh, this seemed to leb blurbed Dan's book 516 00:32:54,720 --> 00:32:57,600 Speaker 1: and his blurb is if you want to learn to 517 00:32:57,800 --> 00:33:00,360 Speaker 1: fend a fraud, read this, and if you want to 518 00:33:00,400 --> 00:33:04,760 Speaker 1: commit fraud, don't. But if you absolutely must, first read this, 519 00:33:05,360 --> 00:33:08,840 Speaker 1: which I thought was a pretty great blurb. Okay, all right, well, 520 00:33:09,040 --> 00:33:12,240 Speaker 1: this has been another edition of the All Thoughts podcast. 521 00:33:12,280 --> 00:33:15,040 Speaker 1: I'm Tracy Alloway. You can follow me on Twitter at 522 00:33:15,080 --> 00:33:18,360 Speaker 1: Tracy Alloway, and I'm Joe wise in't all. You could 523 00:33:18,440 --> 00:33:21,800 Speaker 1: follow me on Twitter at the Stalwart, and you could 524 00:33:21,800 --> 00:33:26,040 Speaker 1: follow our guest Dan Davies on Twitter at d Square Digest, 525 00:33:26,480 --> 00:33:29,719 Speaker 1: and you should follow our producer Topur foreheads on Twitter 526 00:33:29,880 --> 00:33:32,720 Speaker 1: at for hiss T, as well as the Bloomberg head 527 00:33:32,720 --> 00:33:37,680 Speaker 1: of podcasts, Francesca Levy at Francesca Today. Thanks for listening.