1 00:00:01,639 --> 00:00:03,720 Speaker 1: I got an email once a number of years ago 2 00:00:03,920 --> 00:00:07,200 Speaker 1: from somebody to say that they knew my password, and 3 00:00:07,200 --> 00:00:09,360 Speaker 1: I actually mentioned what my password was, which was quite 4 00:00:09,360 --> 00:00:11,720 Speaker 1: an old password. They said, I know this is your 5 00:00:11,760 --> 00:00:14,280 Speaker 1: password if you don't get in touch with me, and 6 00:00:14,360 --> 00:00:17,000 Speaker 1: pain that they would release videos ahead of me and 7 00:00:17,120 --> 00:00:20,319 Speaker 1: pictures that they'd taken from my webcam and I know 8 00:00:20,360 --> 00:00:22,480 Speaker 1: what you get up to in front of your webcam, 9 00:00:22,520 --> 00:00:25,960 Speaker 1: all that sort of you know, insinuations. But what worried 10 00:00:26,000 --> 00:00:28,720 Speaker 1: me initially was the fact that they had legitimate information 11 00:00:28,760 --> 00:00:31,720 Speaker 1: about me in some way. But I contacted the I 12 00:00:31,720 --> 00:00:34,199 Speaker 1: think I contacted the police actually and said to them, 13 00:00:34,240 --> 00:00:36,199 Speaker 1: you know, should I be worried about this? And they said, no, 14 00:00:36,280 --> 00:00:37,880 Speaker 1: we recognize that as a scam. 15 00:00:38,440 --> 00:00:41,240 Speaker 2: We hear all the time about people being fooled by 16 00:00:41,320 --> 00:00:45,080 Speaker 2: online scammers. You fall for a fake email or click 17 00:00:45,080 --> 00:00:48,919 Speaker 2: on a link that looks legit but isn't. I've done it, 18 00:00:49,080 --> 00:00:51,400 Speaker 2: even though I like to think of myself as world 19 00:00:51,479 --> 00:00:54,640 Speaker 2: weary enough to tell the difference. But even as we 20 00:00:54,760 --> 00:00:58,040 Speaker 2: all become more hardened against any kind of request for 21 00:00:58,160 --> 00:01:02,680 Speaker 2: our information, the scam are coming up with more sophisticated 22 00:01:02,680 --> 00:01:06,759 Speaker 2: ways to steal our identities. And our money, and no surprise, 23 00:01:06,840 --> 00:01:10,360 Speaker 2: they're now getting a big helping hand from you guessed it, 24 00:01:10,920 --> 00:01:16,320 Speaker 2: artificial intelligence. The financial industry is a prime target and 25 00:01:16,440 --> 00:01:21,520 Speaker 2: banks are struggling to protect customers. Bloomberg's Nabila Ahmed and 26 00:01:21,640 --> 00:01:25,200 Speaker 2: Adam Haig in Sydney have been reporting on how AI 27 00:01:25,440 --> 00:01:29,640 Speaker 2: is transforming financial crime and what we can do to 28 00:01:29,840 --> 00:01:31,319 Speaker 2: stay one step ahead. 29 00:01:31,840 --> 00:01:35,480 Speaker 3: They're running a business too, and they're going for maximum profit, 30 00:01:35,959 --> 00:01:40,000 Speaker 3: and unlike the banks, they're not bound by rules and regulations, 31 00:01:40,360 --> 00:01:43,800 Speaker 3: so they're able to move a lot faster. And that's 32 00:01:43,840 --> 00:01:46,280 Speaker 3: why the banks it seems like they're just a step 33 00:01:46,319 --> 00:01:47,680 Speaker 3: behind and trying to catch up. 34 00:01:48,320 --> 00:01:52,760 Speaker 4: This is an arms race of technological innovation, really, and 35 00:01:53,200 --> 00:01:56,160 Speaker 4: if the scammers are one step ahead with a technology, 36 00:01:56,840 --> 00:01:59,600 Speaker 4: then they're going to continue to keep winning. 37 00:02:05,400 --> 00:02:20,080 Speaker 2: I'm Wescasova today on the big take Artificial intelligence, Real Money, Adam. 38 00:02:20,360 --> 00:02:23,600 Speaker 2: In this story that you and Nobela rode with our 39 00:02:23,639 --> 00:02:28,720 Speaker 2: colleagues Ainsley Thompson and Ellie Harmsworth, you talk about how 40 00:02:29,120 --> 00:02:33,560 Speaker 2: artificial intelligence AI is changing the way people are falling 41 00:02:33,639 --> 00:02:37,960 Speaker 2: victim to scams, and you're talking your reporting about this 42 00:02:38,080 --> 00:02:41,640 Speaker 2: concept of social engineering scams. What exactly are those. 43 00:02:42,440 --> 00:02:44,520 Speaker 4: I think this is just kind of like another element 44 00:02:44,600 --> 00:02:48,440 Speaker 4: of how AI is gripping this change that we're seeing 45 00:02:48,480 --> 00:02:53,639 Speaker 4: in scams and forgulent payments. So social engineering is kind 46 00:02:53,639 --> 00:02:56,600 Speaker 4: of just another way to phrase what's happening in the 47 00:02:56,600 --> 00:03:00,560 Speaker 4: way that they're getting access to people through the multitudes 48 00:03:00,600 --> 00:03:02,680 Speaker 4: of different ways that they can do that. So one 49 00:03:02,720 --> 00:03:06,880 Speaker 4: of those is coming at you directly through Facebook and 50 00:03:06,960 --> 00:03:11,040 Speaker 4: WhatsApp and those kind of places. But really the next 51 00:03:11,120 --> 00:03:14,919 Speaker 4: level is how AI is being used to essentially replicate 52 00:03:15,160 --> 00:03:18,200 Speaker 4: people who may be close to you in your lives, 53 00:03:18,760 --> 00:03:22,560 Speaker 4: so they can pretend to be someone that you know, 54 00:03:23,120 --> 00:03:26,600 Speaker 4: and they can use that as a way to kind 55 00:03:26,639 --> 00:03:29,520 Speaker 4: of play on your emotions and be able to get 56 00:03:29,560 --> 00:03:33,920 Speaker 4: access to your money and your funds. Your identity essentially 57 00:03:34,120 --> 00:03:37,360 Speaker 4: feels like it's no longer safe, especially once you've been 58 00:03:37,640 --> 00:03:40,440 Speaker 4: out there either talking or you know, everyone has a 59 00:03:40,520 --> 00:03:44,440 Speaker 4: voicemail message that can be used to get a realistic 60 00:03:44,480 --> 00:03:48,000 Speaker 4: interpretation of what your voice is. As the example with 61 00:03:48,200 --> 00:03:51,160 Speaker 4: the fake masks and how they're used with the three 62 00:03:51,280 --> 00:03:54,280 Speaker 4: D printing to create fake IDs, like it shows that 63 00:03:54,680 --> 00:03:56,880 Speaker 4: you've just got so much information out there on the 64 00:03:57,400 --> 00:04:00,840 Speaker 4: Internet through all sorts of social media platforms that we 65 00:04:00,920 --> 00:04:04,040 Speaker 4: all use and everyone uses on a day to day basis. 66 00:04:05,840 --> 00:04:08,280 Speaker 2: Nibela. We've had phishing scams for a long time where 67 00:04:08,320 --> 00:04:11,840 Speaker 2: someone will pretend to be someone you know or somebody 68 00:04:11,960 --> 00:04:14,840 Speaker 2: in your email phone book if you can starts emailing 69 00:04:14,880 --> 00:04:16,719 Speaker 2: you and you kind of know it's not them. How 70 00:04:16,760 --> 00:04:17,719 Speaker 2: are these different? 71 00:04:18,480 --> 00:04:21,520 Speaker 3: The US Federal Trade Commission, which has been really trying 72 00:04:21,560 --> 00:04:24,680 Speaker 3: to look into this space and protect consumers, has been 73 00:04:24,720 --> 00:04:28,960 Speaker 3: saying that AI is being used to turbocharge fraud, and 74 00:04:29,160 --> 00:04:31,960 Speaker 3: that's what we're seeing here, is that artificial intelligence is 75 00:04:32,000 --> 00:04:36,080 Speaker 3: being used to clone people's voices to make it sound 76 00:04:36,240 --> 00:04:40,359 Speaker 3: like them. To really refine these attacks. In the case 77 00:04:40,440 --> 00:04:43,880 Speaker 3: of the ones where people are calling up, say I'm 78 00:04:43,920 --> 00:04:47,920 Speaker 3: calling you pretending to be your child, and I've actually 79 00:04:47,960 --> 00:04:52,080 Speaker 3: downloaded their voice from the internet, mostly from social media accounts. 80 00:04:52,080 --> 00:04:55,320 Speaker 3: A lot of people now have open public social media accounts, 81 00:04:55,320 --> 00:04:58,479 Speaker 3: so it's very easy to get snippets of people's voice. 82 00:04:58,520 --> 00:05:01,240 Speaker 3: They only need about twenty the thirty seconds, so it's 83 00:05:01,279 --> 00:05:03,680 Speaker 3: not very much, and they can put it through a 84 00:05:03,760 --> 00:05:06,640 Speaker 3: synthesizer that you can buy on the internet, or you 85 00:05:06,680 --> 00:05:09,960 Speaker 3: could even get free ones where it allows you to 86 00:05:10,040 --> 00:05:13,400 Speaker 3: clone that person's voice and gets it to say whatever 87 00:05:13,440 --> 00:05:14,320 Speaker 3: you want it to say. 88 00:05:14,800 --> 00:05:16,640 Speaker 5: So we've had cases where. 89 00:05:16,520 --> 00:05:19,599 Speaker 3: Children ringing up their parents asking for money or saying, 90 00:05:19,640 --> 00:05:22,599 Speaker 3: you know, I'm stark, I'm a school excursion, the bus 91 00:05:22,720 --> 00:05:25,320 Speaker 3: is broken down. I really need money to pay for 92 00:05:25,360 --> 00:05:27,960 Speaker 3: an uber or pay for a taxi whatever, Please. 93 00:05:27,760 --> 00:05:28,760 Speaker 5: Can I have that money? 94 00:05:29,279 --> 00:05:32,880 Speaker 3: And in that moment, when you hear your child in distress, 95 00:05:32,960 --> 00:05:36,919 Speaker 3: it's very hard for the target of these scams to 96 00:05:37,040 --> 00:05:40,479 Speaker 3: decipher and to stop and think, pause and think is 97 00:05:40,480 --> 00:05:41,560 Speaker 3: this really my child? 98 00:05:42,000 --> 00:05:44,960 Speaker 5: Are they really in trouble? What should I do here? 99 00:05:46,160 --> 00:05:48,880 Speaker 2: How widespread a problem is this? How many people are 100 00:05:48,920 --> 00:05:52,400 Speaker 2: getting scammed using these new technologies. 101 00:05:53,480 --> 00:05:55,920 Speaker 3: It's hard to tell how many are getting scammed using 102 00:05:55,920 --> 00:05:58,960 Speaker 3: the new technologies. We know that the number of people 103 00:05:59,000 --> 00:06:02,200 Speaker 3: getting scammed and that amount of money that people are 104 00:06:02,240 --> 00:06:05,560 Speaker 3: losing is just going up and up and up. For example, 105 00:06:05,560 --> 00:06:09,200 Speaker 3: in the US last year, consumers lost about eight point 106 00:06:09,279 --> 00:06:12,560 Speaker 3: eight billion dollars, which was up forty four percent from 107 00:06:12,600 --> 00:06:17,040 Speaker 3: the year before. Of those, the investment scams are some 108 00:06:17,080 --> 00:06:20,360 Speaker 3: of the fastest growing ones. But AI is being used 109 00:06:20,400 --> 00:06:24,120 Speaker 3: in all kinds of different scams, so it's really hard 110 00:06:24,160 --> 00:06:28,080 Speaker 3: to pass through exactly you know what percentage of scams 111 00:06:28,120 --> 00:06:30,039 Speaker 3: are using AI, But we just know that it's a 112 00:06:30,080 --> 00:06:33,560 Speaker 3: new tool that's available to fraudsters and they're using it 113 00:06:33,880 --> 00:06:36,160 Speaker 3: in more and more innovative ways. 114 00:06:36,480 --> 00:06:39,760 Speaker 2: What are some of the most popular scams, like ways 115 00:06:39,800 --> 00:06:42,040 Speaker 2: of extracting money from people. 116 00:06:42,760 --> 00:06:44,960 Speaker 3: So one really good example that we got from one 117 00:06:45,000 --> 00:06:47,360 Speaker 3: of the banks that we were talking to, they had 118 00:06:47,400 --> 00:06:50,440 Speaker 3: a customer who lost about seven hundred and fifty thousand 119 00:06:50,640 --> 00:06:51,840 Speaker 3: dollars through one. 120 00:06:51,800 --> 00:06:53,039 Speaker 5: Of these investment scams. 121 00:06:53,080 --> 00:06:55,520 Speaker 3: So this was a guy who had worked in finance, 122 00:06:55,880 --> 00:06:57,919 Speaker 3: who was coming towards the end of his career, and 123 00:06:58,000 --> 00:07:00,200 Speaker 3: he decided that he wanted to do some of his 124 00:07:00,240 --> 00:07:02,919 Speaker 3: own investments, and he took a lot of his money 125 00:07:02,920 --> 00:07:05,440 Speaker 3: and put them into a bond fund, you know, it's 126 00:07:05,480 --> 00:07:08,520 Speaker 3: meant to be safe, and it was pretty good yielding. 127 00:07:08,640 --> 00:07:12,080 Speaker 3: And he found this fund on the internet. So he 128 00:07:12,200 --> 00:07:15,160 Speaker 3: did a search engine search at Google Search and it 129 00:07:15,200 --> 00:07:19,000 Speaker 3: popped up with this particular investment company. Looked pretty legit, 130 00:07:19,400 --> 00:07:22,080 Speaker 3: and as he looked into it more and more. And 131 00:07:22,240 --> 00:07:24,560 Speaker 3: remember this is a guy who worked in finance himself, 132 00:07:24,600 --> 00:07:27,120 Speaker 3: so he's a little bit familiar with the language. So 133 00:07:27,520 --> 00:07:31,280 Speaker 3: he looked at the site they had regular prospectuses, they 134 00:07:31,360 --> 00:07:35,000 Speaker 3: had updates. They would send him quarterly updates talking about 135 00:07:35,000 --> 00:07:37,800 Speaker 3: how his investments were doing, and it was showing that 136 00:07:37,840 --> 00:07:40,520 Speaker 3: it was going really well. So he kept investing more 137 00:07:40,560 --> 00:07:43,080 Speaker 3: and more, and it was I think a year or 138 00:07:43,120 --> 00:07:46,840 Speaker 3: two later when he went to actually withdraw that money 139 00:07:46,880 --> 00:07:49,360 Speaker 3: and cash in on his profits that he realized that 140 00:07:49,440 --> 00:07:50,080 Speaker 3: it was all. 141 00:07:49,920 --> 00:07:55,240 Speaker 2: Fake and nobila. One of the ways where AI is 142 00:07:55,320 --> 00:07:58,720 Speaker 2: used for a financial fraud is to simply make investment 143 00:07:58,800 --> 00:08:01,000 Speaker 2: websites that look exactly like the real ones we use. 144 00:08:01,720 --> 00:08:05,080 Speaker 3: That's exactly right. You know, we're all familiar with chat GPT. 145 00:08:05,440 --> 00:08:08,640 Speaker 3: If you type into that that you want to have 146 00:08:08,680 --> 00:08:12,760 Speaker 3: a prospectus written for somebody that works in the financial 147 00:08:12,800 --> 00:08:16,480 Speaker 3: industry and write it from the perspective of a financial 148 00:08:16,520 --> 00:08:18,080 Speaker 3: investment firm, it. 149 00:08:18,040 --> 00:08:20,360 Speaker 5: Does a pretty good job. It actually does a pretty 150 00:08:20,360 --> 00:08:21,480 Speaker 5: scarily good job. 151 00:08:22,400 --> 00:08:25,960 Speaker 3: So AI is being used to really refine the language 152 00:08:26,400 --> 00:08:29,760 Speaker 3: and refine the look and feel of these prospectuses and 153 00:08:29,880 --> 00:08:34,439 Speaker 3: financial updates so that investors find it difficult to figure 154 00:08:34,480 --> 00:08:35,920 Speaker 3: out what's real and what's fake. 155 00:08:36,880 --> 00:08:39,840 Speaker 4: So what we're really seeing is that AI is being 156 00:08:40,040 --> 00:08:44,640 Speaker 4: used in many different areas of scams across the spectrum 157 00:08:44,720 --> 00:08:49,520 Speaker 4: which includes all sorts from romance scams to voice scams 158 00:08:49,520 --> 00:08:52,040 Speaker 4: where they take your voice and pretend to be someone else. 159 00:08:53,000 --> 00:08:56,839 Speaker 4: ID based scams where they're taking another form of ID 160 00:08:57,080 --> 00:09:00,200 Speaker 4: such as your face and pretending to be you. 161 00:09:00,880 --> 00:09:04,960 Speaker 2: And you talked about how much this is costing consumers. 162 00:09:05,760 --> 00:09:09,680 Speaker 2: What about the financial institutions that are also getting scammed 163 00:09:09,679 --> 00:09:11,240 Speaker 2: when all these accounts are being hacked. 164 00:09:12,200 --> 00:09:16,599 Speaker 3: More broadly, when you look at cybercrime costs, which includes scams, 165 00:09:16,880 --> 00:09:20,720 Speaker 3: they're set to hit eight trillion dollars this year, which 166 00:09:21,160 --> 00:09:23,720 Speaker 3: if it were a country, it would be the third 167 00:09:23,960 --> 00:09:28,160 Speaker 3: largest economy in the world today. By twenty twenty five, 168 00:09:28,720 --> 00:09:31,800 Speaker 3: the projections are that it will reach ten and a 169 00:09:31,840 --> 00:09:34,760 Speaker 3: half trillion dollars, which would mean that they would have 170 00:09:34,920 --> 00:09:38,560 Speaker 3: tripled in a decade. So there are some staggering numbers here. 171 00:09:38,559 --> 00:09:41,240 Speaker 3: And Microsoft put together some numbers for this story that 172 00:09:41,240 --> 00:09:44,400 Speaker 3: we're working on, and I'm just going to reel some 173 00:09:44,440 --> 00:09:47,960 Speaker 3: of them off here because they're just so incredible. Twenty 174 00:09:47,960 --> 00:09:50,559 Speaker 3: two billion is the number of records dolen by hackers 175 00:09:50,600 --> 00:09:55,000 Speaker 3: in twenty twenty two. Twelve one hundred and eighty seven 176 00:09:55,320 --> 00:09:59,440 Speaker 3: is a number of password attacks per second in the world, 177 00:09:59,679 --> 00:10:02,760 Speaker 3: just in about that. One thy two hundred and eighty 178 00:10:02,840 --> 00:10:07,000 Speaker 3: seven fifty six is the average number of days between 179 00:10:07,120 --> 00:10:12,079 Speaker 3: the time a scammer infiltrates your system and that infiltration 180 00:10:12,280 --> 00:10:16,319 Speaker 3: is detected. Seven to two is the minutes the median 181 00:10:16,440 --> 00:10:19,760 Speaker 3: time for an attacker to access your private data if 182 00:10:19,800 --> 00:10:24,040 Speaker 3: you fall victim to a phishing email. Five hundred thousand 183 00:10:25,000 --> 00:10:27,719 Speaker 3: is the number of new mailware variants that are being 184 00:10:27,760 --> 00:10:33,079 Speaker 3: created every day, and three point four million is a 185 00:10:33,120 --> 00:10:36,680 Speaker 3: global shortage of skilled security workers. So you can see 186 00:10:36,720 --> 00:10:38,720 Speaker 3: that there's an uphill battle here for sure. 187 00:10:40,400 --> 00:10:44,000 Speaker 2: After the break, who's responsible if you fall victim to 188 00:10:44,080 --> 00:10:44,720 Speaker 2: a scammer? 189 00:10:53,600 --> 00:10:57,480 Speaker 6: I was out on holiday in abi's Abi and I 190 00:10:57,600 --> 00:11:00,439 Speaker 6: got a notification on my phone that my car had 191 00:11:00,440 --> 00:11:02,240 Speaker 6: been used to try and book some flights with South 192 00:11:02,280 --> 00:11:05,000 Speaker 6: African Airways. Obviously, I thought that was a little bit 193 00:11:05,080 --> 00:11:07,120 Speaker 6: unusual given that I was on holiday, So they must 194 00:11:07,120 --> 00:11:08,800 Speaker 6: have taken a picture or someone who must have seen 195 00:11:08,800 --> 00:11:11,080 Speaker 6: my card details and then tried to plug it in online, 196 00:11:11,080 --> 00:11:13,760 Speaker 6: because when I called up my credit card company, it 197 00:11:13,840 --> 00:11:16,120 Speaker 6: was American Express. They were really good and they managed 198 00:11:16,120 --> 00:11:18,200 Speaker 6: to cancelorf and I wasn't charged or anything like that. 199 00:11:18,520 --> 00:11:20,720 Speaker 6: They were also tried to make a booking through booking 200 00:11:20,760 --> 00:11:23,840 Speaker 6: dot com or Trevago or something like that. They were 201 00:11:23,840 --> 00:11:26,160 Speaker 6: trying to get a hotel as well, and they didn't 202 00:11:26,200 --> 00:11:27,839 Speaker 6: manage to get through on that one. It was great 203 00:11:27,880 --> 00:11:29,640 Speaker 6: that I had this you know that feature on your 204 00:11:29,679 --> 00:11:32,520 Speaker 6: phone where it says when you purchased something. I immediately 205 00:11:32,559 --> 00:11:35,560 Speaker 6: called them and they sort it out straight away, Adam. 206 00:11:35,600 --> 00:11:37,199 Speaker 2: So now we know that a lot of people are 207 00:11:37,200 --> 00:11:39,840 Speaker 2: getting scammed and a lot of money is being the last. 208 00:11:40,400 --> 00:11:43,120 Speaker 2: So who pays for it when somebody loses a bunch 209 00:11:43,120 --> 00:11:44,679 Speaker 2: of money, who's on the hook? 210 00:11:45,520 --> 00:11:49,920 Speaker 4: Well, who exactly pays and who's responsible varies a lot 211 00:11:50,000 --> 00:11:53,520 Speaker 4: from country to country, but obviously no one wants to 212 00:11:53,559 --> 00:11:56,520 Speaker 4: take all of the hit hit. So basically what you 213 00:11:56,600 --> 00:12:00,719 Speaker 4: have is a situation where there's lots of discussions and 214 00:12:00,880 --> 00:12:04,480 Speaker 4: disagreement around where the responsibility lies. So it isn't a 215 00:12:04,480 --> 00:12:07,520 Speaker 4: clear cut thing. Obviously, if you've been victim of a 216 00:12:08,280 --> 00:12:11,160 Speaker 4: if you know someone's stolen your credit card, they've copied it, 217 00:12:11,480 --> 00:12:14,440 Speaker 4: the bank's pretty much going to give your money back. 218 00:12:14,440 --> 00:12:17,760 Speaker 4: In that kind of situation, where it becomes a bit 219 00:12:17,840 --> 00:12:21,560 Speaker 4: tricky is when you get tricked into making a payment 220 00:12:21,800 --> 00:12:24,800 Speaker 4: and that's where the responsibility is a little bit less clear. 221 00:12:24,880 --> 00:12:28,200 Speaker 4: So obviously, in a lot of scams, you are almost 222 00:12:28,280 --> 00:12:33,280 Speaker 4: by definition being tricked. So the difficulty then lies around 223 00:12:33,520 --> 00:12:37,360 Speaker 4: how the bank determines what's happened during the process. And 224 00:12:37,400 --> 00:12:40,520 Speaker 4: in places like the US and the UK, the onus 225 00:12:40,640 --> 00:12:46,400 Speaker 4: is moving on to banks taking more responsibility to reimburse 226 00:12:46,720 --> 00:12:49,839 Speaker 4: customers that have lost money through scams. But what we're 227 00:12:49,840 --> 00:12:52,120 Speaker 4: seeing is a big change in this area and a 228 00:12:52,160 --> 00:12:55,480 Speaker 4: lot of political capital now being spent on trying to 229 00:12:55,520 --> 00:13:00,880 Speaker 4: hold a bigger group of organizations responsible, so not just banks, 230 00:13:01,200 --> 00:13:04,400 Speaker 4: but also in some of these AI scams that are 231 00:13:04,440 --> 00:13:08,679 Speaker 4: involving tech platforms, for example, there's an argument now that 232 00:13:08,840 --> 00:13:11,920 Speaker 4: tech firms should have to stump up more for losses 233 00:13:11,920 --> 00:13:12,920 Speaker 4: in this area as well. 234 00:13:13,720 --> 00:13:16,800 Speaker 3: If you ask the banks, they'll tell you who's not paying, 235 00:13:17,320 --> 00:13:20,440 Speaker 3: and that's big Tech. And banks around the world are 236 00:13:20,520 --> 00:13:23,640 Speaker 3: very vexed about this because they want big Tech to 237 00:13:23,720 --> 00:13:27,360 Speaker 3: be held more responsible for some of these scams that 238 00:13:27,400 --> 00:13:30,760 Speaker 3: are being hosted on their sites. So Adam mentioned the 239 00:13:30,800 --> 00:13:36,360 Speaker 3: investment scams, but also fake websites for online shopping for example, 240 00:13:36,559 --> 00:13:40,880 Speaker 3: that are popping up, and telecom companies or others that 241 00:13:40,960 --> 00:13:44,679 Speaker 3: banks think should be doing more to help block numbers 242 00:13:44,720 --> 00:13:47,920 Speaker 3: that are sending those phishing techs for example, or those 243 00:13:47,920 --> 00:13:50,600 Speaker 3: messages that they send where they say you forgot to 244 00:13:50,640 --> 00:13:54,400 Speaker 3: pay your toll, please click this link. They want telecom 245 00:13:54,480 --> 00:13:58,560 Speaker 3: companies to disable those kinds of links and tackle the 246 00:13:58,559 --> 00:14:01,080 Speaker 3: problem at its route. 247 00:14:02,720 --> 00:14:06,240 Speaker 2: When are consumers on the hook to pay? 248 00:14:06,720 --> 00:14:10,600 Speaker 3: I mean, that's the big question, because consumers themselves. So far, 249 00:14:11,240 --> 00:14:13,520 Speaker 3: you know, we've been used to credit card. For example, 250 00:14:13,600 --> 00:14:16,720 Speaker 3: somebody steals a credit card, they use that to buy 251 00:14:17,240 --> 00:14:21,160 Speaker 3: I don't know, one thousand bubblegum machines or something somewhere, 252 00:14:21,200 --> 00:14:23,880 Speaker 3: and you know it wasn't you, and the bank says, okay, 253 00:14:23,960 --> 00:14:26,120 Speaker 3: you can have your money back because we know that 254 00:14:26,200 --> 00:14:30,240 Speaker 3: was fortunant. So credit card fraud is unauthorized fraud because 255 00:14:30,280 --> 00:14:33,000 Speaker 3: you haven't allowed that to happen. But right now, what 256 00:14:33,000 --> 00:14:36,640 Speaker 3: we're seeing globally is that authorized fraud is actually surpassing 257 00:14:36,880 --> 00:14:40,240 Speaker 3: unauthorized fraud. So that's where somebody has tricked you into 258 00:14:40,720 --> 00:14:44,440 Speaker 3: giving up your money voluntarily. And in that instance, you know, 259 00:14:44,600 --> 00:14:48,600 Speaker 3: so far, banks are still paying out to a large degree, 260 00:14:48,880 --> 00:14:51,440 Speaker 3: but at some point, I guess you know, they would 261 00:14:51,480 --> 00:14:55,560 Speaker 3: really like consumers to take more responsibility. But it's very, 262 00:14:55,680 --> 00:14:58,360 Speaker 3: very difficult because you know, we've been saying how AI 263 00:14:58,960 --> 00:15:02,920 Speaker 3: is making it so much harder to discern what is 264 00:15:03,000 --> 00:15:04,280 Speaker 3: real and what is fake. 265 00:15:05,480 --> 00:15:08,440 Speaker 2: Is it realistic to say that the tech companies should 266 00:15:08,600 --> 00:15:10,520 Speaker 2: police this? Do they have the tools to do this? 267 00:15:10,680 --> 00:15:12,280 Speaker 2: And they're simply not doing it? 268 00:15:13,200 --> 00:15:16,640 Speaker 4: Ultimately, this is about whether these firms want to spend 269 00:15:16,720 --> 00:15:20,040 Speaker 4: more money, more time weeding out what's real and what 270 00:15:20,160 --> 00:15:23,120 Speaker 4: isn't on their platforms, And of course they can do it. 271 00:15:23,160 --> 00:15:26,120 Speaker 4: There's varying degrees of how difficult that is to do it, 272 00:15:26,200 --> 00:15:29,880 Speaker 4: but it requires more time and more resources and more staff, 273 00:15:30,000 --> 00:15:33,200 Speaker 4: so ultimately it costs more money. So it comes down 274 00:15:33,440 --> 00:15:36,960 Speaker 4: to a simple equation on cost. Ultimately, if they want 275 00:15:37,000 --> 00:15:40,520 Speaker 4: to spend more of their dollars on getting a grip 276 00:15:40,560 --> 00:15:44,320 Speaker 4: on this, then that's one choice. The other choice is 277 00:15:44,800 --> 00:15:47,320 Speaker 4: not to spend as much time and money on this, 278 00:15:47,680 --> 00:15:49,680 Speaker 4: and that's another choice. 279 00:15:50,360 --> 00:16:03,680 Speaker 2: When we come back, what can we do to protect ourselves? Adam. 280 00:16:03,720 --> 00:16:05,800 Speaker 2: One thing you write about is that is the banks 281 00:16:05,840 --> 00:16:08,400 Speaker 2: try to keep up with the scammers. The scammers are 282 00:16:08,520 --> 00:16:11,000 Speaker 2: trying to stay one step ahead and change the way 283 00:16:11,040 --> 00:16:11,800 Speaker 2: they do things. 284 00:16:12,360 --> 00:16:16,000 Speaker 4: Yeah, there's a constant arms race going on here between 285 00:16:16,560 --> 00:16:19,600 Speaker 4: just a kind of keeping a competitive edge. So as 286 00:16:19,640 --> 00:16:23,840 Speaker 4: every kind of new technology evolves, of course you're trying 287 00:16:23,880 --> 00:16:26,160 Speaker 4: to catch up and you're trying to kind of prevent 288 00:16:26,480 --> 00:16:29,640 Speaker 4: the next thing from happening. But you're always catching up. 289 00:16:29,640 --> 00:16:32,280 Speaker 4: You're always trying to kind of stay one step ahead 290 00:16:32,280 --> 00:16:35,200 Speaker 4: of the game. But of course they're typically a step 291 00:16:35,320 --> 00:16:37,480 Speaker 4: or two behind the game, and the scammers are the 292 00:16:37,480 --> 00:16:42,440 Speaker 4: ones that a step ahead. So it's a constant effort 293 00:16:42,520 --> 00:16:46,240 Speaker 4: to try and to learn more, to understand what's going 294 00:16:46,240 --> 00:16:48,480 Speaker 4: on with the latest technology, and then to try and 295 00:16:48,560 --> 00:16:51,240 Speaker 4: have a plan to fight back against it. But it's 296 00:16:51,440 --> 00:16:54,240 Speaker 4: very clear from all the reporting that there's this sense 297 00:16:54,280 --> 00:16:58,600 Speaker 4: of skepticism and this sense that this isn't something that's winnable. 298 00:16:58,800 --> 00:17:03,200 Speaker 4: This is an arms race of technological innovation, really, and 299 00:17:03,680 --> 00:17:06,640 Speaker 4: if the scammers are one step ahead with a technology, 300 00:17:07,280 --> 00:17:10,040 Speaker 4: then they're going to continue to keep winning. 301 00:17:10,840 --> 00:17:13,000 Speaker 3: So there were four of us reporting on this story 302 00:17:13,280 --> 00:17:17,800 Speaker 3: around the world, and we all heard from our sources 303 00:17:18,240 --> 00:17:22,080 Speaker 3: that it's just like Adam says, it's an arms race 304 00:17:22,119 --> 00:17:26,080 Speaker 3: and it's hard to win, but also that they all 305 00:17:26,119 --> 00:17:30,040 Speaker 3: have to now start pulling their resources a lot more 306 00:17:30,320 --> 00:17:34,040 Speaker 3: banks and tech companies they all have to ban together 307 00:17:34,119 --> 00:17:37,359 Speaker 3: because they just can't be as nimble as these scammers. 308 00:17:37,560 --> 00:17:39,960 Speaker 3: You know, you shut down a scammer call center and 309 00:17:40,000 --> 00:17:41,200 Speaker 3: another one pops. 310 00:17:40,960 --> 00:17:42,480 Speaker 5: Up the next week. 311 00:17:42,880 --> 00:17:45,520 Speaker 3: They're like big businesses, right, So a lot of these 312 00:17:45,560 --> 00:17:48,639 Speaker 3: people we spoke to said, look, they're running a business too, 313 00:17:48,800 --> 00:17:52,440 Speaker 3: and they're going for maximum profit, and unlike the banks, 314 00:17:52,560 --> 00:17:56,000 Speaker 3: they're not bound by rules and regulations, so they're able 315 00:17:56,040 --> 00:17:59,600 Speaker 3: to move a lot faster. And that's why the banks 316 00:17:59,720 --> 00:18:02,120 Speaker 3: it's seems like they're just a step behind and trying 317 00:18:02,119 --> 00:18:02,720 Speaker 3: to catch up. 318 00:18:04,760 --> 00:18:07,960 Speaker 2: We touched a little bit on what governments are trying 319 00:18:08,000 --> 00:18:12,119 Speaker 2: to do to get their arms around this. Adam, what 320 00:18:12,280 --> 00:18:14,879 Speaker 2: kind of government regulation is in the works. 321 00:18:15,440 --> 00:18:18,000 Speaker 4: Well, I think really what you need to see from 322 00:18:18,359 --> 00:18:23,880 Speaker 4: governments is more of a coordinated action around how they're 323 00:18:23,920 --> 00:18:27,960 Speaker 4: trying to tackle this. But of course, governments can only 324 00:18:28,000 --> 00:18:31,760 Speaker 4: step in if the private sector is doing their utmost 325 00:18:32,119 --> 00:18:34,800 Speaker 4: in a coordinated way to try and tackle this problem. 326 00:18:35,240 --> 00:18:37,640 Speaker 4: And I think that's kind of what you're seeing now, 327 00:18:37,720 --> 00:18:41,160 Speaker 4: is that governments are trying to get the private sector 328 00:18:41,200 --> 00:18:44,280 Speaker 4: to talk to each other. They recognize that this spans 329 00:18:44,320 --> 00:18:47,800 Speaker 4: across different parts of the economy. It's not just banks, 330 00:18:47,880 --> 00:18:51,040 Speaker 4: or it's not just technology firms. It's a whole swathe 331 00:18:51,119 --> 00:18:54,080 Speaker 4: of different companies that need to share information about how 332 00:18:54,080 --> 00:18:56,879 Speaker 4: to tackle this together with governments if they're going to 333 00:18:56,920 --> 00:18:59,080 Speaker 4: really tackle this and crack down on it. 334 00:18:59,840 --> 00:19:02,000 Speaker 2: I can imagine a lot of people listening to this 335 00:19:02,080 --> 00:19:05,560 Speaker 2: podcast saying to themselves, oh, great, So if no one 336 00:19:05,600 --> 00:19:07,639 Speaker 2: seems to know how to stop this, how am I 337 00:19:07,680 --> 00:19:11,399 Speaker 2: supposed to protect myself from scammers? When the technology is 338 00:19:11,480 --> 00:19:12,840 Speaker 2: getting so good. 339 00:19:13,480 --> 00:19:15,800 Speaker 3: I think we all can be a lot more vigilant 340 00:19:16,560 --> 00:19:20,680 Speaker 3: and just tidier about how we handle ourselves on the internet. 341 00:19:21,119 --> 00:19:24,399 Speaker 3: Just make sure that before you authorize any kind of 342 00:19:24,440 --> 00:19:27,560 Speaker 3: payment to anybody that you don't know that is not 343 00:19:27,600 --> 00:19:30,879 Speaker 3: an immediate family member that's already in your log of 344 00:19:30,920 --> 00:19:34,199 Speaker 3: accounts that you do send money to, just stop and 345 00:19:34,320 --> 00:19:37,639 Speaker 3: check for a sec It's very very easy to fall 346 00:19:37,800 --> 00:19:41,000 Speaker 3: victim to one of these scams. It's very very easy 347 00:19:41,040 --> 00:19:44,040 Speaker 3: to click that phishing email. In fact, I did it 348 00:19:44,119 --> 00:19:46,399 Speaker 3: last week because I thought it was a work email, 349 00:19:46,440 --> 00:19:49,160 Speaker 3: and as I went to click it, I thought, hang 350 00:19:49,200 --> 00:19:53,240 Speaker 3: on a second, so I like shut down the site 351 00:19:53,359 --> 00:19:55,639 Speaker 3: before it loaded, but I did click on it. You know, 352 00:19:55,800 --> 00:19:58,600 Speaker 3: it's just really easy to fall victim, so we all 353 00:19:58,640 --> 00:20:00,600 Speaker 3: just have to be so hyper vigilant. 354 00:20:00,600 --> 00:20:01,920 Speaker 5: That's the best way to do it. 355 00:20:02,320 --> 00:20:06,159 Speaker 3: And set up as many of those kinds of checks 356 00:20:06,160 --> 00:20:08,560 Speaker 3: and balances for yourself as you can. You know, maybe 357 00:20:08,600 --> 00:20:12,080 Speaker 3: only transfer money to accounct are already vetted and that 358 00:20:12,160 --> 00:20:16,600 Speaker 3: you already have in your address book for example. You know, 359 00:20:17,000 --> 00:20:20,399 Speaker 3: be careful when you're dealing with crypto exchanges and sending 360 00:20:20,440 --> 00:20:21,360 Speaker 3: money over crypto. 361 00:20:22,520 --> 00:20:25,560 Speaker 2: Nobila Adam, thanks so much for coming on the show. 362 00:20:26,119 --> 00:20:27,639 Speaker 5: Thanks Wez, It's been really great. 363 00:20:27,960 --> 00:20:29,320 Speaker 4: Thanks has been great to be with you. 364 00:20:31,000 --> 00:20:32,920 Speaker 2: Thanks for listening to us here at The Big Take. 365 00:20:33,080 --> 00:20:36,480 Speaker 2: It's a daily podcast from Bloemberg and iHeartRadio. For more 366 00:20:36,520 --> 00:20:40,720 Speaker 2: shows from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or 367 00:20:40,760 --> 00:20:43,199 Speaker 2: wherever you listen. And we'd love to hear from you. 368 00:20:43,520 --> 00:20:46,880 Speaker 2: Email us questions or comments to Big Take at Bloomberg 369 00:20:46,920 --> 00:20:50,600 Speaker 2: dot net. The supervising producer of The Big Take is 370 00:20:50,680 --> 00:20:55,399 Speaker 2: Vicky Ergolina. Our senior producer is Catherine Fink. Frederica Romanello 371 00:20:55,520 --> 00:21:00,480 Speaker 2: is our producer. Our associate producer is Zenobsidiki Rafael is 372 00:21:00,520 --> 00:21:05,360 Speaker 2: our engineer with additional production support. From Jill Nematzi. Our 373 00:21:05,400 --> 00:21:09,640 Speaker 2: original music was composed by Leo Sidrin. I'm West Kasova. 374 00:21:09,880 --> 00:21:12,040 Speaker 2: We'll be back tomorrow with another big take.