As a test of their resulting AI tool, the researchers checked its outputs with one cryptocurrency exchange—which the paper doesn't name—identifying 52 suspicious chains of transactions that had all ultimately flowed into that exchange. The exchange, it turned out, had already flagged 14 of the accounts that had received those funds for suspected illicit activity, including eight it had marked as associated with money laundering or fraud, based in part on know-your-customer information it had requested from the account owners. Despite having no access to that know-your-customer data or any information about the origin of the funds, the researchers' AI model had matched the conclusions of the exchange's own investigators.
Correctly identifying 14 out of 52 of those customer accounts as suspicious may not sound like a high success rate, but the researchers point out that only 0.1 percent of the exchange's accounts are flagged as potential money laundering overall. Their automated tool, they argue, had essentially reduced the hunt for suspicious accounts to more than one in four. “Going from ‘one in a thousand things we look at are going to be illicit’ to 14 out of 52 is a crazy change,” says Mark Weber, one of the paper's coauthors and a fellow at MIT's Media Lab. “And now the investigators are actually going to look into the remainder of those to see, wait, did we miss something?”
https://www.wired.com/story/ai-crypto-tracing-model-money-laundering/
SHA256X2 / DirtyRelayKid™️
npub1la…e7spc
2024-05-05 15:26:53
Author Public Key
npub1law8ss95tykalpft0awfjkman4ruwfglsunpk70r0dw9sgwrjv4qde7spcPublished at
2024-05-05 15:26:53Event JSON
{
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"pubkey": "ff5c7840b4592ddf852b7f5c995b7d9d47c7251f87261b79e37b5c5821c3932a",
"created_at": 1714922813,
"kind": 1,
"tags": [],
"content": "As a test of their resulting AI tool, the researchers checked its outputs with one cryptocurrency exchange—which the paper doesn't name—identifying 52 suspicious chains of transactions that had all ultimately flowed into that exchange. The exchange, it turned out, had already flagged 14 of the accounts that had received those funds for suspected illicit activity, including eight it had marked as associated with money laundering or fraud, based in part on know-your-customer information it had requested from the account owners. Despite having no access to that know-your-customer data or any information about the origin of the funds, the researchers' AI model had matched the conclusions of the exchange's own investigators.\n\nCorrectly identifying 14 out of 52 of those customer accounts as suspicious may not sound like a high success rate, but the researchers point out that only 0.1 percent of the exchange's accounts are flagged as potential money laundering overall. Their automated tool, they argue, had essentially reduced the hunt for suspicious accounts to more than one in four. “Going from ‘one in a thousand things we look at are going to be illicit’ to 14 out of 52 is a crazy change,” says Mark Weber, one of the paper's coauthors and a fellow at MIT's Media Lab. “And now the investigators are actually going to look into the remainder of those to see, wait, did we miss something?”\n\nhttps://www.wired.com/story/ai-crypto-tracing-model-money-laundering/",
"sig": "5aa7e40b2af4aff218c69eff01834782ccc6689045541107644bb8bb6beb0a94052ab9d9dd5f177e7849a84c2b55e2b536eb7c62569f3cea85c3877bc7e21c94"
}