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Revolutionizing Crypto Crime Detection: AI’s Leap in Tracing Bitcoin Money Laundering Unveiled



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A Large New Dataset Could Enhance AI's Ability to Detect Cryptocurrency Money Laundering

AI technologies excel at sifting through enormous amounts of data to identify patterns invisible to humans or to speed up the identification of those patterns. The blockchain of Bitcoin, which is a public ledger documenting close to a billion transactions among anonymous users, presents an ideal challenge for AI. A recent study, coupled with the release of a significant dataset focused on crypto-related criminal activity, could significantly advance the capabilities of automated systems in detecting illegal financial transactions within the Bitcoin network.

On Wednesday, a team comprising experts from Elliptic, a cryptocurrency">king company, alongside scholars from MIT and IBM, unveiled a study introducing a novel method for detecting money laundering activities within the Bitcoin blockchain. Instead of focusing on identifying specific cryptocurrency wallets or groups of addresses linked to illicit activities like dark-web marketplaces, theft, or fraud, the team focused on analyzing transaction patterns leading from such illicit sources to a cryptocurrency exchange where the ill-gotten gains might be converted to cash. Utilizing these transaction patterns, they trained an artificial intelligence model to recognize similar patterns of money movement, effectively creating what they term as a detector for the "shape" of potential money laundering actions on the blockchain.

Currently, they are launching a trial version of their artificial intelligence model aimed at identifying illicit bitcoin transactions. Furthermore, they are sharing the data set used for training this model, which consists of 200 million transactions from Elliptic's categorized and marked blockchain records. According to the researchers, this is the largest dataset of its kind ever to be released to the public, surpassing previous records by a factor of a thousand. "We are offering roughly a thousand times more data than before, and rather than just tagging illegal wallets, we are marking instances of money laundering that could involve a series of transactions," explains Tom Robinson, the chief scientist and co-founder of Elliptic. "This represents a significant change in how blockchain analytics are applied."

For several years, experts in blockchain technology have leveraged artificial intelligence to enhance and automate their methods for tracking cryptocurrency transactions and pinpointing illicit activities. Specifically, in 2019, Elliptic collaborated with both MIT and IBM to develop an artificial intelligence model aimed at identifying questionable financial transactions. As part of this project, they shared a relatively small dataset comprising approximately 200,000 transactions, which served as the training data for this model.

In their latest study, the same group of researchers adopted a far more comprehensive strategy. Instead of attempting to categorize individual transactions as lawful or illegal, Elliptic examined groups of up to six transactions occurring between Bitcoin address clusters, which it had pinpointed as involved in illicit activities, and the trading platforms where these identified suspicious entities exchanged their cryptocurrency. The hypothesis was that the transaction patterns between these criminals and their liquidation sites could indicate instances of money laundering activities.

Based on this theory, Elliptic compiled 122,000 examples of these identified patterns, known as subgraphs, which are indicative of money laundering, from a larger pool of 200 million transactions. The researchers utilized this collection of data as a foundation to develop an artificial intelligence model capable of identifying instances of money laundering throughout the entire Bitcoin blockchain.

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In a practical application of their developed AI solution, the team of researchers evaluated its effectiveness by analyzing its findings against data from an unnamed cryptocurrency exchange. They discovered 52 questionable transaction sequences leading into the exchange. Interestingly, the exchange had previously identified 14 of those receiving accounts as potentially involved in criminal activities, with eight specifically tagged for connections to money laundering or fraudulent operations, based largely on the verification information obtained from the account holders. Remarkably, the researchers' AI was able to draw similar conclusions about these accounts without any access to the verification data or details about the money's origins, paralleling the investigative outcomes of the exchange's own team.

Mark Weber, a coauthor of the study and a fellow at MIT's Media Lab, emphasized the significance of their findings, despite what might initially appear as a modest achievement. Identifying 14 suspicious accounts out of 52 might not seem impressive at first glance, especially when considering the exchange's overall rate of flagged accounts for potential money laundering sits at just 0.1 percent. However, the researchers argue that their automated tool has markedly improved the efficiency of detecting such accounts, effectively narrowing the search to roughly one in four accounts. "To shift from a scenario where only one in a thousand accounts examined might be involved in illicit activities to finding 14 out of 52 represents a dramatic improvement," Weber stated. He further noted that investigators are now poised to delve deeper into the remaining accounts to uncover any potentially overlooked suspicious activity.

Elliptic has disclosed that it has been utilizing the AI model internally for some time. Highlighting the effectiveness of the AI model, the team mentions that through its help in scrutinizing the origins of certain dubious transaction sequences, they were able to uncover Bitcoin addresses linked to a Russian dark-web marketplace, a cryptocurrency “mixer” aimed at concealing Bitcoin transaction paths on the blockchain, and a Ponzi scheme operating out of Panama. (Elliptic refrained from naming any of the suspected wrongdoers or entities, informing WIRED of its policy not to disclose subjects of active investigations.)

Arguably, the release of Elliptic's training dataset on Kaggle, a site for machine learning and data science owned by Google, could be seen as more significant than the utility of the AI model developed by the researchers themselves. MIT's Weber highlights the commendable decision by Elliptic to share their resources openly, stating, “Elliptic could have chosen to keep this resource to themselves. Instead, they embraced a spirit of openness, offering a valuable tool to the wider community that even their rivals can use to enhance their efforts in combating money laundering.” Elliptic has made it clear that the data made available is stripped of any personal information, lacking any direct identifiers linked to Bitcoin address owners or the addresses themselves. What has been shared pertains solely to the transaction patterns, or “subgraphs,” marked by Elliptic for their potential relevance to money laundering activities, ensuring privacy and security in their contribution to the field.

This vast collection of data is expected to significantly boost AI-driven research in tracking bitcoin laundering activities, according to Stefan Savage, a computer science professor at the University of California San Diego and adviser to the primary author of a groundbreaking study on bitcoin analysis from 2013. However, he believes that in its current state, the tool in question is unlikely to transform the fight against money laundering in the cryptocurrency space. Instead, it should be seen more as a demonstration of potential. “For an analyst, using a tool that only sometimes provides accurate results can be challenging,” Savage notes. “I see this development as a signal that there's potential in this area and it warrants further investigation by more researchers.”

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Savage cautions that the use of AI tools in investigating money laundering might introduce novel ethical and legal dilemmas, especially if these tools' outputs are utilized as concrete criminal evidence. He points out that these AI systems frequently operate as a "black box," delivering outcomes without elucidating the processes behind them. "This touches on a sensitive area, akin to the unease people feel towards facial recognition technology," he remarks. "The inability to clearly articulate how it functions, while relying on it for making decisions that could affect individual freedoms, is a concern."

MIT's Weber contends that the practice of identifying potentially dubious financial activities has traditionally involved the use of algorithms by investigators specializing in money laundering. He maintains that the integration of AI technologies into these processes merely enhances the efficiency of these algorithms and reduces the number of incorrect alerts that not only divert the attention of investigators but also wrongfully implicate innocent individuals. "The focus here isn't on automating the process," Weber clarifies. "It's about addressing an extremely difficult task of finding something very small or hidden, and we're advocating for the adoption of more precise tools like metal detectors rather than relying on less effective methods like chopsticks."

Regarding the potential influence of their study, Savage believes that the extensive and comprehensive training data from Elliptic could be beneficial not only for studies on blockchain but also for advancing AI research in areas such as healthcare and recommendation engines. Moreover, he emphasizes that the goal of their research is to have tangible applications, particularly in discovering patterns that could uncover financial crimes.

Weber expresses optimism that this endeavor will transcend mere scholarly activity, hoping it will inspire those in the field to actively engage and advance with it.

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