Global data and analytics experts LexisNexis Risk Solutions is upping the ante in the fight against trade-based money laundering by developing AI and LLM-enabled solutions that deliver a more holistic view of trade than ever before.
This is according to Jared Atkinson, group product manager for trade compliance at LexisNexis Risk Solutions, who says trade-based money laundering has become increasingly complex, requiring more advanced technology and greater collaboration by stakeholders to combat it.
“Banks are concerned because traditional approaches to trade compliance identify red flags from single data points, but they don’t offer an in-depth view of the complex patterns and behaviours that are indicative of money laundering,” he says.
Atkinsons says that banks across the globe have improved their trade compliance posture over the past few years and are now increasing their focus on trade-based money laundering.
“It's not easy to do,” he says. “Trade compliance workflows must be sophisticated enough to analyse and synthesize data from multiple sources. Criminals learn from what the regulators and the banks are doing and find new mechanisms to get money into the market.
“Institutions receiving data from a third-party must take it at face value without knowing the data’s history from source, or if it has been altered over time. In some markets, where banks do not finance trade, they might process the transaction without access to data that could indicate trade-based money laundering.”
Collaboration against crime
He notes: “Banks currently act as a proxy policing force because they're held responsible for financing and involvement in any illicit trades, putting them under pressure to create more rigorous processes to identify trade-based money laundering. Some argue the onus shouldn’t rest solely on banks.
“Other parties, including agents and parties moving the products, regulators, governments and customs officials, are also involved. Every stakeholder should collaborate to combat these risks, since they negatively impact on countries, economic growth and individual citizens.”
Getting a step ahead of crime
Atkinson says LexisNexis Risk Solutions is working to use AI, large language models and larger datasets to offer more holistic insights into the entire trade ecosystem. “Thanks to the technologies such as AI and large language models, which can process high volumes of data and then draw patterns and behaviours out of that, it’s a real step change from where we were two or three years ago.”
He says: “We've established a solid foundation of trade compliance capabilities to identify the potential risk on the movement of products and the products themselves. Alongside this, our screening solutions help identify sanctioned and potential risky corporations, individuals, state-owned entities and recognise politically exposed persons and bad publicity, as these are all components of risk.”
“We're actively building and enhancing our ability to identify pricing discrepancies and pricing manipulation from the goods that are being traded.”
These capabilities will become a key tool for identifying trade-based money laundering, he says.
Atkinson highlights the need for a more comprehensive approach to understanding trade vessels and their ownership. “Traditionally, organisations have focused on identifying vessels that are sanctioned. However, this is no longer enough. There is a growing market demand to uncover a wider range of risks related to vessels, including their complete ownership structures. By examining these structures, organisations can identify red flags, such as sanctioned entities, state-owned enterprises, enforcement actions or other financial crime risk typologies,” he explains.
Another critical issue is the movement of vessels. Atkinson points to AIS spoofing, a tactic where vessels manipulate their AIS data to provide false information about their location. “The ability to detect and address this kind of activity is increasingly important. By understanding historical patterns and behaviours, organisations can better identify unusual or suspicious activities,” he says.
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