Chartis: SAS a category leader in trade-based anti-money laundering

Broad end-to-end automation and deep risk analytics capabilities earned SAS the highest completeness of offering score among 10 vendors

Research heavyweight Chartis has named SAS a RiskTech Quadrant^® Category Leader in trade-based anti-money laundering (TBAML). Chartis declared SAS the highest-scoring vendor in completeness of offering, scoring the analytics and AI titan “high” across all four criteria – data support, depth of typology coverage, breadth of analytical techniques, and workflow.

“Trade-based money laundering remains notoriously difficult to investigate due to immense international trade volumes and the complexity of trade transactions,” said Philip Mackenzie, Research Principal for Chartis’ Financial Crime Risk Management pillar. “In this context, SAS can help financial organizations address the core categories of trade finance risk in a holistic way. Account and transaction activity in particular are key areas for SAS. In addition, the company has a library of prebuilt, ready-to-deploy TBAML typologies that employ AI and machine-learning capabilities to help detect anomalous activity.”

Chartis evaluated SAS and nine other vendors, publishing Trade-Based Anti-Money Laundering Solutions, 2022: Market and Vendor Landscape as its first ever assessment of this escalating facet of financial crime compliance. The complementary Vendor Analysis states:

“The SAS solution is notable for combining data extraction, data fusion, document and transactional analytics, KYC/AML analytics and workflow management. In particular, the integration of analytics for trade documents – to extract text from images and to perform various checks for trade fraud and AML risk indicators – stood out.”

The Vendor Analysis further notes, “SAS has rolled out the fully featured TBAML platform to a handful of clients, and currently leads the market in this nascent area.”

Global bank uses AI to improve TBAML risk and compliance

Among its TBAML offerings, SAS co-developed Trade Risk Analytics Compliance Kit (TRACK) with gold partner EY. The advanced trade finance risk solution was engineered to automate manual tasks and improve the detection accuracy of  TBAML, sanctions risks and boycott violations for one of the world’s largest banks.

TRACK replaced approximately 100 checks with a single, hybrid fraud/AML machine-learning model, which scores the probability a trade transaction warrants further investigation. The model continuously learns from past outcomes. Robust text mining and text analytics tools provide critical contextual analysis buried in the documentation for about 9 million trade transactions annually, comprising roughly 25 million related documents. The initiative’s award-winning results include:    

• A more than 30% jump in operational efficiency.
• Accuracy rates above 85% for the fraud/AML model.
• A 60% reduction in false positives by the sanctions screening module.

The promise of AI in AML

As regulatory pressures intensify, financial services organizations will need effective data aggregation and a diverse array of increasingly sophisticated analytics capabilities.

In a recent AML survey of 850 financial crimes professionals by the Association of Certified Anti-Money Laundering Specialists, KPMG and SAS revealed surprising innovation in AML technology. More than half (57%) of respondents indicated that their organizations had either deployed AI or machine learning into their AML compliance processes, were piloting such solutions or planned to implement them in the next 12-18 months.

“Global trade hit a record-breaking $28.5 trillion in 2021, underscoring the depth and complexity of monitoring for trade-based money laundering,” said Stu Bradley, Senior Vice President of Fraud and Security Intelligence at SAS. “The sheer enormity of traded goods makes it easier for criminals to move vast sums of value in plain sight.”

Bradley added, “Given pent-up demand on the global supply chain, it’s critical that trade finance organizations reduce the friction of manual processes while managing their regulatory risk. Firms can rely on SAS’ AI-driven, industry-proven solutions to cut through the noise and help focus scarce resources where they’ll be most effective.”

To learn more about how AI and machine learning can bolster banks’ effectiveness in fighting financial crime, register for the two-part virtual AML Masterclass series by SAS and the European Banking Federation. The first class, Demystifying AI for Anti-Money Laundering, kicks off online March 31, 2022, at 3 p.m. CET and will be available later on demand.