SAS named AML transaction monitoring leader by Chartis

Best-in-class ratings across four capabilities

Chartis evaluated 49 providers in its AML Transaction Monitoring Solutions, 2026: Quadrant Update. SAS received best-in-class ratings for data and system integrations, risk typology modeling, analytical modeling, and model quality and validation.

According to SAS, SAS Viya brings transaction, customer and risk data together across existing systems. The company says this supports detection and investigations, as well as intelligence sharing between anti-money laundering and fraud teams.

SAS combines statistical techniques, machine learning and behavioral analytics to tailor detection strategies to customer profiles, transaction patterns and emerging risks. Its platform also uses AI-assisted code generation to streamline model development. Model validation and ongoing monitoring are designed to help institutions address drift, bias and declining performance while maintaining governance across the model life cycle.

Chartis cites agentic AI and oversight

Chartis also recognized SAS for workflow automation, solution packaging and deployment, and speed, volume and performance. Sean O’Malley, research director for Financial Crime and Control at Chartis, said the company had “significantly expanded the use of agentic AI,” with enhancements to analytical modeling, model quality and validation, and workflow automation.

Chartis said SAS combines scenario-based monitoring with advanced analytics, machine learning, network analysis and AI-driven risk scoring. Its accompanying SAS Vendor Spotlight describes a market shifting toward fewer, higher-value alerts, which can help investigators focus their time on legitimate risks.

The report also highlights AI embedded across Viya to reduce manual coding, accelerate model development and deployment, and focus detection on meaningful risk signals. SAS says built-in model governance and responsible AI capabilities are intended to provide oversight from coding through deployment and everyday use.

“If a bank can't recognize when a model has gone off track or explain why it generated an alert, it has simply traded one blind spot for another,” said Stu Bradley, senior vice president of Risk, Fraud and Compliance Solutions at SAS.

FRAML integration brings fraud and AML signals together

Chartis identified growing momentum behind the convergence of fraud prevention and anti-money laundering, often called FRAML. Fraud and AML teams can see different parts of the same activity, so sharing signals may give financial institutions a broader view of financial crime risk while preserving each team’s investigative processes and regulatory controls.

Chartis credited SAS’s integration of fraud and AML capabilities with bringing transaction data, customer information, risk indicators and analytical models into a more comprehensive view. The company says this supports detection, decisioning and investigative workflows across existing technology environments.

For banks, the recognition positions SAS’s offering around three connected priorities described in the source: improving alert quality, adapting models as risks change, and maintaining oversight while bringing fraud and AML intelligence together.