SAS Named a FRAML Leader by Chartis
SAS’ Category Leader distinction in Chartis’ inaugural evaluation of FRAML solutions reflects how SAS’ technologies “have become established as leading fraud and AML service offerings”
Lauding the AI and analytics giant’s “in-depth analysis capabilities across all stages of the customer lifecycle,” Chartis Research has declared SAS as a RiskTech Quadrant Category Leader in FRAML Solutions. SAS scored “high” across all completeness of offering evaluation criteria and notably ranked best-in-class in the analytics and user interface (UI) criterion.
FRAML has evolved from the idea of combining fraud and anti-money laundering (AML) workflows to broader capabilities that enable financial firms – and increasingly regulators and governments – to follow illicit money flows, examine the connections between different types of financial crimes and gain a more holistic view of risk.
“SAS is unique both in its market maturity and its approach,” said Nick Vitchev, Research Director of Financial Crime and Fraud Markets at Chartis. “SAS’ fraud and financial crime solutions are built on a common AI- and machine learning-driven decisioning architecture. This gives SAS’ clients considerable flexibility. They can deploy specific fraud and AML solutions, bolstering capabilities in a modular fashion, or they can adopt an integrated, end-to-end platform for even greater operational efficiency and optimized risk management.”
Vendor leading practices in fighting fraud and financial crime worldwide
SAS was among 35 vendors whose combined fraud and anti-money laundering solutions were evaluated by Chartis in its quadrant report, FRAML Solutions, 2023: Market and Vendor Landscape. The Vendor Analysis highlights SAS’ many strengths, including:
- Hybrid analytics with embedded AI and machine learning. SAS’ hybrid analytics approach combines techniques like automated business rules, predictive modeling, text mining, exception reporting and network link analysis. Per Chartis, this helps firms “spot more suspicious activity with greater accuracy,” while “embedded AI and machine learning [ . . . ] help firms detect more fraud and reduce false positives.” SAS’ cloud-native AI platform, SAS Viya, supports custom SAS models, consortium SAS models and open source models, such as those using R, Python and Lua.
- Robust data management and simplified data integration. From structured and unstructured data to text, documents and images, SAS enables firms to nimbly combine all internal, external and third-party data. Further, as noted in the Vendor Analysis, “SAS has written APIs for more than 20 popular data providers, which deliver intelligence on device reputation, biometric scores, document verification, sanctioned entities and adverse media.”
- “Particularly strong” flexibility, scalability and overall orchestration. According to Chartis, SAS’ self-service capabilities enable firms to “intelligently prioritize alerts for triage, investigation and disposition” and respond to new and evolving fraud threats, which ultimately helps them deliver “a better customer experience while detecting more instances of fraud.”
“The convergence of fraud, AML and cybersecurity creates the opportunity for greater collaboration and information sharing among stakeholders,” said David Stewart, Director of Financial Crimes and Compliance at SAS. “The right strategy combined with the right technology can deliver not only a more comprehensive picture of financial crimes risk but also greater operational efficiency and long-term cost savings.
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“SAS enables firms to integrate data and processes on-premises or in the cloud, with AI and machine learning-powered solutions that help them advance through the four levels of FRAML convergence: shared investigation, automated triage, orchestration of data and processes, and holistic monitoring.”
Market-leading solutions across financial services
This latest distinction from Chartis complements Category Leader accolades in the firm’s Vendor Landscapes for several other technology categories in recent months, including:
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