August 21, 2019

AML Transaction Monitoring Rediscovered: the Art of Predicting 

Araliya Samme, Head of Financial Crime at Featurespace, joins the line-up for ACAMS Las Vegas 2019.  

2.45pm - 3.15pm(PT), Tuesday 24 September 2019.

The 30 minute presentation will cover:

  • Understanding the value of focusing on good customer behavior vs. bad with real-time machine learning
  • Discovering how a Top Tier Global Bank used Adaptive Behavioral Analytics to spot more suspicious activity
  • Learning how to supercharge your own AML transaction monitoring program with machine learning

The Featurespace team will be available for the duration of the conference, to learn more visit booth 604 or get in touch today.

About the speaker

Araliya joined Featurespace in June 2016 as a Financial Crime Subject Matter Expert with a focus on Fraud and AML before becoming the Head of Financial Crime for the firm. Former Big 4 management consultant, at both Deloitte and EY (Ernst and Young) UK firms, Araliya worked with multiple financial institutions in the UK and abroad as a trusted advisor across all areas of Financial Crime. She started her career at BNP Paribas in Paris, where Araliya managed the major migration of the bank’s AML Transaction Monitoring solution within the Retail Banking Compliance & Risk Management team. Araliya holds a Master of Engineering in Computer Science from the Institut Superieur d’Electronique deParis (I.S.E.P.), French Grande Ecole.

About Featurespace

Headquartered in the U.K. and U.S. and with offices in Atlanta, Cambridge and London, Featurespace™ is the world-leader in fraud prevention and creator of the ARIC™ platform, a real-time AI machine learning software that risk scores events in more than 180 countries.

The ARIC platform combines adaptive behavioural analytics and anomaly detection to automatically identify risk and catch new attacks as they happen. The increased accuracy of understanding behaviour strikes the balance between improving fraud and risk detection and operational efficiencies, while also reducing the number of genuine transactions that would be incorrectly declined due to traditional rules by as much as 70 percent.