AI/ML in AML: Breakfast Seminar

Tuesday 25th February at 8:30am  •  SAS Institute  •  Frederiks Plads 36, 4. Sal DK-8000  •  Århus C

Abstract Data Visualization art - gradient midnight to cobalt blue

Join us as we bridge the gap in information sharing around anti-money laundering best practices and how artificial intelligence and machine learning can improve existing solutions in order to reduce the cost of AML

According to the Financial Action Task Force (FATF), effective information sharing is one of the cornerstones of a well-functioning anti-money laundering/counter-terrorist financing framework. This becomes even more relevant as we continue to see and hear how more and more financial institutions in the Nordics have been used as a conduit for financial crime.

We aim to bring together stakeholders driven by a common goal of combatting money laundering and terrorist financing – to cultivate a culture of cooperation, understand how we can do things better and how we can help each other. We will also explore together the role of artificial intelligence and machine learning in AML, the hesitations of financial institutions and the point of view of regulators.  

Join our breakfast seminar and let’s take one step further in bridging the gap in information sharing within the AML space.


TimeAgendaKey Points
08.30 - 09.00Breakfast Buffet 
09.00 - 09.10Welcome 
09.10 - 09.25AML Regulatory Requirements
  • The AML Directives and the impact on banks
  • Do’s and Don’ts when applying AI in AML based on Finanstilsynet’s
    ”God praksis ved brug af superviseret machine learning”
09.25 - 09.40Addressing AML Regulations
  • The AML Compliance ecosystem
  • The role of people, process and technology
09.40 - 09.55Experience sharing:
Status and Best practices

Dialogues around the tables on market practices and challenges in:

  • Transaction Monitoring/screening activities
  • Customer Due diligence activities
09.55 - 10.05
Coffee Break 
10.05 - 10.20

AI in a nutshell

  • What is AI?
  • What is the value of AI in AML?
10.20 - 10.50Adoption of AI in AML:
Use cases and Demos
  • Customer Segmentation: Setting thresholds right
  • AML Optimization: Reducing false positives and improving detection rate
  • Predictive Modeling: Generating AML alerts at the right time
10.50 - 11.00


  • Summary of where banks are at in the adoption of AI in AML,
    the challenges and the next steps

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