Data-driv­en AML

Purpose

Learn to conduct data-driven risk assessments based on facts and evidence – not anecdotes or opinions. You will gain the skills to identify, analyse and manage risks using technology, with a particular focus on transaction monitoring.

Your benefits

• Conduct data-driven risk assessments in compliance work, particularly related to money laundering, terrorist financing and other financial crime.
• Design and optimise rules for transaction monitoring in the fight against financial crime.
• Analyse the effectiveness of existing monitoring systems and identify behaviours that indicate attempts to circumvent controls (e.g. “smurfing” and “structuring”).
• Assess the balance between compliance requirements and business considerations in combating financial crime.
• Discuss how AI can – and cannot – be used in the fight against financial crime.
• Reflect on the ethical, legal and societal implications of using data and technology (especially AI) in this field.

About the course 

Do you want to strengthen your capabilities in combating financial crime?
Gain the latest methods and tools to work more effectively, data-driven and with full traceability in risk management, anti-money laundering and transaction monitoring.

Learn from research, international cases and regulatory requirements, and develop the skills to deliver sharp analyses, targeted risk models and intelligent monitoring. Gain insight into how AI can enhance your work without compromising ethics, governance or accountability.

The teaching is hands-on and practice-oriented, equipping you with concrete solutions that can be implemented in your organisation from day one. This course is for professionals who want to elevate their expertise, ground decisions in data and stay ahead in a field where requirements are increasing and technology is evolving rapidly.

Themes

• Data-driven risk assessments in compliance and anti-money laundering (AML)
• Design, validation and optimisation of AML monitoring systems
• Models for detecting and predicting suspicious patterns and behaviour
• Machine learning and AI in combating financial crime
• Ethics, bias and governance in the use of data and AI
• National and European regulatory requirements for AML technology and monitoring