Description
This course evaluates the current impact and future opportunities, of technological innovation in the AML Compliance Cycle. The course examines, through the demonstration of a range of contemporary AML (RegTech) solutions, the potential of various technologies – machine learning (ML), artificial intelligence (AI), natural language processing (NLP), and distributed ledger technologies (DLT) – to enhance the operational efficiency, and increase the quality of actionable information, of the AML Compliance Cycle.
The evaluation of use cases across several sectors with AML reporting obligations, and an examination of the challenges in implementing innovative solutions, further develops the courses focus on the adoption of best practice in the AML Compliance Cycle. Finally, to position Ireland as a centre of innovative and regulatory excellence, the course formulates strategic priorities for stakeholders in the Irish AML/CTF ecosystem.
ASSESSMENT
40%: Blend of competency based MCQs & scenario analysis
60%: Final Examination
OUTCOMES
On the successful completion of this course learners will be able to:
1. Contextualise the balance in enabling financial services innovation whilst protecting consumers, maintaining financial stability, and enhancing the AML Compliance Framework.
2. Assess the key concepts and core technologies supporting the innovation and adoption of AML (RegTech) solutions throughout the AML Compliance Cycle.
3. Classify the range of applications and demonstrate the application of contemporary AML (RegTech) solutions within the AML Compliance Cycle.
4. Evaluate use case studies of the application of technologically enhanced solutions through the AML Compliance Cycle.
5. Identify the challenges in adopting/ implementing AML (RegTech) solutions in the context of the EU’s regulatory focus on Digital Resilience.
6. Summarise the evolution of RegTech and SupTech innovation in the context of formulating strategic priorities to position Ireland as a centre of excellence in AML compliance.
          Â
          Â