BSA/ AML - Following the Money, the Difference between Money Laundering and Terrorist Financing and How to Combat Both

This AML training will provide you the tools to identify the differences between money laundering and terrorist financing and how to protect your financial institute against potential criminal activity. It will also highlight the complexities of modern day proactive compliance departments.

Why Should You Attend:

Since the criminalization of money laundering and the creation of Bank Secrecy Act (BSA) provisions, along with the more recent USA Patriot Act, banks have been preventing, identifying and developing ways to stop criminal activity from flowing through their doors. Yet, usually, financial criminals are one step ahead in their schemes and processes compared to governments or banks. How do you combat against what you do not know? How can you prevent something if you are not looking in the right direction?

This webinar highlights the complexities of modern day proactive compliance departments. This course will examine the differences between the confusing and often misapplied terms money laundering (ML) and terrorist financing (TF). Although the crimes independent of each other can be used in conjunction, their concepts, efforts and typologies are different. This course will identify those differences, show how to detect similar cases and ultimately how to protect and defend against these different crimes.

Areas Covered in the Webinar:

• Truly define the terms and crimes of Money Laundering and Terrorist Financing
• Explain how other financial crimes can be part of the ML or TF scheme
• Identifying typologies and concepts of each crime
• Defining a Proactive Compliance Department for;
  • Data analysis
  • Resource development and training
  • Monitoring systems and parameters
  • Identifying the attempted or committed crime
  • Reporting
  • Becoming proactive and not reactive
  • Government resources to contact
• Life cycles of Money Laundering and Terrorist Financing crimes
• Banking case studies of current ML & TF approach and their pitfalls