Strategic and Practical Considerations in Pursuing Money Laundering Cases (AMLS)
Deadline passed
Session No.: JV 22.36
Location: Vienna, Austria
Date: October 31, 2022 - November 4, 2022 (1 week)
Delivery Method: In-person Training
Primary Language: English
Target Audience
Financial intelligence unit (FIU) officials; law enforcement officials; and other government officials involved in undertaking national money laundering investigations, prosecutions and asset confiscation and recovery actions.
Qualifications
Participants are expected to have at least two years of experience with AML/CFT issues.
Course Description
"Disclosure: Due to COVID 19, courses will be delivered in person, hybrid, or virtual, according to safety guidelines. As of today, this course is scheduled to be delivered in person in Vienna, but its delivery method may change. By submitting your application, you confirm your availability to participate in the course either in person or virtually. Up-to-date Covid-19 vaccination is likely to be required and related information may be requested from participants for in-person courses."
This one-week course, presented by the IMF's Legal Department, aims at enhancing and strengthening the capacities of officials engaged in the operational aspects of enforcement against money laundering cases. The course is targeted at FIU officials, law enforcement agencies, prosecutors and judges involved in financial intelligence and effective enforcement against ML offenses including securing convictions, domestic and cross-border confiscation and asset recovery. The course will involve in-depth discussions on practical and strategic consideration in enforcing ML laws including practical exercises.
Course Objectives
Upon completion of this course, participants should be able to:
- Understand the use of key tools and sources of data to assist in mounting ML investigations.
- Understand the challenges and key measures necessary to bringing ML cases to successful prosecution.
- Develop policies and strategies to institutionalize good practices for effective enforcement against ML cases.
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