Course Overview

This program equips financial‑intelligence units (FIUs), law‑enforcement agencies, prosecutors, regulators, and supervisory authorities with the tools to strategically investigate, build, and pursue money‑laundering (ML) cases. It focuses on risk‑based approaches, inter‑agency coordination, evidence gathering, financial‑intelligence analysis, and prosecutorial strategies.

The course aligns with the FATF Recommendations, World Bank AML/CFT Assessment Methodology, UNODC financial‑crime frameworks, and global best practices for investigation, prosecution, and asset recovery.

2. Learning Objectives

Participants will be able to:

• Understand the legal and institutional frameworks for AML/CFT enforcement.

• Apply risk‑based approaches to identify and prioritize ML cases.

• Conduct financial‑intelligence analysis using modern tools and data sources.

• Build strong evidentiary foundations for ML investigations.

• Coordinate effectively across FIUs, regulators, law enforcement, and prosecutors.

• Understand cross‑border ML typologies and international cooperation mechanisms.

• Prepare prosecutable ML case files aligned with global standards.

• Strengthen institutional capacity to pursue complex ML cases.

3. Target Audience

• Financial Intelligence Units (FIUs)

• Law‑enforcement agencies and investigators

• Prosecutors and judicial officers

• Financial‑sector regulators and supervisors

• Customs, tax authorities, and anti‑corruption agencies

• World Bank–funded AML/CFT reform teams

• Compliance officers and financial‑crime analysts

4. Detailed Course Outline

Module 1: Foundations of AML/CFT Enforcement

• Global AML/CFT architecture (FATF, World Bank, UNODC)

• Legal frameworks for money‑laundering offences

• Predicate offences and links to corruption, tax crimes, terrorism financing

• Institutional roles and responsibilities

• Case studies of major ML prosecutions

Module 2: Understanding Money‑Laundering Typologies

• Traditional ML typologies (structuring, smurfing, cash‑intensive businesses)

• Trade‑based money laundering (TBML)

• Use of shell companies, trusts, and beneficial ownership opacity

• Digital assets, virtual asset service providers (VASPs), and cyber‑enabled ML

• Emerging typologies in developing economies

Module 3: Risk‑Based Approaches to ML Case Selection

• National Risk Assessments (NRAs) and sectoral risk assessments

• Prioritizing high‑impact ML cases

• Identifying high‑risk sectors and financial products

• Using risk indicators and red flags

• Strategic intelligence vs. operational intelligence

Module 4: Financial Intelligence & Analytical Techniques

• Role of FIUs in ML case development

• Suspicious Transaction Reports (STRs) and Suspicious Activity Reports (SARs)

• Data sources: banks, DNFBPs, tax authorities, customs, telecoms

• Analytical tools: link analysis, network mapping, pattern detection

• Building intelligence packages for investigators

Module 5: Conducting ML Investigations

• Planning and scoping an ML investigation

• Evidence gathering: financial records, digital evidence, interviews

• Tracing funds through complex financial structures

• Use of forensic accounting and financial‑crime analytics

• Managing parallel investigations (corruption, tax, fraud)

Module 6: Inter‑Agency Coordination & Information Sharing

• Coordination between FIUs, police, prosecutors, regulators, and tax authorities

• Joint task forces and multi‑agency investigation models

• Overcoming institutional silos and legal barriers

• Public–private partnerships for AML enforcement

• International cooperation (Egmont Group, mutual legal assistance)

Module 7: Building a Prosecutable ML Case

• Elements of a money‑laundering offence

• Evidentiary standards and burden of proof

• Direct vs. circumstantial evidence

• Preparing case files and prosecution briefs

• Working with prosecutors from early stages

Module 8: Asset Tracing, Freezing & Recovery

• Legal tools for asset freezing, seizure, and confiscation

• Tracing proceeds of crime domestically and internationally

• Non‑conviction‑based forfeiture

• Managing recovered assets

• World Bank StAR (Stolen Asset Recovery) Initiative

Module 9: Challenges in Pursuing ML Cases

• Complex corporate structures and beneficial ownership secrecy

• Cross‑border transactions and jurisdictional barriers

• Political interference and institutional weaknesses

• Limited resources and capacity constraints

• Strategies to overcome operational challenges

Module 10: Practical Exercises & Capstone Project

• Analyzing STRs to identify potential ML cases

• Conducting a mock ML investigation

• Preparing an ML case file for prosecution

• Designing an inter‑agency coordination plan

• Capstone: Develop a National ML Case‑Pursuit Strategy for a simulated country

5. Training Methodology

• Expert‑led lectures and guided discussions

• Hands‑on financial‑intelligence and case‑analysis exercises

• Case studies from World Bank, FATF, and UNODC programs

• Group work and scenario‑based simulations

• Practical sessions on evidence gathering, analytics, and prosecution

• Capstone project with peer and instructor feedback

6. Deliverables & Outputs

Participants will receive:

• An AML/CFT Enforcement Toolkit (frameworks, templates, checklists)

• Financial‑intelligence and case‑analysis tools

• Sample ML case files and prosecution briefs

• Capstone project report and presentation

• Certificate of Completion from Regewall Training Institute