Gambia Fights Money Laundering and Terror Financing: Challenges Remain
Banjul, The Gambia - The Gambian authorities have identified key threats and vulnerabilities in the financial system to combat money laundering (ML) and terror financing (TF). However, a recent assessment reveals that understanding of these threats is limited, particularly in relation to predicate offences, assets confiscation, and legal persons.
Main Domestic Money Laundering Threats
- Fraud
- Drug trafficking
- Theft/stealing or robbery
- Bribery
- Corruption
Limited Understanding of Organized Crime and Terror Financing Vulnerabilities
- Illicit trafficking in stolen goods
- Tourism sector vulnerabilities
- Informal economy vulnerabilities
- Virtual assets vulnerabilities
State Intelligence Service (SIS), Police, Drug Law Enforcement Agency (DLEAG), and Financial Intelligence Unit (FIU) Understandings
- The authorities have a better understanding of TF threats emanating from international terrorism.
- However, there is still room for improvement in identifying inherent vulnerabilities that could be exploited for TF purposes.
Strategies to Mitigate Systemic Vulnerabilities
- Legal strategies
- Institutional strategies
- Capacity-building strategies
- Simplified measures in low-risk sectors through the National Anti-Money Laundering and Combating Financing of Terrorism (AML/CFT) Guidelines for reporting entities
Higher-Risk Areas
- Banking sector
- Foreign exchange sector
- Remittance sector
- Real estate sector
- Casinos sector
- Digital payment methods (DPMS) sector
Competent Authorities’ Objectives and Activities
- Aligning objectives and activities with national ML/TF risks and strategies.
- Need for more effective targeting of complex and higher-risk ML activities.
National Counter-Terrorism Strategy (GAMSAT)
- Drafted, but does not incorporate CFT yet.
- Importance of transparency in beneficial ownership of legal persons and arrangements recognized.
Challenges and Future Directions
- Effective combat against ML and TF requires continued effort and improvement.
- Prioritization of complex and higher-risk ML activities is crucial with adequate resources and policy guides.