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FIGHTING MONEY LAUNDERING: THE USE OF DATABASE FOR FINANCIAL INTELLIGENCE TO BUILD AN EFFECTIVE AML/
(D306)
1
Points
Submitted:
Dec 6 2019
Status: 
Submitted

Money laundering, terrorist financing and financial crimes are derived from predicate offenses. Predicate offenses include corruption and bribery, fraud, environmental crime, and tax evasion, forgery, illicit trafficking in narcotic drugs and psychotropic substances, trafficking in human beings and migrants smuggling, Counterfeiting Currency, Fraud, etc. Stakeholders Institutions such as the Liberia Anti-Corruption Commission (LACC), Liberia National Police (LNP), Environmental Protection Agency (EPA) and Liberia Revenue Authority (LRA) have jurisdiction over these predicate offenses. The FIU obtains financial information, conducts preliminary investigation and provides law enforcement entities with actionable intelligence regarding the proceeds derived from these criminal activities (predicate offenses). In the same vein, the FIU has increase its partnerships with the LRA and forged cooperation with the Liberia Extractive Industry Transparency Initiative (LEITI) in identifying and investigating the proceeds of crime in the natural resources industries. These efforts will deter criminals from perpetrating financial crimes and thereby reducing leakages in revenue generation.

Effective data mining of financial information and beneficial ownership information to identify suspicious persons, assets, criminal networks and associations, to trace activities or transactions leads to FIU's effectiveness to detect, deter, and prevent organized crime through data analysis, investigation, prosecution, conviction, and confiscation of the proceeds of corruption and money laundering. Stakeholder institutions in the AML/CFT regime in Liberia have challenges with data acquisition, storage, and maintenance thus creating serious impediment in domestic cooperation, collaboration, and coordination in the exchange of information and/or intelligence in the fight against money laundering and Terrorist Financing (ML/TF). Suspicious Transaction Report (STR) wait weeks above deadlines due to searches through paper files for information and/or the cost to locate information from the sources outside the central offices often becomes impossible.

Hence the ultimate need to migrate to the use of automated systems to collect, analyze, store, and maintain huge data in the fight against ML/TF and other preceding crimes. Liberia AML/CFT regime needs assistance to upgrade the fight to a more collaborative, cooperative, and coordinative way via the use of digital analytical systems which will enhance the effort in detecting, preventing, analyzing/investigating financial crimes. The use of automated systems will result into deterrence for criminals using Liberia as a weaker spot to clean their money or generate money to fund other terrorist activities in the region and the world.

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