Fighting Money Laundering: The Use of Data for Financial Intelligence
How to use of big data, financial information, and beneficial ownership information to identify suspicious persons, assets, criminal networks and associations, to follow the trail of particular activities or transactions, and to determine the links between those targets and possible proceeds of crime including corruption and money laundering.
Financial intelligence units, anti-corruption agencies, law enforcement agencies, and other concerned agencies involved in the fight against corruption and money laundering are permanently involved in analyzing different sets of data to identify physical and legal persons involved in those activities. Just to take one example of data, for instance, the lack of availability of beneficial ownership of companies and trusts allows criminals to hide their identity, the criminal origins of their assets behind the corporate veil, and to enjoy the proceeds of crimes. Much of the discussion to date has focused on which technical solution countries should put in place. Whereas the international standards seem to provide countries with different technical options on how to collect and verify beneficial ownership information (e.g., through registers, financial institutions and other businesses, and by using law enforcement powers); ongoing Fund research shows that to have a substantially effective system in place countries must use a combination of these methods.
Furthermore, while many concerned agencies do not have the proper capability to collect, access, and analyze such data, others are currently investing in high performance computing to improve the collection and analysis of data to fight money laundering. Such operational analysis requires relying on big data analysis from multiple sources (e.g., administrative, financial, law enforcement) and the use of analytical software to process information more efficiently and assist in establishing relevant links. Such tools would need to be supplemented by human judgment element of analysis. Artificial Intelligence can also potentially play a powerful role in detecting patterns of money laundering and identifying specific targets.
Areas for proposal submission:
* Identifying the analytical tools that countries need to develop to collect, access, and analyze data more efficiently to identify suspicious persons and assets, to follow the trail of particular activities or transactions, and to determine the links between those targets and possible proceeds of crime, including corruption and money laundering.
* Discussing how artificial intelligence can play a role in verifying the accuracy of existing information, and in connecting different sources of information?
* Considering the general low levels of implementation of transparency of beneficial ownership measures, there are few examples of best practices that countries can follow. Moreover, the tools will have to be tailor made for each country, depending also on the type of legal system, the type of economy, and the level of development of a country. How can the right building blocks for each type of country be identified?
* Discussing how big data and technology can support the effective implementation of beneficial ownership requirements and improve transparency of legal persons and arrangements.
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