Client Profiling for an Anti-Money Laundering System
Alexandre, Claudio, Balsa, João
–arXiv.org Artificial Intelligence
Acts of prevention and fight against money laundering (ML) crimes are prioritized by almost every government in the world, at the same level of the most relevant global issues. Money laundering is a crime that typically consists in making a certain illegal financial gain into a legal gain. According to the United Nations Office on Drugs and Crimes (UNODC) the annual global estimate of laundered money is about 2% - 5% of the Gross World Product, or US$800 billion - US$2 trillion [1]. As if the financial volume were not enough, another reason for governments to focus on this crime is for the fact that it is clearly connected to other types of crimes such as illegal drug trade, fraud, corruption, kidnapping, terrorism, arms smuggling, among others. Most countries' financial authorities, usually Central Banks, are responsible for controlling and defining antimoney laundering (AML) regulations, demanding from financial institutions the implementation of procedures that apply the defined norms.
arXiv.org Artificial Intelligence
Jan-11-2016
- Country:
- South America > Brazil
- São Paulo (0.04)
- Oceania > New Zealand
- North Island > Waikato (0.04)
- North America > United States
- District of Columbia > Washington (0.04)
- Pennsylvania > Philadelphia County
- Philadelphia (0.04)
- New York
- New York County > New York City (0.04)
- Albany County > Albany (0.04)
- California > San Francisco County
- San Francisco (0.14)
- Europe
- Portugal > Lisbon
- Lisbon (0.15)
- Germany > Baden-Württemberg
- Karlsruhe Region > Karlsruhe (0.04)
- Portugal > Lisbon
- South America > Brazil
- Genre:
- Research Report (0.64)
- Industry:
- Law (1.00)
- Banking & Finance (1.00)
- Law Enforcement & Public Safety > Fraud (0.84)
- Government > Intergovernmental Programs (0.54)
- Technology: