AML is the acronym for Anti Money Laundering. All financial organizations must have offices that, by constantly carrying out in-depth analysis on customers’ transactions, are able to intercept and, when possible, prevent the execution of financial crimes.
Money laundering
Money laundering consists in the financing of lawful activities starting from the profits deriving from illegal or criminal activities; it is therefore a crime that is fought at European level as well as in single nations through the “FATF” rules, which define the operating methods of the AML anti-money laundering function of banks, ranging from customer identification to the assessment of transactions and continuous monitoring of operations.
Criminals often “launder” money they get through illegal acts such as drug trafficking, so the funds cannot easily be traced back to them. A common technique is to manage money through a legitimate business based on money owned by the criminal organization or its confederates. The supposedly legitimate business deposits the money, which criminals can then withdraw.
Money launderers can also introduce cash into foreign countries to deposit, deposit cash in small increments to avoid arousing suspicion, or use illicit money to purchase other means of payment. Launderers sometimes invest the money, using dishonest brokers willing to ignore the rules in exchange for large commissions.
Artificial Intelligence for the fight against money laundering
To combat money laundering, banks use various techniques: for example, deposits are often required to remain in an account for a minimum of five trading days. This holding period is intended to aid in anti-money laundering and risk management.
Going beyond operational measures, which are often ineffective because they can be easily circumvented by recyclers, today the technology that can most determine a concrete step forward in the fight against financial crimes is represented by Artificial Intelligence, which allows the analysis of Big Data correlated to transactions through Machine Learning and therefore makes it possible to discover anomalous or at least “suspicious” behaviors by economic operators.
In particular, through data analysis and user profiling techniques it is possible to:
- make possible in-depth knowledge of bank customers (KYC – Know Your Customer)
- effectively assess a series of risk indicators (KRI – Key Risk Indicators) and allow the operators of the AML function to identify the customers to report to the financial authorities
- profile users based on behavior
- discover relationships in the execution of financial transactions
- identify user groups that are coalesced to make the improper circulation of funds possible
RegTech
Money laundering cannot be stopped completely, but it can be reduced through constant vigilance. Financial institutions can monitor customer deposits and other transactions to make sure they are not part of a money laundering scheme. Institutions must verify the origin of large sums, monitor suspicious activity and report cash transactions in excess of € 1,000.
The application of artificial intelligence to compliance and AML management is defined as RegTech; today, thanks to the availability of tools capable of analyzing large user networks in real time, RegTech is the reference technology for banks and fintechs operating in an increasingly complex world, in which money circulates quickly thanks to electronic money.
