The European Financial Supervisory Authority estimates that transactions involving "dirty" money now account for around 1.5% of annual gross domestic product in the EU - that's 133 billion euros. The European Union therefore wants to block a larger portion of these dirty deals and has its sights set on a new directive this fall. There continues to be tough penalties in this area when financial institutions are caught in compliance scandals. An example of this came in the Spring of 2021, a situation in which a bank ultimately had to pay a fine of around 500 million euros for violations of money laundering laws. Suspicious transactions were allegedly not reported to the relevant authorities and accounts involved in money laundering were also left unreported.
To comply with the new guidelines and prevent new scandals, financial institutions will have to rely massively on artificial intelligence (AI) and machine learning (ML) methods. "We have made huge strides in recent years and our EU AML rules are now among the toughest in the world," states European Commission Vice President Valdis Dombrovskis, "but they now need to be applied consistently and closely supervised to make sure they really bite." The best way to monitor and implement these rules almost inevitably leads to a holistic software model that incorporates data from a wide variety of channels and sources into the assessment - in both knowledge and data-based processes. Without the consistent use of AI and machine learning, the high number of "false positives" will remain a core problem in monitoring regulatory watchlists and transactions. Solutions such as RiskShield by INFORM allow for the real-time screening of transactions, account holders and recipients. Artificial Intelligence algorithms are used to significantly reduce the "false positive" rate.
Growing mountains of data make detecting money laundering a herculean task
Money laundering by criminal organizations and terrorist groups is increasingly becoming a threat to the financial world and national economies. In the U.S., banks had to pay just over $14 billion in fines last year for violations of money laundering laws. This means that fines have almost doubled in one year (2019: $8 billion). As of this month, according to the International Association of Economic Crime Specialists (ACFCS), there is another threat, as a sharp increase in illicit financial flows to and from Afghanistan could make it possible to finance terrorist attacks.
At the same time, the amount of data to be analyzed in the financial sector has been increasing for years, and with it the effort to filter out suspicious transactions. It is often difficult for financial institutions to analyze and retrieve accurate, relevant data in a timely manner. At the same time, regulators expect these same firms to monitor all trading and transactions and be able to detect and report any market abuse or suspicious activity. It's a Herculean task, because for too long, financial institutions have worked in silos to detect violations of money laundering and terrorist financing laws. It's time for a change and for a holistic, enterprise-wide approach.
End of island solutions in preventing money laundering and terrorist financing.
Island solutions and decoupled systems can no longer meet the requirements of anti-money laundering and counter-terrorist financing laws and regulations. Artificial intelligence tools can aggregate and evaluate results from customer due diligence (CDD) programs, watchlist screening, and suspicious activity monitoring. Benoît Cœuré, Head of the BIS Innovation Hub (there are 63 central banks represented in BIS) says, "With the addition of artificial intelligence (AI)-enabled solutions or tools, automation is taken one step further as natural language processing is leveraged to scrape data from the web or machine learning is used to match and merge disparate data sets…This is not science fiction. This technology is available today. If it is adopted, supervisory teams would have more time to spend on pre-emptive and early supervisory actions before any potential problems start to materialize." Without the use of artificial intelligence, manual rework often remains necessary when one system reports a transaction as innocuous, but another reports the same transaction as potentially suspicious.
Investment in software is needed
Going forward, to prevent the transfer of "dirty" money from criminal activity into the normal, "clean" money cycle as often as possible, banks will need to invest in solutions based on artificial intelligence and machine learning. A modern enterprise solution combines AML compliance software technologies such as Machine Learning, Fuzzy Logic, Dynamic Profiling and network visualization. RiskShield, as an enterprise risk and financial crime management platform, can help the industry take big strides forward in the fight against money laundering and terrorist financing.