An increasing number of European banks and their supervisory authorities are being drawn into money laundering allegations.
According to the Organised Crime and Corruption Reporting Project (OCCRP) the latest allegations on ‘Troika Laundromat’ involved the use of a complex network of 75 shell companies moving billions of US dollars belonging to wealthy Russians – including politicians and prominent business heads – into major Western banks. The funds were allegedly used to purchase properties in Spain and Austria as well as high-end luxury goods including yachts and chartered jets. This has ultimately resulted in criticism for failing to prevent potentially criminal Russian funds moving through their branches across the world.
As the headlines and enforcement figures consistently show, the financial services sector can no longer rely on outdated compliance systems and armies of analysts and investigators to help tackle this challenge.
Given the high volume and complex nature of today’s transactions, combined with opaque and disparate relationships and connections between customers and entities, financial services companies are turning to technology that can better identify and process all the hidden and known connections between legitimate customers, businesses, criminals, PEPs, sanctioned entities and shell companies.
AI and machine learning can then create accurate risk profiles or risk scores to determine whether or not banks should enter into a potential client or customer relationship with anomalies or suspicious activity flagged for further investigation by analysts.