The news cycle often delivers stories about shell companies facilitating some type of financial crime or risk. In 2016, the Panama Papers exposed that numerous shell companies were owned by politicians and some of the world’s wealthiest people for illicit activities including tax evasion, fraud and evading international sanctions. The recent release of the Paradise Papers revealed even more wrongdoing by wealthy individuals and corporations around off-shore tax avoidance.
Shell companies are often established in secrecy havens where little or no information is published about corporate ownership which interferes with international law enforcement agencies ability to obtain timely information necessary to proceed with an investigation. Additionally, international law enforcement relies on Mutual Legal Assistance Treaty (MLAT) requests to formally obtain financial and other information from governments. These requests can further complicate the investigative process due to their complex and time-consuming nature.
For example, if a U.S. law enforcement agency wanted to request corporate ownership documents from the Cayman Islands, an agent would have to draft a lengthy MLAT memo and seek approval from a local federal prosecutor. The agent would then work with the Department of Justice’s Office of International Affairs to finalize the MLAT request which is ultimately sent to Embassy officials via U.S. government channels. Typical MLAT requests take approximately 9 to 12 months to process and deliver information back to the investigating agent and prosecutor.
If the process is this difficult for law enforcement to investigate shell companies, then how can we expect financial institutions and corporations to efficiently mitigate FCPA risks often entangled in shell company investigations? The practical answer is advanced data analytics solutions that provide artificial intelligence (AI) enhanced link analysis and data visualization of connections between entities and networks.
Link analysis can be employed to augment traditional KYC and KYCC (Know Your Customer’s Customers) processes, creating multilayer and hierarchical networks for relationships between customers, their organizations, suppliers, and business partners. Through data visualization, large and complex data sets can be presented so that they can be easily understood by non-technical investigative staff.
The above graphic illustrates the connections between accounts, customers and other entities that QuantaVerse prepared with a client’s transactional and case management data. Mitigating shell company risk can be achieved by giving compliance teams the ability to see and identify connections that are often hidden in massive data sets. Advanced data analytics and AI can help financial institutions address the risk of unidentified shell corporations through link analysis and data visualization.
Packaged financial crime solutions are built from the ground up by proven AI experts working alongside experienced AML professionals. Unlike other AI for AML approaches, packaged AI solutions are quick to implement and are continuously updated by the solution provider based on input from multiple customers and regulators.
Serving almost 20 million customers, the bank was concerned about the risks associated with false negatives that its current AML compliance technology was missing. Intent on driving financial crime out of its operation, the bank began searching for a solution that could enhance its existing rules-based transaction monitoring system (TMS) and minimize the risk related to undiscovered financial crime.
While all agree the promise of AI for AML is great, taking the steps to select and implement AI within an institution is relatively new. So, what is the best approach for implementing AI and machine learning into your efforts to combat financial crime?