Wayne, PA (May 11, 2017) – QuantaVerse, innovator of data science and AI solutions purpose-built for the identification of financial crimes, has released a new paper that explores how the application of artificial intelligence (AI), machine learning and big data strategies enables banks and financial institutions to surpass the risk-based customer due diligence (CDD) or “Fifth Pillar” requirements by the May 11, 2018 applicability date.
On May 11, 2016, FinCEN published a Final Rule designed to formalize new and existing CDD requirements. The Final Rule codified four anti-money laundering (AML) provisions, or “pillars,” found in Section 352 of the USA Patriot Act and added a “Fifth Pillar” which requires covered institutions to either 1.) identify and verify the identity of the beneficial or true owner(s) of the account by determining who directly or indirectly owns 25 percent or more of the equity interests of the legal entity customer; or 2.) determine which individuals control, manage, or direct a legal entity customer, including an executive officer or senior manager, or any other individuals who regularly perform similar functions.
Banks and financial institutions, faced with inadequate approaches to obtaining beneficial ownership information, are left with the prospect of “de-risking.” De-risking requires terminating or significantly restricting business relationships with clients and correspondent banks where information about transactions and entities, such as beneficial ownership data, is hard to secure or difficult to analyze. For banks, de-risking slashes entire sources of correspondent-related revenue. For various regions of the world, de-risking threatens to cut off access to the global financial system which the World Bank warns will threaten financial inclusion and drive up costs for remittance. According to estimates from the World Bank, global remittances will increase to more than $636 billion in 2017. Lack of access to financial services due to de-risking has even hindered the delivery of humanitarian assistance to refugees of political conflicts and natural disasters. Ironically, despite the intentions of AML-related regulations, resulting de-risking also pushes criminal and terrorist financing out of regulated systems where it becomes much more difficult to track.
Today, expensive and highly manual processes of conventional KYC and CDD programs plague banks and financial institutions. The traditional method of verifying the beneficial or true owner(s) of an account routinely fails to identify connections between customers and their underlying motives. Leveraging the new technologies of data science, such as AI, machine learning and big data strategies, QuantaVerse helps banks and financial institutions reduce the risk of money laundering while supporting KYC requirements. The QuantaVerse platform can automatically address FinCEN’s Fifth Pillar requirements through tactical and strategic financial crime-focused algorithms, coupled with independent and external sources of information. The QuantaVerse platform can also define specific risk factors for affiliated institutions or customers and predict future risk trends.
“While banks and financial institutions have long complied with Know Your Customer (KYC) regulations and requirements, many have treated KYC as a ‘check-the-box’ function rather than a way of keeping money laundering and other financial crimes from infecting their institution and increasing their risk of sanctions and fines,” explained David McLaughlin, CEO and Founder of QuantaVerse. “QuantaVerse’s AI-based approach enables banks and financial institutions to surpass regulatory required KYC decisions and root financial crimes out of their operations.”
QuantaVerse is the emerging leader in data science-powered risk reduction solutions, purpose-built for identifying financial crimes. Utilizing proprietary data science algorithms including artificial intelligence (AI), machine learning and big data technologies, QuantaVerse integrates and filters institutional data and related external data – including public Internet data, unstructured deep web data, as well as government and commercial datasets – to significantly improve AML, KYC and BSA compliance and prevent money laundering and the crimes it supports. For more information, contact QuantaVerse at (610) 465-7320.
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?