Wayne, PA, February 28, 2017 – QuantaVerse today announced that its artificial intelligence (AI) and data science-powered technology platform is uniquely designed to help banks and other regulated institutions comply with risk-based Anti-Terrorism and Anti-Money Laundering (AML) regulations, including the rule recently put into effect by the New York Department of Financial Services (DFS).
New York State’s Anti-Terrorism Transaction Monitoring and Filtering Program Regulation requires New York DFS-regulated financial institutions to “maintain programs to monitor and filter transactions for potential Bank Secrecy Act (BSA) and AML violations and prevent transactions with sanctioned entities, and certify compliance with the regulation annually to DFS.” Significant to this new regulation is that institutions must review their transaction monitoring programs to ensure they comply with regulatory safeguards. Institutions must also adopt either an annual board resolution or senior compliance officer attestation to certify compliance with the DFS regulation beginning April 15, 2018.
QuantaVerse is uniquely able to provide institutions and their senior compliance leadership with data-driven solutions to enhance their existing transaction monitoring systems. QuantaVerse employs state-of-the-art proprietary algorithms to find hidden risks in the flood of transaction data that institutions deal with daily. QuantaVerse’s data analysis is powered by artificial intelligence that helps institutions identify where their risks are today and where they are migrating.
Leveraging QuantaVerse’s AI and data science-powered platform, financial institutions can address the New York DFS regulation in the following ways:
- Quickly identify counter-terrorism finance (CTF) red flags through CTF-specific algorithms;
- Provide predictive analysis of customers who might engage in CTF typologies in the future; thereby enhancing transaction monitoring and Know Your Customer (KYC) effectiveness, and;
- Provide the Board or senior compliance officers with added assurance that their transaction monitoring program adheres to the regulation before they sign their annual attestation.
Historically, financial institutions have relied on ineffective rules-based monitoring systems that lead to excessive false positive alerts as well as case management software to track manual and time-consuming investigations. An automated, AI-based approach, however, can be much more efficient, effective and cost-effective than traditional data analytics approaches.
“QuantaVerse applauds the New York DFS for enacting these regulations,” explained David McLaughlin, CEO of QuantaVerse. “Strengthening BSA and AML compliance among financial institutions is essential to choking off the money that funds terrorism and other illegal activities. With our AI and data science platform, financial institutions’ compliance leaders and AML teams can have confidence that hidden risks are being discovered and criminals are being stopped from laundering money through their organizations.”
QuantaVerse is the emerging leader in data science‐powered risk reduction and revenue growth solutions, purpose‐built for the global banking industry. Founded by financial services industry veterans and innovators, QuantaVerse solutions employ proprietary data science algorithms, integrate and filter internal bank data and related external data – including public Internet data, unindexed deep web data and government and commercial datasets – to help the global banking industry to significantly improve their compliance with AML, KYC and BSA regulations and requirements. QuantaVerse solutions also drive revenue by turning KYC data into strategic insights about the markets and customers they serve. To learn more how QuantaVerse can help your financial institution, please contact us 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?