Wayne, PA (June 7, 2017) – QuantaVerse, innovator of data science and artificial intelligence (AI) solutions purpose-built for identifying financial crimes, today announced an analysis that financial institutions can use to test the effectiveness of their AML programs. The CCO (Chief Compliance Officer) Checkup is a service whereby QuantaVerse’s AI solution will analyze transaction data to detect “false negatives” or anomalous behaviors that may have been missed by an institution’s existing transaction monitoring system (TMS).
These risks take the form of fines, penalties and forfeitures for violations of BSA and other regulations. Despite increased investments in AML systems, this risk is increasing year over year. In 2016 alone, $42 billion in fines for AML non-compliance were paid to federal regulators by the largest North American and European financial institutions.
Transaction monitoring technology is essential for the maintenance of an effective AML program. However, transaction monitoring systems are failing to flag many transactions that represent serious risks for financial institutions. Legacy TMS are incomplete because they rely solely on rules-based engines. If a financial crime does not violate a stated rule, the TMS won’t flag it. Unlike static rules-based TM engines, AI-enhanced systems are dynamic in that they can detect patterns of behavior and analyze the intent of those patterns to identify suspicious activities.
The QuantaVerse solution also provides financial institutions with a virtuous feedback loop that improves performance of their TMS by recommending new rules for catching new criminal behavior patterns that are uncovered during AI analysis. This provides financial intuitions with documented guidance on TMS rule scenarios and model tuning and drives continuous improvement of their AML efforts over time.
Financial institutions can take full advantage of QuantaVerse’s CCO Checkup with minimal effort and no long-term commitment. Included in the CCO Checkup, participating financial institutions receive a QuantaVerse Financial Crimes Report (FCR) detailing the five cases scored highest by the AI solution and representing the greatest risk to the institution. The QuantaVerse FCR also includes all the supporting documentation necessary for the case to be efficiently analyzed by an AML investigator and all that is required for a suspicious activity report (SAR) to be created if indicated.
In one CCO Checkup, the analysis of a single month’s worth of previously unflagged transaction data detected thousands of end-clients who had originated suspicious transactions, representing tens of millions of dollars of likely illicit cash flows. One end client, in particular, generated suspicious transactions totaling millions of dollars that were worthy of closer examination.
“Money laundering continues to be a serious problem for banks. Fortunately, advancements in AI and machine learning technologies promise to greatly reduce the risk these transactions represent,” explains David McLaughlin, CEO and Founder of QuantaVerse. “Our CCO Checkup provides financial institutions a fast method to evaluate an AI-powered solution, while also dramatically lowering BSA risks. They will see first-hand how AI can easily improve upon the effectiveness of their existing TMS and AML processes while greatly reducing their exposure to fines and reputational damage.”
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 or visit www.QuantaVerse.net.
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?