Wayne, PA (July 10, 2018) – QuantaVerse, the first in the market with artificial intelligence (AI) solutions purpose-built for identifying financial crimes, helps financial institutions file timely and accurate suspicious activity reports (SARs) that match FinCEN’s recently revamped SAR filing format in the BSA E-Filing System. The QuantaVerse Financial Crime Report automatically compiles information relevant for filing SARs including content related to FinCEN’s newly recommended fields and categories such as geographic targeting orders, IP address date/timestamping, and new/modified subtype selections associated with structuring, fraud, money laundering, gaming, identification/documentation, securities and mortgage fraud.
A critical output of the company’s Alert Investigator and False Negative Identification and Investigator solutions, the QuantaVerse Financial Crime Report details serious anomalous transactions including information on the transacting parties, transactional relationships, negative news surrounding entities, and money laundering and terrorism financing typologies. The QuantaVerse Financial Crime Report includes risk scores and all supporting documentation necessary for the case to be efficiently analyzed by an AML investigator and all that is required for a SAR to be created.
“We anticipate that this will reduce by up to 70 percent the time that investigators spend gathering critical facts and summarizing their research to support SAR filings. Our comprehensive Financial Crime Report helps financial institutions’ investigative teams automate data-gathering activities and apply human resources to things they are uniquely qualified for such as intuition and logic,” explained David McLaughlin, CEO and Founder of QuantaVerse. “With our Financial Crime Report, we are elevating the work of investigators to its highest and best use while radically increasing efficiency.”
The QuantaVerse Financial Crime Report meets regulatory and internal audit requirements for properly documenting, explaining and detailing the activity investigated. It contains sections for recommended action, identified risk, subject(s) identification, dates of identified activity, account number(s), number of transactions, amounts, transaction details including dates, negative news, and an investigative narrative that conforms to regulatory and internal audit requirements as appropriate for SARs and Suspicious Transaction Reports (STRs).
In the event that the QuantaVerse AI Solution determines that additional investigation and verification by a level-three or level-four investigator is necessary, the Financial Crime Report will state that “further investigation is recommended.” If an alert does not constitute suspicious activity (i.e., it’s a false positive), the report will indicate “no further investigation recommended.”
“Our customers appreciate having AI-derived insights, risk scores and supporting evidence on suspicious transactions served up to them in a clear and logical report,” said McLaughlin.
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?