Wayne, PA, July 19, 2016 – QuantaVerse, a data science company founded specifically to help financial services companies, today announced it has secured new financing from private equity investors. This latest round of funding has raised the current investment in QuantaVerse to $1.5 million, and will enable QuantaVerse to accelerate its growth and scale its advanced data science platform to meet growing demand by banks and financial services firms.
Purpose‐built for the banking industry and its unique business and compliance challenges, QuantaVerse solutions apply data science techniques, technology, automation and financial industry insider know‐how to help banks address their AML (Anti‐Money Laundering), BSA (Bank Secrecy Act), KYC (Know Your Customer) and other related requirements and regulations. QuantaVerse solutions are comprised of processes and systems designed to examine very large internal and external, structured and unstructured volumes of data in order to produce insights and identify suspicious relationships and patterns that can indicate money laundering and other illegal activity.
“This latest round of financing is further validation of our data science‐powered solutions and proven go‐to‐ market strategy,” said David McLaughlin, CEO of QuantaVerse. “Support from our equity investors is enabling us to accelerate the advancement of our technology which will reduce the risk of our customers running money laundering, terrorism financing, drug trade and other financing crimes through their banks and, in doing so, decrease the cost of regulatory compliance.”
“The QuantaVerse team is in the midst of building something really remarkable and I’m proud to support their efforts,” said Arthur Spector, Managing Director of Safeguard International. “Their management team has decades of experience in the financial services industry and understands the complexity of the issues. I believe the software platform they have developed will make a difference in fighting financial crime.”
Led by a seasoned group of financial industry veterans, QuantaVerse’s management team includes Founder and CEO David McLaughlin, COO Phil McLaughlin and CTO Kelly Torrence. Other key executives at QuantaVerse include Senior Software Engineer Oleg Koslovsky and Data Scientist Dr. Leandro Loss, PhD.
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.
Report: How Financial Firms Are Using Artificial Intelligence and Machine Learning to Meet AML Demands of Today and Tomorrow
It’s a well-known fact that the global pandemic caused a radical shift in consumer banking and payments behavior. What isn’t as obvious is how financial institutions responded behind the scenes. Fortunately, a new study helps shed light on the pandemic’s impact on the adoption of new technologies for anti-money laundering (AML) efforts.
Regulators and those handling compliance at covered institutions have long accepted the pitiful state of AML program efficacy, including: An estimated $2 trillion laundered through the global banking system annually 90+% of false positives coming from transaction...
While not mandating that firms invest in technology to automate financial crime investigations, regulators are certainly encouraging it. They are noticing that advanced BSA/AML teams are using robotic process automation (RPA) bots to gather data for investigations. They are aware that those same firms are using machine learning to analyze huge data sets, identify patterns, and pinpoint where exceptions or anomalies exist.