Wayne, PA (February 20, 2018) – QuantaVerse, the first in the market with artificial intelligence (AI) solutions purpose-built for identifying financial crimes, finished 2017 strong as adoption of AI and machine learning to identify suspicious financial activity continues to accelerate.
The QuantaVerse AI Financial Crime Platform helps financial institutions and corporations effectively address multiple types of financial crime. Not only does it assist in addressing AML (Anti‐Money Laundering) and FCPA (Foreign Corrupt Practices Act) regulatory and compliance requirements, it effectively identifies suspicious activities that may indicate money laundering, terrorism financing, fraud, bribery or corruption. Increased certainty in the ability to accurately evaluate entity and transactional risk frees organizations to confidently operate in markets and industries that have historically been considered uncertain.
“The success of QuantaVerse can be largely attributed to our focus on developing an AI and machine learning platform dedicated to finding financial crimes. Being ahead-of-the-curve in product development has created advantage for us in the AML space and has sped our expansion into addressing other financial crime challenges,” explained David McLaughlin, CEO and Founder of QuantaVerse.
Key milestones contributing to the company’s success in 2017 include:
- Product Line Expansion. QuantaVerse introduced two “checkup” services that allow compliance and AML teams to test the effectiveness of AI first hand. QuantaVerse’s AML-specific CCO (Chief Compliance Officer) Checkup analyzes banks’ transactional data to detect false negatives missed by transaction monitoring systems (TMS), while the audit-focused CAE (Chief Audit Executive) Checkup speeds the ability of audit departments and teams to analyze how AML and FCPA compliance is performing.
- Customer Acquisitions. Leveraging its AI Financial Crime Platform, QuantaVerse has screened and analyzed transactions around the globe and are currently working with a variety of organizations including global banks, community banks, data providers, money services businesses, regulators, law firms, global corporates and cryptocurrency exchanges.
- Expansion into new markets. QuantaVerse has expanded its work beyond anti-money laundering into new market segments and is presently engaged with blockchain-based companies as well as corporations concerned with internal fraud, bribery and corruption, and needing to address FCPA requirements.
- Strategic Partnership with Liberty Asia. QuantaVerse partnered with Liberty Asia, a global NGO that aims to prevent human trafficking through legal advocacy, technological interventions, and strategic collaborations with NGOs, corporations, and financial institutions. Through the partnership, QuantaVerse receives training and frequent data updates related to entities involved with human trafficking and the sex trade.
- Awards and Recognitions. QuantaVerse was named a finalist in the “technology startup” category at the annual PACT Enterprise Awards, a prestigious program honoring top technology and life sciences companies, leaders, and entrepreneurs. QuantaVerse also presented at key industry conferences including ACAMS New York, BAFT Global Payments Symposium, FATF FinTech and RegTech Forum, Blockchain Economic Forum and more.
The increased adoption of its solutions in 2017 led the company to add new funding which will be used in 2018 to increase market share and expand product development and customer support teams.
Each year, trillions of dollars are laundered through global banking system. That money supports numerous illicit activities including human trafficking, drug trade, terrorism, tax evasion, and the lavish lifestyles of criminal networks. Other financial crimes, such as bribery and corruption, have an adverse effect on both corporations and countries. The expensive penalties stemming from FCPA enforcement actions continue to increase year over year.
Traditional financial crime detection tools and methodologies used to find financial crimes, such as Transaction Monitoring Systems (TMS), have proved ineffective. The industry estimates that approximately 95 percent of transactions flagged by TMS are actually legitimate in nature while 50 percent of money laundering trafficked through global banks is missed altogether. QuantaVerse’s approach marries its AI-powered platform with customer-provided data and myriad other datasets to more efficiently and effectively identify financial crimes. Through this approach, QuantaVerse helps organizations reduce regulatory and reputational risk by identifying anomalous data patterns related to both known and not yet identified financial crime typologies.
QuantaVerse is a leader of artificial intelligence (AI) and machine learning solutions purpose-built for identifying financial crimes. Utilizing its AI Financial Crime Platform, QuantaVerse integrates and filters institutional data and related external data – including public Internet data, unstructured deep and dark Web data, as well as government and commercial datasets – to help its customers comply with AML (Anti-Money Laundering), KYC (Know Your Customer) and FCPA (Foreign Corrupt Practices Act) regulations and to rid their institutions of money laundering and other financial crimes. 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?