Quantifind, the proven AI leader in Risk Intelligence, proudly announces its new Payments Risk Intelligence solution, set to transform the landscape of payments risk management with unmatched accuracy and scalability. This new solution offers purpose-built artificial intelligence (AI) to provide a comprehensive and holistic risk analysis approach that spans pre-, during, and post-alert generation from the first customer assessment through payment transactions.
Limitations of Traditional Payment Risk Management
Detecting risk amidst the rapid flow of payments poses a formidable challenge. Criminal activity seamlessly blends into payment chains, necessitating sophisticated methods to identify potential threats. Examining traditional payment risk management reveals two extreme approaches. On one end, sanctions enforcement focuses on screening transactions for compliance with international sanctions. However, traditional transaction screening methods are often ineffective due to their high rate of false positives. The static nature of these screening rules allows sophisticated criminals to adjust their tactics and evade detection. At the same time, reliance on predefined lists limits the system’s ability to adapt to new and emerging threats.
On the other end of the spectrum is transaction monitoring. These applications face significant limitations that undermine their effectiveness in detecting and preventing financial crimes. Typically implemented as on-premises solutions, they rely on static rules and simplistic AML scenarios, resulting in a high volume of false positives. Traditional monitoring is reactive and periodic, failing to keep pace with evolving money laundering techniques and allowing criminals to exploit gaps between monitoring periods. This inability to adapt to dynamic financial crime patterns makes traditional transaction monitoring increasingly inadequate.
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A New Approach is Necessary
Quantifind Payments Risk Intelligence analyzes customer and counterparty behavior at pre-alert generation and post-alert phases, which is done with unprecedented scalability, speed, and accuracy. This changes the philosophy from reactive anomaly alerting to a proactive understanding of the risk of counterparties, automated network analysis, and a SaaS solution commitment to workflow optimization and installing automated steps efficiently and thoughtfully.
“In today’s fast-moving payment landscape, traditional surveillance methods are insufficient to protect against financial crime,” said Scott Nathan, Managing Director at Citi. “Scalable AI solutions are needed to navigate the massive reach of modern commerce and identify potential risks. Partnering with Quantifind offers cutting-edge AI technology to enhance payments surveillance capabilities with reduced false positive rates, even when the counterparties are not well known.”
SOURCE: PRNewswire