ThetaRay AI Tech to Monitor African payments for ARCA

ThetaRay, a provider of AI-powered transaction monitoring technology, announced that ARCA, a premier African payment services provider, will implement ThetaRay’s advanced SONAR SaaS anti-money laundering (AML) and sanctions list screening solution for transactions on its open AI-based platform.

“Instant payments have become the new norm in the digital ecosystem, and ARCA is a leader in driving this revolution in the African financial system,” said Mark Gazit, CEO of ThetaRay. “ARCA prioritizes trust, confidence, and quality. We are thrilled to build this partnership and help facilitate both the growth of their business and expansion of the world economy by enabling financial inclusion.”

ARCA is the first Nigerian FinTech to adopt ThetaRay’s advanced SONAR solution, an industry renowned for detecting the first signs of sophisticated financial crime. ARCA provides advanced digital payments for an open banking ecosystem, helping expand innovative and inclusive financial services throughout Africa.

“Our mission is to provide feature-rich financial solutions delivered through an open and flexible digital platform, through the use of cutting-edge technologies,” said Alex Umeh, Chief Information Security Officer at ARCA. “ThetaRay’s SONAR is a perfect fit. Its advanced machine learning and algorithms can instantly spot any attempts to launder money or circumvent sanctions, no matter how sophisticated. This will help us to create new lines of revenue, better serve our customers, and continue to remain compliant with regulatory requirements.”

SONAR is based on an advanced form of AI that makes better decisions with no bias or thresholds. It enables FinTechs and banks to implement a risk-based approach to effectively identify suspicious activity and create a complete picture of customer identities across complex, cross-border transaction paths. This enables the rapid discovery of both known and unknown money laundering threats, with a 99% reduction in false positives compared to rules-based solutions.