DataVisor, the company delivering comprehensive AI-powered fraud management solutions across industries, today announced expanded operations in EMEA to address the growing need for its solutions. The company has emerged as a leader in fraud management solutions globally and is seeing more demand in the region.
DataVisor already has great traction in the area – several household names in leading marketplaces and in gaming use DataVisor to detect and prevent fraud. Now, the company has its sights on banking and financial services, as GDPR settles in and newer payment flows as a result of PSD2.
“Fraud is a growing problem for companies in banking and financial services globally and particularly in EMEA as merchants and banks adapt to regulatory changes from GDPR and PSD2,” said Dr. Yinglian Xie, chief executive officer at DataVisor. “Today, would-be fraudsters are using increasingly sophisticated methods and tools to commit cybercrimes, and it is exceedingly difficult to block their efforts.”
Richard Biss, who recently joined DataVisor as head of Operations for EMEA, said: “We are in a great position to expand our presence in EMEA as we are in some of the most challenging times for organisations trying to address and remediate against the constant threats from sophisticated organised crime syndicates.”
“I’m looking forward to leading DataVisor’s continued success in one of the most challenging periods for fraud management in the EMEA region,” said Biss. “With continued growth of online banking, CNP transactions and third-party payment frameworks, cyber-enabled fraud continues to be a key battleground for financial services. As we see these new fronts unfold, we also see a great opportunity to grow our presence in the region by continuing to work with our customers and educating the financial community on mitigating these threats.”
The DataVisor Platform combines transformational AI and machine learning technologies to correlate fraudulent and suspicious patterns across billions of accounts in real-time.
Patented and proprietary unsupervised machine learning approach works without ‘labelled’ input data to automatically detect new and previously unidentified fraud and abuse patterns, making it perfect for keeping up with unknown and sophisticated fraud attacks.