The threat of money laundering between highly-coordinated networks of cyber criminals is growing year-over-year. These intelligent attacks involve complex interactions between multiple accounts and players, and they often result in massive financial loss and reputational damage for companies across a host of industries. Today, companies are too often forced to choose between keeping costs down and risking high regulatory fines, or paying top dollar to stay compliant. DataVisor provides advanced machine learning-powered fraud solutions that can stop would-be attackers and sophisticated crime rings before they cause harm. Proprietary unsupervised machine learning algorithms detect hidden networks of suspicious activity by analyzing enormous quantities of data across accounts and customers.
Early detection stops money launderers from using stolen or synthetic identities to create fraudulent accounts to move and transfer funds.
Unsupervised machine learning algorithms look at complex networks of transactions instead of individual ones, and can detect and eliminate launderers who deposit small denominations of funds to avoid CTR reporting.
Real-time machine learning recognizes and puts a stop to fraudsters that attempt to mask money flow by transferring funds across multiple mule accounts.
Catch sophisticated money launderers with the comprehensive fraud management solution that combines unsupervised machine learning algorithms with powerful enterprise workflows. Perform global network detection to holistically reveal money laundering patterns, while lowering false positives and ensuring operational efficiency.
Detect hidden networks of money laundering activity with a robust fraud detection solution that combines adaptive machine learning technology and powerful investigative workflows to deliver real-time fraud analytics. Triage alerts effectively, and build a robust AML infrastructure to prevent reputational and financial damage.
Current solutions for AML monitoring evaluate risk at the transaction level. This is an outdated method that results in a high volume of alerts, particularly false positives. Legacy models also require constant retuning as fraudsters learn and employ new techniques to avoid detection. The DataVisor approach is to take a holistic view of the relationships between accounts, to identify hidden patterns and catch cybercriminals in the act. This dramatically increases rates of detection and reduces the time and effort required for investigations. Our solutions also enable companies to meet strict compliance requirements by creating human-understandable rules.
Adapt to new and evolving money laundering tactics and techniques; stop fraudulent activities in real time.
Automatically receive linked accounts and suspicious attributes to accelerate investigations.
Bridge the gap between machine learning techniques and compliance requirements with human-understandable reasons.
DataVisor partnered with a top U.S. credit card issuer to deliver a scalable solution to fight application fraud.
5 stories. 5 victories against fraud. See how organizations across industries are proactively defeating attacks.
Learn how financial institutions are successfully saving millions in the fight against fraud.
Intelligent solutions. Informed decisions. Unrivaled results.
Detect, deter, and defeat known and known money-laundering attacks.
Access proprietary data and research results to discover the latest attack techniques and prevention strategies.
Learn more about how DataVisor uses unsupervised machine learning to reduce false positives and false negatives.
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