By AML Intelligence Correspondents
LYNX Tech, an AI-driven software company, has launched a new anti-money laundering (AML) screening solution to help financial institutions detect high-risk individuals and entities.
Lynx’s solution uses Natural Language Processing (NLP) and machine learning. These technologies improve name similarity scoring and reduce false positives. The system is also highly configurable, allowing institutions to adapt to evolving regulations.
Lynx Tech AML tool aims to address gaps
The rise of global sanctions, increased scrutiny of Politically Exposed Persons (PEPs), and evolving evasion tactics have exposed weaknesses in conventional screening. Many legacy systems are slow and generate too many false positives, creating inefficiencies.
Lynx Tech’s solution aims to address these challenges. According to the company, it can screen hundreds of transactions per second. It also has an average response time of less than one second and a false positive rate below 1%.
“The landscape of financial crime is shifting rapidly, and financial institutions need a solution that not only keeps up – but gets ahead,” said Dan Dica, CEO of Lynx Tech. “Our AI-driven AML screening helps institutions remain compliant while reducing unnecessary alerts and operational strain.”
Real-time screening
The solution supports over 100 languages and allows businesses to modify screening logic in real time. It also offers:
- Real-time transaction screening for faster detection
- Scalable infrastructure to handle different transaction volumes
- Cloud-native deployment for quick implementation
- Real-time insights and key performance indicators (KPIs) for better oversight
Becki LaPorte, strategic advisor in the fraud and AML practice at Datos Insights, emphasized the need for advanced screening. “In a world marked by constant turmoil, the intricacies of economic sanctions have become increasingly complex,” she said. “It is important to have a solution that addresses the demand for faster payments while applying a risk-based approach to effectively weed out threats posed by bad actors.”
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