Ripjar’s Labyrinth Screening saves you time and increases accuracy when screening for adverse media, watchlists, sanctions and PEPs. The platform uses advanced natural language processing and machine learning to extract entities and accurately identify risk, using structured and unstructured data from multiple sources. Innovative, industry-leading risk profiles provide you with a detailed view which reduces false positives and significantly improves analyst efficiency.
- Processing c. 3M articles per day from multiple sources
- Data Agnostic
- Utilise NLP in 21+ languages to provide contextual risk analysis
- High quality entity extraction and risk association
- Combine structured and unstructured data
- Specification classification of critical client risks
- Next generation name matching including cross-language, cross-script matching
- Available as SaaS, private-cloud and on-premise deployment
- Highly secure, built with Eexpererience of managing top-secret data
- Sophisticated data and match tuning
High quality data classification is extremely powerful at reducing false positive matches against adverse media data.
Next generation name matching decreases both false positives and false negatives, while enabling customers to meet regulatory expectations.
Risk Profiles massively reduce the operational requirement for reviewing client screening alerts and assessments massively, providing customers with ability to optimise workloads.
An oil and gas company implemented Laybrinth Screening and saw an increase in Recall (detection of risk) from 42% to 87%.
A tier-1 global bank implemented the system and saw Recall (detection of risk) rise from 69% to 82% with a reduction of false positives from 60 per query to 5 per query.