Adverse media screening is the process of checking customers, counterparties and other entities against global news and media sources to identify allegations of financial crime, fraud, corruption or other risk. It is a core component of anti-money laundering (AML) customer due diligence programmes.
You've always faced a trade-off when it comes to adverse media screening. Human-curated data is entity-centric but narrow in coverage. Machine-curated articles cast a wider net but flood your analyst queues with irrelevant matches.
Ripjar eliminates the trade-off. AI and machine learning transform over 6 billion news articles, and approximately 6 million new ones every day, into customer-specific Dynamic Risk Profiles, matched to your customers using Ripjar's proprietary name matching. The result: broad coverage, concentrated risk, dramatically fewer false positives.
Regulators around the world increasingly expect you to screen against adverse media to identify allegations of money laundering, terrorist financing and other risks early. The problem is execution.
Take a broad view with manual searches and you drown in low-quality matches. Narrow the scope and you miss genuine risk. Screen a common or famous name and the signal disappears entirely beneath irrelevant results. As regulators like MAS, the AMLA and FCA raise expectations, banks are being asked to screen more of their book, or their entire book, against media. The economics of traditional approaches simply don't hold.
“The economics of traditional adverse media screening break down at enterprise scale.”
Built by intelligence experts, proven with customers, scaled with partners.
Adverse media at scale — process time fell from 20 minutes to 3 minutes per review. 100% traceable decisions.
From less than 1M to over 10M names. 90% fewer false positives. 21× faster processing.
Entire customer book screened with existing headcount. 80% reduction in analyst workload.
Ripjar is data-agnostic by design. It integrates with sanctions lists (OFAC, EU, UK, AUSTRAC and others), PEP databases, a range of adverse media sources and your own internal datasets. There is no lock-in to a single data provider.
Adverse media screening is the process of checking customers and counterparties against global news and media sources to identify allegations of financial crime, fraud, corruption or other risk. It is a core component of AML customer due diligence and ongoing monitoring programmes.
Keyword-based screening searches for name matches in news articles, generating high volumes of irrelevant results — particularly for common names. Entity-based screening resolves each article to a specific person or organisation, filtering noise at source and surfacing only contextually relevant risk.
Ripjar provides natural language processing across 22+ languages, with named entity recognition spanning 50 languages. The adverse media corpus covers over 6 billion articles, with approximately 6 million new articles appearing every day.
Ripjar updates its adverse media data twice daily. With approximately 6 million new articles published every day, twice-daily updates ensure screening decisions reflect the most current risk landscape.