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Overview

Minerva uses publicly available data from over 250,000 reputable and relevant news sources including national & regional outlets, radio, television and video news sites, professional journals, and blogs in 147 languages.

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How do we find adverse media and detect risk?

Minerva uses trained machine-learning models to evaluate new articles in real time through the UI and API. Our AI models conduct sentiment and risk analysis for each article that may pertain to the subject of the search:
  • Sentiment analysis is responsible for determining the tone of the article as “negative”, “positive”, or “neutral”
  • Risk analysis scores the supported financial and non-financial adverse media categories that are enabled for the applicable workspace and screening channel
Sentiment and risk analysis work together to strengthen the overall adverse media result. Role-aware adverse media can add another article-level signal by checking whether a negative article appears to involve the screened subject or is likely only a name mention. For configuration details, see the Role-Aware Adverse Media Guide.

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Adverse Media Categories

Minerva supports 32 category keys. The standard configuration enables 28 financial crime categories and the legacy Other (Non-Financial) category. DUI, Property Damage, and Assault and Battery are available but disabled by default so they do not appear unexpectedly in existing integrations. Administrators can add or remove categories independently for Onboarding, Ongoing Monitoring, Direct API Calls, and Risk Assessments. API integrations can also override the category set for one synchronous search or one entity in a batch. See Adverse Media Categories for:
  • the complete financial and non-financial category reference
  • the AML scope of financial crime adverse media
  • workspace and four-channel configuration steps
  • request-level search-sync and batch search overrides