AI reads context, not keywords.
Adverse media monitoring across domestic and international sources, classified by category and severity. A name in a headline is not a signal: it is noise. The engine tells a mention from involvement.
Adverse Media with AI.
See how VAAS eliminates false positives with contextual intelligence, comparing name, age, location, date and news severity.
José Cardoso investigated for money laundering in Rio de Janeiro
The Federal Police launched an operation on Monday against a money laundering scheme in Rio de Janeiro. Businessman José Cardoso, 58, resident in the Copacabana neighborhood, is identified as the main organizer of a scheme that moved more than R$ 12 million between 2015 and 2017.
"José Cardoso" vs "José de Cardoso Freitas". Names partially similar, high chance of homonym.
No proceedings found linked to the subject.
Subject is 34 years old. Headline mentions a 58-year-old person.
Subject in São Paulo, SP. Headline refers to Rio de Janeiro.
Article from 2018, single publication. Checking for duplicates.
Article involves money laundering, a high-severity financial crime. Reliable source.
Keyword search drowns the signal in noise.
Three pain points that travel together in AML/CFT, reputational exposure, and periodic portfolio review.
Namesakes and headlines become false alarms.
A literal name search returns every mention: the namesake, the story where the person is a victim, the passing reference. The desk gets hundreds of "signals" and loses trust in the tool that shouts more than it points.
Manual periodic review cannot cover the portfolio.
Regulation requires reviewing the base under the risk-based approach. Doing that article by article, client by client, is not viable. So the review delays, becomes annual, and misses the event at the moment it matters.
No category or severity means everything looks the same.
An environmental fine and a citation on an unknown blog do not carry the same weight. Without classifying by category (money laundering, fraud, corruption, environmental, labor) and by outlet, the desk treats noise and threat with the same urgency.
Everything in a single call.
Public and private sources queried in parallel, normalized and weighted by the use-case matrix.
2,840 articles scanned. 4 that matter.
The engine reads the volume, discards the noise, classifies by category and severity, and prioritizes national-coverage outlets. The desk receives what is signal, with the source.
Recommendation: Review risk policy. 4 medium-severity signals in the environmental category, 2 of them in TIER 1 national-coverage outlets. AI discarded 188 mentions as noise. Suggested: review the matrix and contact the client before the next renewal.
A name in a headline is not a signal. The engine reads the article context and separates victim, witness, and passing reference from actual involvement.
Money laundering, fraud, corruption, environmental, labor - each signal tagged and scored. The desk prioritizes by weight, not by arrival order.
National coverage weighs more than an unknown blog. Outlet-based prioritization puts what has reputational reach at the top of the queue.
What the rules already require of you.
Adverse media is a mandatory component of the risk-based approach. Four instruments make continuous monitoring a duty, not an option.
AML/CFT and risk-based approach
Requires continuous monitoring and periodic review of the client risk profile. Adverse media is a core input for reassessing risk throughout the relationship.
AML prevention in capital markets
Market participants must maintain monitoring routines and identify suspicious transactions. Adverse media signals feed the analysis.
AML/CFT in the insurance sector
Insurers and reinsurers follow the risk-based approach with periodic review. Media monitoring is part of the set of signals reassessed.
Suspicious transactions and situations
Relevant adverse media signals may form part of the basis for reporting to COAF. The source trail supports the decision to report or not.
From kickoff to go-live in 4 weeks.
Multi-tenant architecture. What changes are the relevant categories, outlet weights, and portfolio review cadence for each client.
Discovery & scope
Mapping relevant categories, priority sources, and portfolio review cadence.
AI calibration
Tuning categories, outlet weights, and severity thresholds. Validating the noise discard rate.
Directed pilot
Sweeping a slice of the portfolio. First review reports for compliance validation.
Go-live
Full portfolio under continuous monitoring. Automated periodic review. Category alerts active. Team trained.
Five modules, one decision engine.
One flow from data intake to continuous monitoring. Explore the other platform modules.
Workflows
Orchestrate data, rules, AI agents and the desk in auditable flows. No deploy for every new rule.
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Artificial Intelligence
Agents read the dossier, vote alongside the analyst and justify every decision.
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Decision Desk
Queue, authority, dossier and committee in one screen. Human and AI decide together.
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Risk Hub
Continuous monitoring of people and companies. Automatic re-analysis when something changes.
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Datahub
+40 integrated bureaus, normalized into typed and auditable variables.
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Start with a meeting.
In 15 minutes we show how VAAS works in your scenario, with your rules, your data, your volume.