Adverse Media Screener - KYC/AML Negative News Check

Screen a person or company for adverse media (negative news) for KYC/AML and enhanced due diligence. Give the actor one or more names and it returns categorized, LLM-classified hits - financial crime, fraud, corruption, sanctions, money laundering and more - each with the entity's role, a severity score, an entity-match confidence, and full source provenance.

No API keys, nothing to configure - names in, a triaged adverse-media report out. Search and the language model are included.

Adverse-media screening is a mandatory step in real KYC/AML and EDD workflows, and it is the one area sanctions/PEP lists do not cover. Most tools either dump raw news or bury "reputation risk" inside a broad bundle. This actor is purpose-built for adverse media and - crucially - filters out the false positives that keyword search produces.

Why the classification matters (the false-positive problem)

A naive keyword search for "Acme Corp" lawsuit flags Acme even when Acme is the one suing someone. This actor reads each hit with a language model and decides:

• Is the screened entity the wrongdoer? (perpetrator/defendant) - only then is it adverse. If the entity is the plaintiff, a victim, a regulator, or merely mentioned, it is not flagged.
• Is it even the same entity? Namesakes (a different person/company sharing the name) are dropped via an entity-match confidence.
• How serious is it? A severity score separates a major criminal case from a minor, old, or unproven mention.

Example: screening Patagonia Inc surfaces trademark lawsuits - but Patagonia is the plaintiff, so they are correctly not flagged as adverse. Screening Wirecard AG correctly returns fraud, money laundering, and embezzlement at high severity.

How much does it cost?

Pay per entity screened - you are not charged for Apify platform usage, only per name you screen (search and the language-model classification are included in that price, with no keys of your own to buy). See the Pricing tab on this page for the current figure. The free Apify plan returns a small preview. No subscription, no minimum.

Is it legal, and where does the data come from?

The actor searches open-web news and search results and classifies what it finds. It reads only publicly available news; it stores no personal data beyond the article metadata (title, URL, source, date, snippet) needed for audit. Output is media mentions attributed to their sources for analyst review - not verified allegations or a legal determination. Use it lawfully as one input to a KYC/AML/EDD process, and confirm any hit against primary records before acting on it. It is not a consumer report - do not use it for FCRA-governed decisions.

How do I use it?

Click Try it and screen one or more names:

{
  "entityNames": ["Wirecard AG", "Jan Marsalek"],
  "entityType": "auto",
  "country": "de",
  "minSeverity": "low",
  "maxHits": 25
}

Feed it the companies and people surfaced by the registry and UBO actors in the suite (see below) to complete the source -> screen loop.

Input options

Parameter	Type	Description
entityNames	array	One or more people/companies to screen. Each is billed and returned separately.
entityType	enum	auto / person / company - improves disambiguation.
aliases	array	Alternative names / transliterations (single-entity screening).
country	string	ISO code or name to bias the search and aid disambiguation.
categories	array	Only return these risk categories (optional).
minSeverity	enum	low / medium / high - minimum severity to return.
maxHits	integer	Max adverse hits per entity (1-50).

What data do you get?

One record per entity screened:

{
  "entityName": "Wirecard AG",
  "overallRisk": "high",
  "adverseHitCount": 6,
  "categoriesFound": ["fraud", "financial_crime", "money_laundering"],
  "hits": [
    {
      "title": "Wirecard Investors Drop Fraud Case ...",
      "url": "https://...",
      "source": "bloomberglaw.com",
      "publishedDate": "Nov 10, 2025",
      "snippet": "Wirecard invented fictional escrow accounts worth about $2 billion ...",
      "riskCategory": "fraud",
      "severity": "high",
      "entityRole": "perpetrator",
      "entityMatchConfidence": "high",
      "reason": "Wirecard AG is accused of inventing fictional escrow accounts."
    }
  ],
  "sources": ["Google News", "Google Web (adverse-term search)"],
  "screenedAt": "2026-06-12T...",
  "disclaimer": "Adverse-media mentions for analyst review - not verified allegations."
}

Field	Description
overallRisk	clear / low / medium / high - the worst severity found.
adverseHitCount	Number of genuine adverse hits returned.
categoriesFound	Distinct risk categories across the hits.
hits[]	Each adverse hit: source, date, snippet, risk category, severity, entity role, match confidence, and a one-line reason.
candidatesScreened	How many candidate articles were examined before filtering.

Risk categories: financial_crime, fraud, corruption_bribery, sanctions, money_laundering, terrorism, organized_crime, regulatory_enforcement, litigation, environmental, other.

How it works

1. Query expansion + search - news plus a web search with adverse-term expansion (fraud, investigation, sanctions, corruption, money laundering, ...). Aliases and country bias the search.
2. De-duplication - candidate hits are de-duplicated by URL.
3. Classification - every hit is classified for risk category, the entity's role, severity, and entity-match confidence.
4. Filtering - only genuine adverse hits survive (entity is the wrongdoer, confident match, meets your severity/category filters).
5. Provenance - every hit keeps its source, URL, and date for audit.

Search and the language model are included - no API keys of your own required.

The source -> screen flow (KYB in two halves)

This actor is the adverse-media check at the end of a KYB chain. Source the parties with the registry/UBO actors, then screen them here:

Company register -> UBO -> insolvency/debtors -> adverse media (this actor) is the standard onboarding chain.

What you can do with it

• KYC / KYB onboarding - the adverse-media step alongside sanctions/PEP checks.
• Enhanced due diligence (EDD) - investigate high-risk counterparties and the UBOs surfaced by the other actors.
• Ongoing monitoring - re-screen your book periodically for new negative news.
• Investigations & journalism - map allegations against a person or company.

Run it from code

# cURL - start a run and get the dataset back
curl -X POST "https://api.apify.com/v2/acts/regdata~adverse-media-screener/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{ "entityNames": ["Wirecard AG"] }'

// Node.js - apify-client
import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });
const run = await client.actor('regdata/adverse-media-screener').call({ entityNames: ['Wirecard AG'] });
const { items } = await client.dataset(run.defaultDatasetId).listItems();

# Python - apify-client
from apify_client import ApifyClient
client = ApifyClient("YOUR_TOKEN")
run = client.actor("regdata/adverse-media-screener").call(run_input={"entityNames": ["Wirecard AG"]})
items = client.dataset(run["defaultDatasetId"]).list_items().items

Integrations

Runs anywhere Apify does: call it from the REST API, schedule recurring re-screens of your book, pipe results into n8n / Make / Zapier, or use it from any MCP client (Claude, Cursor) via mcp.apify.com.

Compliance & methodology notes

• Output is "media mentions" attributed to their sources for analyst review - not verified allegations or a legal determination. Confirm against primary records before any decision.
• The actor is designed to minimise false positives (entity role + match confidence), but no automated screen is perfect - treat results as a triage aid.
• Coverage is open-web news and search; combine it with sanctions/PEP and registry checks (see the suite below) for full KYC/AML.

FAQ

Is it legal? Yes - it reads publicly available news and search results. Output is media mentions for analyst review, not verified allegations, and it is not a consumer report (do not use it for FCRA-governed decisions).

Do I need an API key for search or the language model? No - both are included. Just an Apify account.

How does it avoid false positives? It classifies the screened entity's role in each story (only wrongdoers are flagged) and scores an entity-match confidence to drop namesakes.

What does overallRisk: clear mean? No genuine adverse hit met your filters - a clean screen, not a failure.

What export formats are supported? JSON, CSV, Excel, or XML, or read live via the API.

Related actors

Part of a suite of official government registry actors - no public API, pay per result, no keys to supply.

Country	Actors
Poland	REGON/GUS · KRS financials · KRS board · CRBR UBO · KRZ debtors · MSiG gazette · KNF · PEP · EKW land · UOKiK · BDO waste · Premises
Germany	Handelsregister · Insolvency
Spain	Company directory · BORME acts · Concursal
Italy	Registro Imprese · PEC lookup
Austria	WKO directory · Ediktsdatei
France	Societe.com
Belgium	KBO/BCE
Czechia	ISIR insolvency
Slovakia	RPVS UBO
UAE	ADGM register
USA	California SoS · California UCC liens
Global	Adverse media (this actor)

Common combination: company register -> UBO -> insolvency/debtors -> adverse media (this actor) is the standard KYB/onboarding chain.

Full suite: apify.com/regdata · Callable from any MCP client via mcp.apify.com