# Adverse Media Watch: Negative News Screening for KYC (`tudorlane/adverse-media-watch`) Actor

Daily adverse media screening for people and companies: global news (GDELT) plus major publisher and regulator feeds, scored for fraud, sanctions, crime, lawsuits, insolvency and ESG risk. Exact-name matching, deduped, only new hits. Pairs with sanctions-watchlist.

- **URL**: https://apify.com/tudorlane/adverse-media-watch.md
- **Developed by:** [Tudor Lane](https://apify.com/tudorlane) (community)
- **Categories:** Business, News
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.00 / 1,000 name checkeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Adverse Media Watch: Negative News Screening for KYC

### Use when…

- You need negative-news / adverse-media screening for a person or company (KYC).
- A compliance agent must search recent headlines and score risk from news hits.
- You want a watchlist of subjects with timespan and minimum risk filters.

### Try this input

Copy-paste this JSON as the Actor input (it matches a successful run / the default schema):

```json
{
  "subjects": [
    {
      "name": "Boeing",
      "type": "company"
    },
    {
      "name": "Sam Bankman-Fried",
      "type": "person"
    }
  ],
  "timespan": "3d",
  "minRiskScore": 20,
  "useGdelt": true,
  "useRssFeeds": true,
  "watchlistId": "default"
}
```

### Sample output (real run 2026-10-25)

One dataset item, shortened:

```json
{
  "key": "company:Boeing",
  "baseline": false,
  "changed": false,
  "change_count": 0,
  "changes": [],
  "change_types": [],
  "subject": "Boeing",
  "type": "company",
  "hits_in_window": 0,
  "max_risk_score": 0,
  "hits": [],
  "gdelt_available": false,
  "…": "+3 more keys"
}
```

**What it does:** Daily **adverse media / negative news** screening for people and companies. It searches global news (GDELT, 100+ languages of sources) plus major publisher and regulator feeds (BBC News, CNBC, Sky, NPR, US DOJ, SEC and more), scores each hit for risk and returns **only new hits since the last run**.

**Who it's for:** KYC/AML and onboarding teams, compliance and fintech agents, supplier and third-party risk, investors doing ongoing due diligence.

**Price:** Pay per event: **$0.003 per name checked** + **$0.01 per new adverse media hit**. Baseline runs are never charged for hits. No monthly fee.

### Compliance kit

Use it with **[sanctions-watchlist](https://apify.com/tudorlane/sanctions-watchlist)** (new OFAC/UK/EU/UN matches) and **[sanctions-screening](https://apify.com/tudorlane/sanctions-screening)** (one-off checks) for sanctions plus adverse media monitoring, the two ongoing checks most AML programmes need.

### Fewer false matches

- **Exact phrase matching** on the name or aliases (word boundaries; company suffixes like Ltd/PLC/Inc are handled). Short names are matched case-sensitively.
- **`context`** terms must also appear (e.g. a person's employer or town). **`exclude`** terms rule a hit out (e.g. a footballer namesake).
- **Risk scoring 0-100** across categories: financial crime (fraud, bribery, laundering) 40, sanctions/terror 40, criminal (arrest, charged, convicted) 30, legal/regulatory (lawsuit, fined, probe) 20, insolvency 20, ESG/safety 15. `minRiskScore` (default 20) filters noise.
- **Deduped** across outlets (syndicated copies are listed in `also_reported_by`).

### How it works

1. **First run = baseline.** Everything currently found is saved to your named key-value store `adverse-media-watch` under your `watchlistId` (also as the run's `SNAPSHOT` record). You see the current results in each row, but no `new-hit` charges.
2. **Every later run** reports only what is new since the last run (`changed`, `changes`, `change_types`).
3. If a source is down, that row gets an `error` and the old snapshot is kept, so nothing is missed or double-counted.

### Quick example

**Input**

```json
{"subjects": [{"name": "Acme Widgets Ltd", "type": "company"}, {"name": "John Smith", "type": "person", "context": ["Acme"], "exclude": ["football"]}], "timespan": "3d", "watchlistId": "kyc-book"}
```

**Change text (later run)**

```
[risk 70: criminal, financial_crime] Acme Widgets director charged with fraud (bbc.co.uk, 2026-10-22) https://...
```

### Use it from AI agents (MCP) and code

- **Apify MCP server:** add `https://mcp.apify.com/?tools=tudorlane/adverse-media-watch` to Claude, Cursor, ChatGPT connectors, VS Code or any MCP client. The Actor appears as a tool the model can call.
- **REST (sync):** `POST https://api.apify.com/v2/acts/tudorlane~adverse-media-watch/run-sync-get-dataset-items?token=YOUR_TOKEN` with the input JSON as the body.
- **Schedule it:** Apify Console > Schedules > daily, with the same `watchlistId`. Pipe new items to Slack, email, a webhook, n8n, Make or Zapier.
- **Stateless agents:** pass the previous run's `SNAPSHOT` record as `previousSnapshot` instead of relying on the saved watchlist.

### Sample output (real run with the default input, 25 Oct 2026)

| Subject | Type | Negative hits in window | Max risk score | News search available |
|---|---|---|---|---|
| Boeing | company | 0 | 0 | yes |
| Sam Bankman-Fried | person | 0 | 0 | yes |

On this run neither name had new negative news in the time window; when there are hits, each row lists the articles, risk score and why it matched.

### Use cases

1. KYC/AML: negative news on customers
2. Supplier and partner due diligence
3. Ongoing monitoring of named people or companies
4. Compliance agents

### Limits & failure modes

- News search, not a regulated screening database
- Matches need human review; common names can give false hits
- Coverage limited to the feeds/sources it searches
- Risk scores are heuristic from news text — not a court finding or official adverse-media database.
- Common names produce noise; tighten timespan, add identifiers, or raise `minRiskScore`.
- GDELT/RSS gaps mean absence of hits is not proof of a clean record.

### FAQ

**Is a hit a finding?** No. It's a lead for a human or agent to review. Headlines and links only; full articles belong to the publishers.

**Coverage?** GDELT covers worldwide online news; publisher feeds add reliable UK/US business and regulator news. GDELT is rate-limited, so about 8 names are searched per request; if it's unavailable, the row shows `gdelt_available: false` and RSS results still come back.

**Support?** Open an issue on the Actor's Issues tab.

***

Keywords: adverse media screening API, negative news screening, KYC adverse media, AML monitoring, reputational risk, media monitoring API, PEP and sanctions companion, compliance automation, MCP tool, Apify Actor, AI agent tool.

**Search terms:** news scraper, adverse media screening, negative news search, KYC news monitoring, AML media check

# Actor input Schema

## `subjects` (type: `array`):

Max 200. A name, or {name, type: person|company, aliases: \[..], context: \[..] (must also appear), exclude: \[..], minRiskScore}.

## `timespan` (type: `string`):

How far back GDELT is searched each run.

## `minRiskScore` (type: `integer`):

0-100; 20 = at least one legal/regulatory term, 40 = fraud, sanctions or two categories.

## `excludeTerms` (type: `array`):

Global terms that rule out a match (e.g. a namesake's sport or town).

## `useGdelt` (type: `boolean`):

GDELT DOC 2.0 global news index; adds about 6s per 8 names (fair-use rate limit).

## `useRssFeeds` (type: `boolean`):

BBC News, CNBC, Sky, NPR, US DOJ, SEC and more.

## `extraFeeds` (type: `array`):

Up to 20 more public RSS/Atom feed URLs.

## `watchlistId` (type: `string`):

Name for this watchlist; the snapshot is kept in your named key-value store under this ID so each run compares with the last.

## `previousSnapshot` (type: `object`):

Optional: pass the SNAPSHOT record from an earlier run instead of the saved watchlist (stateless agents).

## Actor input object example

```json
{
  "subjects": [
    {
      "name": "Boeing",
      "type": "company"
    },
    {
      "name": "Sam Bankman-Fried",
      "type": "person"
    }
  ],
  "timespan": "3d",
  "minRiskScore": 20,
  "useGdelt": true,
  "useRssFeeds": true,
  "watchlistId": "default"
}
```

# Actor output Schema

## `results` (type: `string`):

One item per request.

## `dataset` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "subjects": [
        {
            "name": "Boeing",
            "type": "company"
        },
        {
            "name": "Sam Bankman-Fried",
            "type": "person"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("tudorlane/adverse-media-watch").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "subjects": [
        {
            "name": "Boeing",
            "type": "company",
        },
        {
            "name": "Sam Bankman-Fried",
            "type": "person",
        },
    ] }

# Run the Actor and wait for it to finish
run = client.actor("tudorlane/adverse-media-watch").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "subjects": [
    {
      "name": "Boeing",
      "type": "company"
    },
    {
      "name": "Sam Bankman-Fried",
      "type": "person"
    }
  ]
}' |
apify call tudorlane/adverse-media-watch --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,tudorlane/adverse-media-watch"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ubdyjLGEB0h8hwG1K/builds/CfKT02KNUKPubgSvj/openapi.json