# Adverse Media Screener - KYC/AML Negative News Check (`regdata/adverse-media-screener`) Actor Screen a person or company for adverse media (negative news) for KYC/AML and due diligence. Returns categorized, LLM-classified hits - fraud, corruption, sanctions, money laundering and more - with the entity's role, a severity score and source provenance. False positives filtered out. - **URL**: https://apify.com/regdata/adverse-media-screener.md - **Developed by:** [getregdata](https://apify.com/regdata) (community) - **Categories:** Automation, Agents, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $80.00 / 1,000 entity screeneds This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 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**. 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 (false positives) A naive keyword search for `"Acme Corp" lawsuit` flags Acme even when Acme is the one **suing** someone. This actor uses an LLM to read each hit and decide: - **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. ### Input ```json { "entityNames": ["Wirecard AG", "Jan Marsalek"], "entityType": "auto", "country": "de", "minSeverity": "low", "maxHits": 25 } ```` | 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) | ### Output (one record per entity) ```json { "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** - Google News plus a web search with adverse-term expansion (fraud, investigation, sanctions, corruption, money laundering, ...), via Serper. Aliases and country bias the search. 2. **De-duplication** - candidate hits are de-duplicated by URL. 3. **LLM 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 LLM are **included** - no API keys required. ### Use Cases - **KYC / KYB onboarding** - the adverse-media step alongside sanctions/PEP checks - **Enhanced due diligence (EDD)** - investigate high-risk counterparties and UBOs - **Ongoing monitoring** - re-screen your book periodically for new negative news - **Investigations & journalism** - map allegations against a person or company ### 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 with sanctions/PEP and registry checks (see the suite below) for full KYC/AML. ### Related Actors (regdata KYC/AML suite) - [Poland Parliamentary PEP Scraper](https://apify.com/regdata/poland-parliamentary-pep-scraper) - PEP screening dataset - [Poland CRBR Beneficial Owners](https://apify.com/regdata/crbr-beneficial-owners-scraper) - UBO/beneficial owners - [Germany Handelsregister Scraper](https://apify.com/regdata/germany-handelsregister-scraper) - company data + officers - Full suite: [apify.com/regdata](https://apify.com/regdata) # Actor input Schema ## `entityNames` (type: `array`): One or more person or company names to screen, e.g. \["Wirecard AG", "Jan Marsalek"]. Each entity is billed and returned as its own result. ## `entityType` (type: `string`): Whether the entities are people or companies (improves disambiguation). Leave on "auto" if mixed. ## `aliases` (type: `array`): Alternative names / transliterations for the entity (only meaningful when screening a single entity). ## `country` (type: `string`): ISO country code or name to bias the search and aid disambiguation, e.g. "de", "Germany". ## `categories` (type: `array`): If set, only return hits in these risk categories. Leave empty to return all. ## `minSeverity` (type: `string`): Only return hits at or above this severity. ## `maxHits` (type: `integer`): Maximum number of adverse hits to return per entity. 1-50. ## `model` (type: `string`): OpenRouter model id used for classification. Default is a cheap, accurate model. ## `serperApiKey` (type: `string`): Optional. Search is included; provide your own Serper key only to bill search to your own account. ## `openRouterApiKey` (type: `string`): Optional. The LLM is included; provide your own OpenRouter key only to bill classification to your own account. ## Actor input object example ```json { "entityNames": [ "Wirecard AG" ], "entityType": "auto", "minSeverity": "low", "maxHits": 25, "model": "deepseek/deepseek-chat" } ``` # Actor output Schema ## `results` (type: `string`): One record per entity - overall risk, adverse-hit count, categories found, and the classified hits (title, url, source, date, snippet, risk category, severity, entity role, match confidence, reason). # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "entityNames": [ "Wirecard AG" ] }; // Run the Actor and wait for it to finish const run = await client.actor("regdata/adverse-media-screener").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "entityNames": ["Wirecard AG"] } # Run the Actor and wait for it to finish run = client.actor("regdata/adverse-media-screener").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "entityNames": [ "Wirecard AG" ] }' | apify call regdata/adverse-media-screener --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=regdata/adverse-media-screener", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Adverse Media Screener - KYC/AML Negative News Check", "description": "Screen a person or company for adverse media (negative news) for KYC/AML and due diligence. Returns categorized, LLM-classified hits - fraud, corruption, sanctions, money laundering and more - with the entity's role, a severity score and source provenance. False positives filtered out.", "version": "1.0", "x-build-id": "fHgIBdU0pbfh7pWZa" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/regdata~adverse-media-screener/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-regdata-adverse-media-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/regdata~adverse-media-screener/runs": { "post": { "operationId": "runs-sync-regdata-adverse-media-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/regdata~adverse-media-screener/run-sync": { "post": { "operationId": "run-sync-regdata-adverse-media-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "entityNames": { "title": "Entities to screen", "type": "array", "description": "One or more person or company names to screen, e.g. [\"Wirecard AG\", \"Jan Marsalek\"]. Each entity is billed and returned as its own result.", "items": { "type": "string" } }, "entityType": { "title": "Entity type", "enum": [ "auto", "person", "company" ], "type": "string", "description": "Whether the entities are people or companies (improves disambiguation). Leave on \"auto\" if mixed.", "default": "auto" }, "aliases": { "title": "Aliases (optional)", "type": "array", "description": "Alternative names / transliterations for the entity (only meaningful when screening a single entity).", "items": { "type": "string" } }, "country": { "title": "Country (optional)", "type": "string", "description": "ISO country code or name to bias the search and aid disambiguation, e.g. \"de\", \"Germany\"." }, "categories": { "title": "Risk categories filter (optional)", "type": "array", "description": "If set, only return hits in these risk categories. Leave empty to return all.", "items": { "type": "string", "enum": [ "financial_crime", "fraud", "corruption_bribery", "sanctions", "money_laundering", "terrorism", "organized_crime", "regulatory_enforcement", "litigation", "environmental", "other" ] } }, "minSeverity": { "title": "Minimum severity", "enum": [ "low", "medium", "high" ], "type": "string", "description": "Only return hits at or above this severity.", "default": "low" }, "maxHits": { "title": "Max hits per entity", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum number of adverse hits to return per entity. 1-50.", "default": 25 }, "model": { "title": "Classification model (advanced)", "type": "string", "description": "OpenRouter model id used for classification. Default is a cheap, accurate model.", "default": "deepseek/deepseek-chat" }, "serperApiKey": { "title": "Serper API Key (optional override)", "type": "string", "description": "Optional. Search is included; provide your own Serper key only to bill search to your own account." }, "openRouterApiKey": { "title": "OpenRouter API Key (optional override)", "type": "string", "description": "Optional. The LLM is included; provide your own OpenRouter key only to bill classification to your own account." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```