Adverse Media Screening Check

The adverse media screening check analyzes news articles, media coverage, and other public sources to identify potential risks or concerns associated with a business. The check searches multiple data sources including Refinitiv World Check, ComplyAdvantage, and Google News to build comprehensive adverse media profiles.

Check ID

kyb.adverse_media_screening_check_v2

Response Structure

The check result contains a list of verified adverse media hits, where each hit represents a business profile found in adverse media sources along with detailed article information and metadata.

{
  type: "KYBAdverseMediaScreeningCheckResultV2";
  verified_adverse_media_hits: Array<BusinessAdverseMediaProfile>;
}

BusinessAdverseMediaProfile Structure

Each adverse media profile contains comprehensive information about a business found in adverse media sources:

{
  // Identification
  id: string;                    // Short unique identifier (8 characters)
  reference_id: string | null;   // External reference ID from data source
  business_name: string | null;  // Name of the business in the profile

  // Geographic Information
  associated_countries: Array<StandardizedCountry>;
  associated_addresses: Array<Address>;
  location: string | null;       // e.g., "Houston, TX"

  // Weblinks and Articles
  weblinks: Array<Weblink>;      // Detailed article references with metadata

  // Classification
  is_perpetrator: boolean | null;  // Whether business is perpetrator of the event
  topics: Array<string>;           // Event topics (e.g., "Regulatory Violations", "Legal Disputes")
  title: string | null;            // Brief title summarizing the event
  summary: string | null;          // Summary of the adverse media event
  when: string | null;             // When the event occurred

  // Matching and Review
  profile_review: BusinessProfileReview;  // Match analysis and ratings
  escalate_for_review: boolean;           // Whether to escalate for manual review
  vendor: string | null;                  // Data source vendor (e.g., "refinitiv_world_check")
}

Weblink Structure

Each weblink represents a specific article or source with full metadata:

{
  // Identification
  id: string;                    // Unique ID (6 characters)
  url: string | null;            // Article URL
  title: string | null;          // Article title

  // Temporal and Geographic Data
  date: string | null;           // Date from the weblink
  when: string | null;           // When the event occurred (e.g., "2025")
  where_countries: Array<string>; // Countries mentioned in article
  where_cities: Array<string>;    // Cities mentioned in article

  // Content
  summary: string | null;        // Summary of the article
  scanned_website: ScannedWebsite;  // Full scraped webpage content
  metadata: BusinessArticleMetadataV1;  // Structured article metadata

  // Source Information
  article_source: ArticleSource;  // Source type (e.g., "serp_google_search", "refinitiv_world_check")

  // Status
  has_photo: boolean | null;
  is_dead_url: boolean | null;
}

BusinessArticleMetadataV1 Structure

Detailed metadata extracted from each article using LLM analysis:

{
  type: "BusinessArticleMetadataV1";
  id: string;

  // Article Classification
  adverse_media_article_title: string | null;
  is_adverse_media_article: boolean;    // Whether this is adverse media
  topics: Array<string> | null;         // Topics like "Regulatory Violations", "Legal Disputes"

  // Business Involvement
  is_perpetrator: boolean;               // Whether business is the perpetrator
  is_hit_business_name_found: boolean;  // Whether business name was found in article
  hit_business_name_value_found: string | null;  // Exact business name found

  // Geographic Context
  associated_addresses: Array<Address>;
  associated_countries: Array<string>;

  // Temporal Context
  year_of_the_crime: number | null;           // Year the event occurred
  year_of_article_publication: number | null; // Year article was published

  // Event Details
  summary_of_event: string | null;            // Summary of the main event
  summary_of_relation_to_crime: string | null;  // How business relates to the event
  quote_from_article: string | null;          // Direct quote from the article
  source_url: string | null;                  // Article URL
}

ScannedWebsite Structure

Raw scraped content from the webpage:

{
  type: "ScannedWebsite";
  source_id: string;                 // Unique source ID (6 characters)
  source_name: string | null;        // Name of the source
  webpage_url: string | null;        // URL of the webpage
  webpage_title: string | null;      // Page title
  webpage_text: string | null;       // Full text content
  screenshot_url: string | null;     // Screenshot URL
  pdf_snapshot_url: string | null;   // PDF snapshot URL
  response_code: number;             // HTTP response code
  is_valid_url: boolean | null;      // Whether URL is accessible
  language: string | null;           // Language of the content
  publication_date: string | null;   // Publication date
  scrape_type: ScrapeType;           // Type of scrape ("generic", etc.)
  error: string | null;              // Error message if scraping failed
  cloudflare_encountered: boolean;   // Whether Cloudflare protection was encountered
}

BusinessProfileReview Structure

Match analysis and confidence scoring:

{
  id: string;                                  // Review ID (8 characters)
  business_name_match: BusinessNameMatch | null;  // Business name match result
  address_matches: Array<AddressMatch> | null;     // Address match results
  best_address_match: AddressMatch | null;         // Best matching address
  match_rating: HitMatch | null;                   // Overall match confidence
}

Match Rating System

The match_rating field uses the following enum values:

strong_match: High confidence that the adverse media refers to the screened business
- Business name matches closely
- Location matches
- Other identifying details align
partial_match: Moderate confidence in the match
- Business name is similar but not exact
- Some geographic or contextual alignment
- May require manual review
weak_match: Low confidence in the match
- Name similarity is limited
- Location may not match
- Likely a different business with a similar name
no_match: Clear mismatch
- Business name is significantly different
- Location doesn’t match
- Context clearly indicates a different entity
unknown: Unable to determine match confidence
- Insufficient information available
- Ambiguous details

Example Response

{
  "type": "KYBAdverseMediaScreeningCheckResultV2",
  "verified_adverse_media_hits": [
    {
      "id": "tm2m3kdz",
      "reference_id": null,
      "business_name": "Empower Pharmacy",
      "associated_countries": [
        {
          "original_country_input": "United States",
          "country_name": "United States of America",
          "alpha_2_country_code": "US",
          "alpha_3_country_code": "USA",
          "numeric_country_code": "840"
        }
      ],
      "associated_addresses": [
        {
          "type": "Address",
          "street_1": null,
          "street_2": null,
          "city": "Houston",
          "state": null,
          "country_code": "US",
          "postal_code": null
        }
      ],
      "weblinks": [
        {
          "id": "ijbjo6cc",
          "url": "https://lawgaze.com/empower-pharmacy-lawsuit/",
          "date": null,
          "has_photo": null,
          "is_dead_url": null,
          "summary": "Empower Pharmacy is the defendant in regulatory disputes with the FDA over alleged sterility concerns, labeling violations, and compliance failures.",
          "when": "2025",
          "where_countries": ["United States"],
          "where_cities": ["Houston"],
          "scanned_website": {
            "type": "ScannedWebsite",
            "source_id": "nbxery",
            "webpage_url": null,
            "webpage_title": "Empower Pharmacy Lawsuit: FDA Disputes and Legal Battles",
            "response_code": 200,
            "scrape_type": "generic",
            "cloudflare_encountered": false,
            "error": null
          },
          "metadata": {
            "type": "BusinessArticleMetadataV1",
            "id": "ijbjo6cc",
            "topics": ["Regulatory Violations", "Legal Disputes", "Compliance Issues"],
            "adverse_media_article_title": "Empower Pharmacy Lawsuit: FDA Disputes and Legal Battles",
            "is_adverse_media_article": true,
            "is_perpetrator": false,
            "is_hit_business_name_found": true,
            "hit_business_name_value_found": "Empower Pharmacy",
            "associated_addresses": [
              {
                "type": "Address",
                "street_1": null,
                "street_2": null,
                "city": "Houston",
                "state": "Texas",
                "country_code": "US",
                "postal_code": null
              }
            ],
            "associated_countries": ["United States"],
            "year_of_the_crime": 2025,
            "year_of_article_publication": 2025,
            "summary_of_relation_to_crime": "Empower Pharmacy is the defendant in regulatory disputes with the FDA over alleged sterility concerns, labeling violations, and compliance failures.",
            "summary_of_event": "The FDA has alleged that Empower Pharmacy, one of the largest compounding pharmacies in the US, has violated regulations regarding sterility standards, labeling compliance, and drug manufacturing practices.",
            "source_url": "https://lawgaze.com/empower-pharmacy-lawsuit/",
            "quote_from_article": "Empower Pharmacy, one of the largest compounding pharmacies in the United States, has found itself at the center of a legal storm."
          },
          "title": "Empower Pharmacy Lawsuit: FDA Disputes and Legal Battles",
          "article_source": "serp_google_search"
        }
      ],
      "is_perpetrator": null,
      "topics": [],
      "title": null,
      "summary": null,
      "when": null,
      "profile_review": {
        "id": "abc123de",
        "business_name_match": null,
        "address_matches": null,
        "best_address_match": null,
        "match_rating": {
          "match": "partial_match",
          "reason": "Business name matches, location matches, but needs manual review"
        }
      },
      "escalate_for_review": true,
      "vendor": null
    }
  ]
}

Response Fields

type

string

Always "KYBAdverseMediaScreeningCheckResultV2".

verified_adverse_media_hits

array

List of verified adverse media profiles for the business. Each profile represents a business found in adverse media sources.

Show BusinessAdverseMediaProfile Fields

string

Short unique identifier (8 characters) for this profile.

reference_id

string | null

External reference ID from the data source (e.g., Refinitiv World Check ID).

business_name

string | null

Name of the business in the adverse media profile.

associated_countries

array

Countries associated with the business in the adverse media.

country_name

string

Full country name (e.g., “United States of America”).

alpha_2_country_code

string

ISO 3166-1 alpha-2 code (e.g., “US”).

alpha_3_country_code

string

ISO 3166-1 alpha-3 code (e.g., “USA”).

associated_addresses

array

Addresses associated with the business in the adverse media.

city

string | null

City name.

state

string | null

State or province.

country_code

string | null

ISO 3166-1 alpha-2 country code.

weblinks

array

Array of article references with detailed metadata. See Weblink Structure above.

url

string | null

URL of the article or source.

title

string | null

Title of the article.

summary

string | null

Summary of the article content.

when

string | null

When the event occurred (e.g., “2025”, “2024-2025”).

where_countries

array

Countries mentioned in the article.

where_cities

array

Cities mentioned in the article.

metadata

object

Structured metadata extracted from the article using LLM analysis.

topics

array

Event topics (e.g., “Regulatory Violations”, “Legal Disputes”, “Compliance Issues”).

is_adverse_media_article

boolean

Whether this is classified as adverse media.

is_perpetrator

boolean

Whether the business is the perpetrator of the event described.

year_of_the_crime

number | null

Year when the event occurred.

year_of_article_publication

number | null

Year when the article was published.

summary_of_event

string | null

Summary of the main event described in the article.

summary_of_relation_to_crime

string | null

Summary of how the business relates to the event.

quote_from_article

string | null

Direct quote from the article about the business.

article_source

string

Source of the article. Possible values:

serp_google_search - Google Search results
serp_google_news - Google News
refinitiv_world_check - Refinitiv World-Check
comply_advantage - ComplyAdvantage
Other source identifiers

is_perpetrator

boolean | null

Whether the business is the perpetrator of the adverse event.

profile_review

object

Match analysis and confidence scoring for this profile.

match_rating

object | null

Overall match confidence rating.

match

string

Match strength: strong_match, partial_match, weak_match, no_match, or unknown.

reason

string | null

Explanation for the match rating.

escalate_for_review

boolean

Whether this profile should be escalated for manual review based on match ratings and risk thresholds.

Article Sources

The check aggregates adverse media from multiple sources:

Primary Data Sources

Refinitiv World-Check (refinitiv_world_check)
- Global risk intelligence database
- PEPs, sanctions, and adverse media
ComplyAdvantage (comply_advantage)
- Real-time risk database
- Comprehensive adverse media coverage

Search Engine Sources

Google Search (serp_google_search)
- Web search results for adverse media
- Broad coverage of online content
Google News (serp_google_news)
- News-specific search results
- Recent and archived news articles
Brave Search (serp_brave_search, serp_brave_news)
- Privacy-focused search engine results

Other Sources

Opoint (opoint)
- Specialized adverse media intelligence
Other (other)
- Miscellaneous or unclassified sources

Key Components

Article Metadata Extraction

Each article undergoes LLM-powered analysis to extract:

Event Classification: Topics and categories (regulatory, legal, financial, etc.)
Business Involvement: Whether business is perpetrator, victim, or mentioned
Geographic Context: Countries and cities mentioned
Temporal Context: When the event occurred and when it was published
Relationship Analysis: How the business relates to the adverse event
Evidence Extraction: Direct quotes and summaries

Match Confidence Scoring

The system evaluates multiple factors to determine match confidence:

Business name similarity and exact matches
Geographic alignment (addresses, cities, countries)
Contextual relevance
Article quality and recency
Source reliability

Escalation Logic

Profiles are escalated for manual review based on:

Match Rating: Strong and partial matches typically escalated
Event Severity: Regulatory violations, criminal activity, major fraud
Recency: Recent events (within last 2-5 years)
Volume: Multiple articles about the same event
Source Quality: Articles from reputable sources

Common Topics

Articles are categorized into topics such as:

Regulatory Violations: FDA warnings, compliance failures, regulatory actions
Legal Disputes: Lawsuits, legal battles, civil litigation
Compliance Issues: Failure to meet standards, policy violations
Safety Issues: Product safety, public health concerns
Financial Misconduct: Fraud, embezzlement, financial crimes
Criminal Activity: Criminal charges, investigations
Operational Problems: Business failures, bankruptcy
Reputational Concerns: Scandals, negative publicity

Implementation Details

Pydantic Schema Location

Main Schema: ai/data_loaders/schema/kyb_schema.py
Base Classes: ai/data_loaders/schema/base.py
Models: ai/tools/bdd/bdd_models.py

Data Loader

ai/data_loaders/kyb_adverse_media_profile_loader_v2.py

Tool Implementation

ai/tools/kyb/kyb_adverse_media_screening_check_v2.py

Filtering Examples

Use JSONPath queries to extract specific fields from the check results:

# Get only articles from Google Search
?jsonpath_query=$.check_results[?(@.command_id=='kyb.adverse_media_screening_check_v2')].payload.verified_adverse_media_hits[*].weblinks[?(@.article_source=='serp_google_search')]

# Get only articles where business is the perpetrator
?jsonpath_query=$.check_results[?(@.command_id=='kyb.adverse_media_screening_check_v2')].payload.verified_adverse_media_hits[*].weblinks[?(@.metadata.is_perpetrator==true)]

# Extract all article titles
?jsonpath_query=$.check_results[?(@.command_id=='kyb.adverse_media_screening_check_v2')].payload.verified_adverse_media_hits[*].weblinks[*].title

# Get articles about regulatory violations
?jsonpath_query=$.check_results[?(@.command_id=='kyb.adverse_media_screening_check_v2')].payload.verified_adverse_media_hits[*].weblinks[?(@.metadata.topics[*]=='Regulatory Violations')]

See getJobById filtering documentation for more JSONPath query examples.

Getting Started

Checks Results

API Reference

Check ID

Response Structure

BusinessAdverseMediaProfile Structure

Weblink Structure

BusinessArticleMetadataV1 Structure

ScannedWebsite Structure

BusinessProfileReview Structure

Match Rating System

Example Response

Response Fields

Article Sources

Primary Data Sources

Search Engine Sources

Other Sources

Key Components

Article Metadata Extraction

Match Confidence Scoring

Escalation Logic

Common Topics

Implementation Details

Pydantic Schema Location

Data Loader

Tool Implementation

Filtering Examples

Getting Started

Checks Results

API Reference

​Check ID

​Response Structure

​BusinessAdverseMediaProfile Structure

​Weblink Structure

​BusinessArticleMetadataV1 Structure

​ScannedWebsite Structure

​BusinessProfileReview Structure

​Match Rating System

​Example Response

​Response Fields

​Article Sources

​Primary Data Sources

​Search Engine Sources

​Other Sources

​Key Components

​Article Metadata Extraction

​Match Confidence Scoring

​Escalation Logic

​Common Topics

​Implementation Details

​Pydantic Schema Location

​Data Loader

​Tool Implementation

​Filtering Examples

Check ID

Response Structure

BusinessAdverseMediaProfile Structure

Weblink Structure

BusinessArticleMetadataV1 Structure

ScannedWebsite Structure

BusinessProfileReview Structure

Match Rating System

Example Response

Response Fields

Article Sources

Primary Data Sources

Search Engine Sources

Other Sources

Key Components

Article Metadata Extraction

Match Confidence Scoring

Escalation Logic

Common Topics

Implementation Details

Pydantic Schema Location

Data Loader

Tool Implementation

Filtering Examples