Adverse Media Screening Check

The Adverse Media Screening check helps identify potential risks by analyzing news articles, media coverage, and customer reviews related to a business. This guide explains how to run the check, what data to provide, and how to interpret the results.

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Overview

The check searches through various news sources and review platforms to identify:

• Negative news coverage
• Regulatory investigations
• Legal proceedings
• Customer complaints
• Reputational issues

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Running the Check

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API Endpoint

POST https://api.parcha.ai/runCheck

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Request Parameters

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Example Request

curl -X POST 'https://api.parcha.ai/runCheck' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-H 'Content-Type: application/json' \
-d '{
  "agent_key": "public-bdd",
  "check_id": "kyb.adverse_media_check",
  "payload": {
    "id": "adverse-media-check-001",
    "self_attested_data": {
      "business_name": "Acme Corp",
      "address_of_incorporation": {
        "country_code": "US"
      },
      "address_of_operation": {
        "country_code": "US"
      },
      "incorporation_date": "2020-01-15",
      "customer_countries": ["US", "CA", "GB"]
    }
  }
}'

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Initial Response

{
  "id": "check-abc123",
  "status": "PENDING",
  "created_at": "2024-02-15T10:30:00Z",
  "updated_at": "2024-02-15T10:30:00Z",
  "agent_id": "public-bdd",
  "input_payload": {
    // Original request payload
  }
}

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Check Process

The adverse media screening check follows these steps:

1. Data Collection

   • Searches news archives and media databases
   • Analyzes customer review platforms
   • Gathers information from business registries

2. Match Analysis

   • Compares business names and aliases
   • Validates geographic relevance
   • Checks temporal relevance (based on incorporation date)
   • Evaluates source credibility

3. Risk Assessment

   • Categorizes findings by severity
   • Analyzes impact on business operations
   • Evaluates reputational risk

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Check Results

Once the check is complete, you can retrieve the results using the check ID. The results will include:

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Response Structure

{
  type: "KYBAdverseMediaScreeningCheckResult";
  verified_adverse_media_hits: Array<AnnotatedNewsArticle> | null;
  verified_adverse_review_hits: Array<AnnotatedCustomerReview> | null;
  passed: boolean;
}

type AnnotatedNewsArticle = {
  article_id: string | null;
  source_url: string | null;
  source_name: string | null;
  title: string | null;
  text_content: string | null;
  publication_date: string | null;
  article_type: string | null;
  article_metadata: {
    who: {
      perpetrators: Array<{
        business_name: string | null;
      }> | null;
    } | null;
    what: string | null;
  } | null;
}

type AnnotatedCustomerReview = {
  review_id: string | null;
  source_url: string | null;
  source_name: string | null;
  title: string | null;
  text_content: string | null;
  publication_date: string | null;
  rating: number | null;
  review_metadata: {
    sentiment: string | null;
    topics: Array<string> | null;
  } | null;
}

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Example Result

{
  "type": "KYBAdverseMediaScreeningCheckResult",
  "verified_adverse_media_hits": [
    {
      "article_id": "news123",
      "source_url": "https://example.com/article1",
      "source_name": "Business News Daily",
      "title": "Company XYZ Faces Regulatory Investigation",
      "text_content": "Company XYZ is under investigation for potential compliance violations...",
      "publication_date": "2024-01-15",
      "article_type": "News",
      "article_metadata": {
        "who": {
          "perpetrators": [
            {
              "business_name": "Company XYZ"
            }
          ]
        },
        "what": "Regulatory compliance investigation regarding financial reporting practices"
      }
    }
  ],
  "verified_adverse_review_hits": [
    {
      "review_id": "review456",
      "source_url": "https://reviews.example.com/xyz",
      "source_name": "Business Reviews",
      "title": "Poor Customer Service Experience",
      "text_content": "Extremely dissatisfied with the service quality and response time...",
      "publication_date": "2024-02-01",
      "rating": 1,
      "review_metadata": {
        "sentiment": "negative",
        "topics": ["customer service", "response time", "quality"]
      }
    }
  ],
  "passed": false
}

​

Risk Categories

The check evaluates findings across different risk levels:

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High Risk

• Criminal activities
• Regulatory violations
• Major legal issues
• Severe financial problems
• Significant fraud allegations

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Medium Risk

• Customer complaints
• Service issues
• Minor legal disputes
• Operational problems
• Negative reviews

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Low Risk

• Isolated incidents
• Minor complaints
• Resolved issues
• Historical events
• Non-material concerns

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Best Practices

1. Provide Accurate Business Information

   • Use the legal business name
   • Include all known operating countries
   • Specify accurate incorporation date

2. Consider Time Frames

   • Recent events (within 1 year) are most relevant
   • Historical events may require context
   • Consider the business’s age when evaluating findings

3. Evaluate Results Holistically

   • Consider the credibility of sources
   • Look for patterns in findings
   • Assess the severity and frequency of issues

4. Follow Up Actions

   • Document significant findings
   • Investigate serious allegations
   • Consider additional due diligence if needed

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Error Handling

Common errors and their solutions:

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Limitations

• The check only covers publicly available information
• Results may vary based on the availability of data in different jurisdictions
• Some sources may have delayed reporting of incidents
• Coverage may be limited in certain geographic regions

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Support

If you encounter any issues or need assistance:

• Email: support@parcha.ai
• Documentation: https://docs.parcha.ai
• API Status: https://status.parcha.ai