Adverse Media

2. Run Adverse Media Search

cd /Users/superfunguy/wsp/scolo/backend
python -c "from src.tools import adverse_media; import json; result = adverse_media.check('ENTITY_NAME'); print(json.dumps(result, indent=2))"

3. Analyze Results

Categories of adverse media:

• Financial Crime: Fraud, money laundering, embezzlement
• Corruption: Bribery, kickbacks, political corruption
• Legal Issues: Lawsuits, regulatory violations, arrests
• Ethical Concerns: Environmental damage, labor violations
• Reputational: Scandals, controversies, negative publicity

Examples

Example: Check Company Reputation

User: "Any negative news about Wells Fargo?" Action:

python -c "from src.tools import adverse_media; import json; result = adverse_media.check('Wells Fargo'); print(json.dumps(result, indent=2))"

Important Notes

• Consider source credibility and bias
• Distinguish between allegations and confirmed facts
• Check publication dates for relevance
• Multiple sources strengthen findings38:["$","$L40",null,{"content":"$41","frontMatter":{"name":"adverse-media","description":"Search for negative news coverage, controversies, and reputational risks associated with individuals or companies across news sources and media databases","allowed-tools":["Bash","Read","Write","WebSearch"]}}]