Litigation risk often surfaces before financial distress — from unpaid bills to regulatory disputes. Yet legal exposure is scattered across registries and press, making it invisible in CRM or supply chain workflows.
Consultants, SaaS vendors, and strategic foresight teams need a structured, explainable view of disputes to act early. This segment standardizes legal filings, enriches with litigation news, classifies case typologies, and scores exposure by severity, recency, and counterparties.
Normalize filings and case registries into structured variables.
Enrich with litigation press mentions and sentiment.
Score exposure by severity, recency, typology, and counterparty risk.
This segment is activated with a blend of trusted sources and your own inputs
AI reasoning
User Input
Curated APIs
Web intelligence
Legal filings: counts, dates, dispute types, counterparties.
Press signals: volume and sentiment of litigation coverage.
Exposure scoring: composite index combining severity, recency, and escalation risk.
Roll-ups: sector and country heatmaps to size and prioritize risk.
Your questions on this segment, answered
Your questions on this segment, answered
How can consulting or foresight teams use this dataset?
By quantifying litigation exposure at sector or country level, they can identify systemic risks, benchmark industries, and support foresight models with hard legal evidence.
How fresh and reliable is the litigation data?
Filings and press coverage are continuously updated. Each field carries a confidence score based on source quality and recency, making the dataset auditable.
Can this be used in supplier and M&A due diligence?
Yes. Early visibility on disputes helps assess counterparties and acquisition targets, mitigating reputational and financial risks before they escalate.
What’s the added value compared to raw court filings?
Instead of scattered filings, you get standardized variables, exposure scoring, and aggregated heatmaps by sector and country, ready to plug into BI or CRM workflows.
How is the legal exposure score calculated?
It combines the number of filings, severity, recency, and litigation press signals into a single 0–100 index.
What kinds of disputes are included in this radar?
We capture and classify commercial, employment, regulatory, and intellectual property disputes, mapped to counterparties such as suppliers, customers, employees, governments, or competitors.
Why does litigation matter for risk management?
Because disputes often surface before financial distress. They flag issues like unpaid invoices, regulatory fines, or commercial conflicts that can escalate months before balance sheets show trouble.
{ "_meta": { "dictionaryColumns": ["Variable", "Data_Type", "Sample_Value", "Description"] }, "data": [ { "Variable": "company_name", "Description": "Registered name of the company", "Business_Rules": "UTF-8 string, registry standard", "Source_System": "Open Data", "Data_Type": "VARCHAR", "Sample_Value": "EuroBuild Contractors GmbH" }, { "Variable": "website_domain", "Description": "Primary website domain for enrichment", "Business_Rules": "Valid domain string", "Source_System": "Web Intelligence", "Data_Type": "VARCHAR", "Sample_Value": "eurobuild.de" }, { "Variable": "litigation_case_count", "Description": "Number of legal cases filed against company", "Business_Rules": "Integer ≥ 0", "Source_System": "Open Data", "Data_Type": "INTEGER", "Sample_Value": "5" }, { "Variable": "case_typology", "Description": "Classification of litigation type", "Business_Rules": "ENUM: {Commercial dispute, Employment dispute, Regulatory fine, IP litigation}", "Source_System": "Web+AI Reasoning", "Data_Type": "ENUM", "Sample_Value": "Commercial dispute" }, { "Variable": "counterparty_type", "Description": "Type of counterparty in litigation", "Business_Rules": "ENUM: {Supplier, Customer, Employee, Government, Competitor}", "Source_System": "Web Intelligence", "Data_Type": "ENUM", "Sample_Value": "Supplier" }, { "Variable": "case_severity_index", "Description": "Normalized severity score of the case(s)", "Business_Rules": "0–100 index", "Source_System": "Web+AI Reasoning", "Data_Type": "INTEGER", "Sample_Value": "72" }, { "Variable": "press_mentions_count", "Description": "Number of litigation-related press mentions", "Business_Rules": "Integer ≥ 0", "Source_System": "Web Intelligence", "Data_Type": "INTEGER", "Sample_Value": "12" }, { "Variable": "press_sentiment", "Description": "Sentiment of litigation-related press", "Business_Rules": "ENUM: {Negative, Neutral, Positive}", "Source_System": "Web+AI Reasoning", "Data_Type": "ENUM", "Sample_Value": "Negative" }, { "Variable": "legal_exposure_score", "Description": "Composite exposure score (filings + press + severity)", "Business_Rules": "0–100 index", "Source_System": "Web+AI Reasoning", "Data_Type": "INTEGER", "Sample_Value": "84" } ] }
