Foreign direct investment reshapes local industries, but its influence often hides behind complex corporate structures. Consultants, SaaS providers, and foresight units struggle to identify which subsidiaries are controlled abroad, and what risks this creates.
This segment standardizes ownership chains, flags foreign control, and scores exposure across geopolitical, compliance, and structural dimensions — turning raw ownership into actionable strategy.
Normalize legal ownership chains to identify foreign parents.
Assign exposure score based on % control, depth, and risk layers.
Enrich with website, workforce, and investment-related signals.
This segment is activated with a blend of trusted sources and your own inputs
AI reasoning
User Input
Curated APIs
Web intelligence
Ranked dataset of subsidiaries under foreign control
Ownership chains standardized with % control, depth, and type
Risk layers: sanctions, geopolitical sensitivity, ownership recency
Enrichment with workforce, revenue, and investment context
FDI exposure scores, rollups by sector and country
Your questions on this segment, answered
Your questions on this segment, answered
Can I explain the scores internally to regulators or management?
Absolutely. Every score is backed by traceable sources and a clear rationale, making it defensible in regulatory or strategic discussions.
Does the dataset flag sanctioned entities?
Yes. Each subsidiary is checked against sanction lists, with a risk flag and confidence score, so compliance teams can act quickly.
How can the FDI Exposure Score be applied in workflows?
Scores can be filtered, ranked, and exported into CRM, BI, or risk dashboards. This allows prioritization of subsidiaries and aggregation at sector/country level.
Who benefits most from this dataset?
Compliance teams use it to flag foreign control, foresight units to assess geopolitical risk, and consultants to advise on FDI strategies at sector or country level.
How current is the foreign control information?
Ownership links are updated continuously from registries and web intelligence, with timestamps for first foreign control and latest change. This ensures transparency on recency.
How is this different from standard corporate ownership databases?
Traditional datasets map ownership but stop at legal hierarchy. Here, we add risk layers—sanctions, geopolitical sensitivity, and ownership change recency—to make exposure actionable.
How reliable are the ownership and exposure scores?
Every chain is sourced from registry or open data, normalized, and scored with transparent rules. Each field carries a confidence score so exposure can be audited.
{ "_meta": { "dictionaryColumns": ["Variable", "Data_Type", "Sample_Value", "Description"] }, "data": [ { "Variable": "company_name", "Description": "Registered name of the local subsidiary", "Business_Rules": "As in registry", "Source_System": "Open Data", "Data_Type": "VARCHAR", "Sample_Value": "Hexa Rail Services SAS" }, { "Variable": "website_domain", "Description": "Primary website domain for enrichment", "Business_Rules": "Valid domain format", "Source_System": "Web Intelligence", "Data_Type": "VARCHAR", "Sample_Value": "hexarail.fr" }, { "Variable": "local_country_iso", "Description": "ISO code of the subsidiary country", "Business_Rules": "2-letter ISO 3166-1", "Source_System": "Open Data", "Data_Type": "VARCHAR", "Sample_Value": "FR" }, { "Variable": "sector_naics", "Description": "Sector classification code", "Business_Rules": "NAICS code 2–6 digits", "Source_System": "Open Data", "Data_Type": "VARCHAR", "Sample_Value": "488210" }, { "Variable": "ultimate_parent_name", "Description": "Global ultimate controlling entity", "Business_Rules": "Resolved from ownership chain", "Source_System": "Open Data", "Data_Type": "VARCHAR", "Sample_Value": "TransPacific Holdings Ltd" }, { "Variable": "ultimate_parent_country_iso", "Description": "Country of the ultimate parent entity", "Business_Rules": "2-letter ISO code", "Source_System": "Open Data", "Data_Type": "VARCHAR", "Sample_Value": "GB" }, { "Variable": "ownership_percentage", "Description": "Control % held by ultimate parent", "Business_Rules": "0.0–100.0 decimal", "Source_System": "Open Data", "Data_Type": "DECIMAL", "Sample_Value": "82.4" }, { "Variable": "ownership_type", "Description": "Categorized control type", "Business_Rules": "ENUM: {Minority, Joint Control, Majority, Full Control}", "Source_System": "Web+AI Reasoning", "Data_Type": "ENUM", "Sample_Value": "Majority" }, { "Variable": "ownership_directness", "Description": "Direct or indirect control", "Business_Rules": "ENUM: {Direct, Indirect}", "Source_System": "Open Data", "Data_Type": "ENUM", "Sample_Value": "Indirect" }, { "Variable": "link_depth_to_ultimate", "Description": "Number of ownership layers to ultimate parent", "Business_Rules": "Integer >= 1", "Source_System": "Open Data", "Data_Type": "INTEGER", "Sample_Value": "2" }, { "Variable": "sanctions_risk", "Description": "Flag if parent/subsidiary is under sanctions", "Business_Rules": "ENUM: {Yes, No}", "Source_System": "Web Intelligence", "Data_Type": "ENUM", "Sample_Value": "No" }, { "Variable": "geopolitical_sensitivity", "Description": "Exposure level based on parent country", "Business_Rules": "ENUM: {Low, Moderate, Elevated}", "Source_System": "Web+AI Reasoning", "Data_Type": "ENUM", "Sample_Value": "Moderate" }, { "Variable": "ownership_change_risk", "Description": "Recent change in ownership control", "Business_Rules": "ENUM: {Stable, Recent Change}", "Source_System": "Web+AI Reasoning", "Data_Type": "ENUM", "Sample_Value": "Recent Change" }, { "Variable": "fdi_exposure_score", "Description": "Composite exposure score", "Business_Rules": "0–100 integer", "Source_System": "Web+AI Reasoning", "Data_Type": "INTEGER", "Sample_Value": "78" } ] }
