refinq provides asset-level climate and nature risk analytics for banks and corporates across Europe, turning location data into audit-ready intelligence. The platform scores each site for physical climate and nature risk under IPCC scenarios, linking exposure to financial impact and adaptation measures. It serves regulatory frameworks including EBA, Pillar 3 ESG, CSRD, EU Taxonomy, SFDR, and ICAAP with a single dataset.
Funding
Funding not disclosed
Founders
Product
Problem
Banks and corporates face growing regulatory pressure to assess and disclose climate and nature-related risks, yet traditional approaches rely on sector-level proxies or manual assessments that lack the granularity supervisors require. Existing tools often fail to connect physical location data to financial impact, leaving institutions unable to meet EBA, CSRD, EU Taxonomy, and other disclosure requirements with confidence.
Solution
refinq provides an asset-level climate and nature risk analytics platform that scores each location for physical hazards under IPCC scenarios, then links exposure to financial impact and adaptation measures. Users upload site lists via CSV, Excel, or API, and the platform computes hazard scoring, nature screening, scenario projection, and financial translation—with every value carrying provenance for auditability. The platform serves multiple regulatory frameworks including EBA, Pillar 3 ESG, CSRD, EU Taxonomy, SFDR, and ICAAP from a single dataset. For banks, it converts loan book addresses into supervisory-grade risk data; for corporates, it delivers site-level risk assessments for operations and supply chains, including protected-area screening and satellite-based change detection.
Target Audience
Primary customers are banks and financial institutions needing supervisory-grade climate risk data for their loan books, as well as corporates requiring site-level risk assessments for operations, supply chains, and regulatory disclosures.
Features
- Address-level risk scoring with no sector proxies, providing the granularity supervisors and DNSH assessments assume
- Hazard scores under IPCC scenarios with horizons to 2100, covering physical climate and nature-related risks
- Machine learning and geospatial analysis processing over 2.5 billion data points from earth observation and climate models
- Protected-area screening, dependencies and impacts, and satellite-based change over time for nature risk assessment
- Site-specific adaptation measures prioritized by risk reduction potential
- Financial translation linking physical exposure to monetary impact for ICAAP, credit processes, and disclosures
- Data provenance tracking for every value, supporting audit-ready reporting across EBA, Pillar 3 ESG, CSRD/ESRS, EU Taxonomy DNSH, and SFDR