Financial services & insurance data: credit, ESG and climate risk

Banks, asset managers and insurers run on external data, and the demands on it, from prudential rules to climate disclosure, keep rising. This guide covers the data types that matter for finance and insurance, the quality and licensing pitfalls, and how to source it with the governance regulators expect.

Why financial data demand is rising

Risk, pricing and disclosure all depend on external data, and regulation is widening the requirement, from credit and market risk to ESG and climate. The edge increasingly comes from combining traditional datasets with alternative and geospatial data.

The finance & insurance data landscape

• Company and credit: financials, corporate registries, ownership and credit-risk signals.
• Market data: prices, reference and economic indicators.
• Alternative data: footfall, transactions and other non-traditional signals.
• ESG and climate: emissions, ESG indicators and climate-risk data.
• Insurance-specific: property and catastrophe risk, geospatial hazard exposure.

Common use cases

Credit and counterparty risk, underwriting and pricing, portfolio and catastrophe modelling, regulatory disclosure (CSRD, SFDR), and alternative-data-driven investment research.

Sourcing considerations

Licensing is often the hardest part: redistribution, derivative and group-entity rights need care, and some market data carries strict usage terms. ESG scores diverge between providers, and alternative data raises privacy and methodology questions. Provenance is essential where data feeds regulated decisions.

Delivery and cadence

Trading and risk want low-latency feeds via API; disclosure and modelling use scheduled batches in analytical formats. Insurance catastrophe work leans on geospatial layers and consistent referencing.

Governance

Financial data feeds regulated decisions, so auditability and provenance are non-negotiable, and personal or alternative data brings the GDPR into scope. Practices aligned with NIS2 and ISO/IEC 27001 principles support the controls supervisors expect.

Market data vs alternative data

Financial data splits broadly into traditional market and reference data, prices, fundamentals, ratings, corporate actions, and alternative data, non-traditional signals such as transactions, footfall or satellite imagery. Traditional data is authoritative but widely held, so it rarely confers an edge; alternative data can, but with shorter shelf life and higher compliance and quality risk. Mature sourcing blends both, using traditional data as the backbone and alternative data for differentiated signal, with each held to its own provenance standard.

Licensing pitfalls in financial data

Financial data is among the most tightly licensed of all. Watch for restrictions on redistribution, derived data, display vs non-display use, per-user and per-application fees, and group-entity scope. Exchange and benchmark data carry particular constraints. A use the licence does not cover is a use you cannot make, regardless of how valuable, so confirming permitted use and the right scope before acquisition is essential, not a formality.

A financial data checklist

• Is this traditional or alternative data, and does it actually confer an edge?
• What exactly does the licence permit (redistribution, derivative, display, group)?
• For alternative data, is there an MNPI or privacy concern in how it was collected?
• Are provenance and methodology documented for regulated decisions?
• What latency does the use, trading or analysis, require?

Sources & further reading

• ECB, EBA and ESMA: financial data and reporting frameworks.
• European Commission: CSRD, EU Taxonomy and SFDR.
• EUR-Lex: Regulation (EU) 2019/2088 (SFDR).
• EUR-Lex: Regulation (EU) 2016/679 (GDPR).