Insurance Claims and Loss Data | DataSupplier
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Insurance claims and loss data

DataSupplier·12 min read

Claims and loss data is the raw material of pricing and reserving. It is also personal and sensitive. This guide covers sourcing claims and loss data responsibly.

Available across the EU. DataSupplier sources and delivers this data in all 27 European Union countries — including Germany, France, Spain, Italy, the Netherlands and Poland — and across the EEA, in the format and cadence you need.

Why claims data matters

Pricing, reserving and risk selection depend on understanding losses, frequency, severity and cost. Market and pooled claims data complements an insurer own experience.

The data landscape

  • Loss costs: aggregated claims by peril and segment.
  • Frequency and severity: distributions.
  • Fraud signals: patterns and indicators.
  • Benchmarks: market loss experience.

Privacy and pooling

Claims data is personal and sensitive, so market and pooled data is aggregated and governed. Industry pools share loss experience under strict rules.

Common use cases

Pricing and reserving, risk selection, fraud detection, and benchmarking.

Sourcing considerations

Aggregated market and pooled data, plus relevant external signals, support analysis. Provenance, methodology and lawful basis are central, and aggregation protects privacy.

In a managed model

A managed partner can source aggregated claims and loss data and relevant external signals with documented provenance.

Aggregation and pooling

Claims and loss data is personal and sensitive, so market and pooled data is aggregated and governed; industry pools share loss experience under strict rules. The value for pricing and reserving is in distributions, frequency and severity by peril and segment, that complement an insurer’s own experience without exposing individuals.

Provenance for regulated use

Because claims data feeds pricing, reserving and capital, lawful basis, provenance and methodology are central. Aggregation protects privacy while still supporting benchmarking and fraud analysis, and documented sourcing keeps the analysis defensible to actuaries and supervisors.

Key takeaways
  • Claims data drives pricing, reserving and risk selection.
  • Combine loss costs, frequency/severity, fraud signals and benchmarks.
  • Claims data is personal; market data is aggregated and governed.
  • Provenance and lawful basis are central.

Sources & further reading

  • EIOPA and national supervisors: insurance data.
  • Industry loss-pooling bodies.
  • EUR-Lex: Regulation (EU) 2016/679 (GDPR).
  • Actuarial-association data standards.
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