EU AI Policy Observatory

Published document record

EIOPA Big Data Analytics thematic review

Official metadata

Official title
Big Data Analytics in motor and health insurance: A thematic review
Official reference
ISBN 978-92-9473-142-5; DOI 10.2854/54208; EI-02-19-220-EN-N
Document type
Report
Document date
2019-05-08
Document date kind
Publication
Document-date evidence
European Insurance and Occupational Pensions Authority · Release header and publication field 8 May 2019; opening paragraph explicitly states that the report was published today. — Exact official publication used because no precise issue day is evidenced.
Publication date
2019-05-08
Publication-date evidence
European Insurance and Occupational Pensions Authority · 8 May 2019 release, opening paragraph and related-resource link to this thematic review. — Official release evidence for this independently citable text.
Additional official dates
Version label
Published thematic review
Legal status
Non binding
Legal-status evidence
European Insurance and Occupational Pensions Authority · Document title and substantive institutional report/factsheet content. — Institutional research or high-level guidance, not a binding legal instrument.
Language
en

Research classifications

These are researcher classifications used to organise and analyse the corpus; they are not official EU metadata.

Record level
Principal
Version status
Final
Temporal collection
Contemporary EU AI policy
Relevance class
Direct AI relevance
Policy stage
Implementation
Corpus tier
Directly related
Sectors
  • Health
  • Financial services
Policy membership
  • EU AI policy pathway

Classification evidence

  • Relevance class: Direct AI relevance · European Insurance and Occupational Pensions Authority · Printed pp.6-7 executive summary, pp.14-16 section 2.2.1 and pp.42-44 chapter 5: AI/ML in motor and health insurance, fairness, explainability and governance. Health-insurance data and pricing scope is explicit. — AI is a substantive policy or sectoral subject throughout the cited passages.
  • Sector tags: Financial services · European Insurance and Occupational Pensions Authority · Printed page 6 (PDF page 8), Executive summary; printed pages 14-16 (PDF pages 16-18), section 2.2.1: AI/ML applications in motor and health insurance. — Substantive AI/ML analysis concerns insurance services, supporting the financial-services subject label.
  • Sector tags: Health · European Insurance and Occupational Pensions Authority · Printed page 6 (PDF page 8), Executive summary; printed pages 14-16 (PDF pages 16-18), section 2.2.1: AI/ML analysis within the report's motor and health insurance scope. — Health-insurance analysis supports the broad health subject label; it is not a claim that this report regulates clinical practice.
  • Provenance tags: Authored by an EU agency or body · European Insurance and Occupational Pensions Authority · Corporate attribution and substantive institutional account. — Provenance reflects evidenced production or official publication; hosting is not itself authorship.
  • Provenance tags: Officially published · European Insurance and Occupational Pensions Authority · 8 May 2019 release, opening paragraph and related-resource link to this thematic review. — Provenance reflects evidenced production or official publication; hosting is not itself authorship.

Production provenance

Provenance tags
  • Authored by an EU agency or body
  • Officially published
Named bibliographic authors
Institutions and roles

Official sources and identifiers

  • Official source · Publisher/host: European Insurance and Occupational Pensions Authority
  • Official source · Publisher/host: European Insurance and Occupational Pensions Authority

Research assessment

Inclusion rationale
Printed pp.6-7 executive summary, pp.14-16 section 2.2.1 and pp.42-44 chapter 5: AI/ML in motor and health insurance, fairness, explainability and governance. Health-insurance data and pricing scope is explicit.
Researcher notes
PDF imprint supplies year 2019 only; April2019 in its filename is not issue-date evidence. The contemporaneous release establishes exact publication despite a later catalogue date of 19 June 2019. Publications Office is the printed publisher, while EIOPA is author and official host/releasing authority. Cover photograph credit: istock.com/SusanneB (PDF p.2), not a report author. Empirical sample is 222 insurers/intermediaries from 28 jurisdictions, not a census of the entire EU insurance industry. The initial review left concept coding and policy-group assignment incomplete. Broad policy-group membership was recorded on 12 September 2026; concept-review history remains separate. This editorial grouping does not establish a legal or procedural relationship.
Review status
verified

Relationships

No additional published relationships touch this record.

Verification

Expanded evidence review
Verified
Reviewed by
Yichen Hao
Evidence review date
2026-09-06T22:52:47Z

The evidence review date is not a separate human sign-off date. Review credit and evidence-review actions

  • Last verified: 2026-09-06T22:52:47Z · AI-assisted evidence review. Fresh HTTP 200 capture, 3037963 bytes, SHA-256 6b30027da9863aea7b1e6b229a4ceb205193d67ad37bdfbc70f223108bb20ba8. Printed pp.6-7 executive summary, pp.14-16 section 2.2.1 and pp.42-44 chapter 5: AI/ML in motor and health insurance, fairness, explainability and governance. Health-insurance data and pricing scope is explicit. Capture retained outside canonical data; no public archive guarantee. Exact receipt in candidate-health-finance-20260906 audit. PDF extraction recovered with pypdf after optional fitz module was unavailable.
  • Last verified: 2026-09-06T22:52:47Z · AI-assisted evidence review. Fresh HTTP 200 capture, 93558 bytes, SHA-256 3f8bd75bfee1646e68752c81e4fc207b064a162e070d2380719bad4c63c5ecf4. 8 May 2019 release, opening paragraph and related-resource link to this thematic review. Capture retained outside canonical data; no public archive guarantee. Exact receipt in candidate-health-finance-20260906 audit. PDF extraction recovered with pypdf after optional fitz module was unavailable.

Return to the Corpus