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Industry · Insurance & InsurTech

Data Engineering for Insurance

Policy, claims, and actuarial data unified with bank-grade lineage — regulatory reporting, pricing pipelines, and self-service BI proven with a 6-hours-to-15-minutes insurer engagement.

Problems We Solve

Industry Challenges

Core systems don't share a language

Policy admin, claims, billing, and reinsurance systems — often one per acquired book — disagree on the basics. We unify them into a governed warehouse with conformed policy, claim, and party dimensions.

Regulators want lineage, not promises

IFRS 17, Solvency II, and GDPR all demand provable data provenance. Our Apache Atlas lineage engagement covered 100% of a European bank's data assets — the same architecture applies to insurance reporting.

Actuaries wait on data, not models

Pricing and reserving teams lose weeks assembling datasets. We build governed actuarial data marts with point-in-time-correct history, so triangles and GLM features are a query away.

Reporting takes all day

When daily management reporting takes 6 hours, decisions wait. Our Snowflake + dbt + Looker stack cut exactly that cycle to 15 minutes for an insurance group.

Proven In Production

Measured Results

6h → 15min
Daily reporting cycle
insurance group · snowflake
100%
Data asset lineage
apache atlas · gdpr certified
12M/min
Record masking engine
non-prod data protection
Evidence

Related Case Studies

Questions, Answered

Insurance FAQ

Can you unify multiple policy administration systems?
Yes — including the post-acquisition case of several PAS for different books of business. We use CDC or batch extracts into a conformed model with standard policy, claim, coverage, and party dimensions.
Do you support IFRS 17 / Solvency II data requirements?
We build the data foundation those regimes demand: governed historisation, point-in-time correctness, full lineage from report figure back to source transaction, and auditable transformation logic in dbt.
How do you protect policyholder data in non-production?
Masked, referentially-consistent non-prod environments — our PySpark masking engine processes 12 million records per minute, so realistic test data never exposes real policyholders.
Can actuaries self-serve without breaking governance?
Yes — governed data marts with a semantic layer give pricing and reserving teams direct SQL/BI access to certified datasets, with row-level security and full audit trails.
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