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Confidential client / Insurance

Shipping a modern data platform for a commercial insurer

Stakforge built a production-ready lakehouse platform that unified policy, claims, and risk data for a commercial insurer, enabling analytics and AI on top of governed, trusted models.

Data EngineeringInsuranceLakehouse
INSURANCE / DATA LANDSCAPEFrom fragmented streams to governed domain productsIllustrative model / anonymized engagement
Insurance data as a governed landscapeIllustrative, anonymized insurance lineage. Policy administration, claims, billing, and broker sources enter incremental capture. A canonical model relates policy, coverage, exposure, claim, and reserve. Tests, freshness monitoring, and release approval govern reusable data products for underwriting, claims, actuarial, and finance. Data operations use alerts, runbooks, promotion, and rollback.01 / FRAGMENTED SOURCES02 / CANONICAL INSURANCE MODEL03 / GOVERNED RELEASEPolicy adminClaimsBillingBrokerINCREMENTALCAPTUREPolicyCoverageExposureClaimReservehashas claimcovershas reserveQUALITY GATEtest · freshnessrelease approvalUnderwriting1Claims2Actuarial3Finance4REUSABLE DOMAIN PRODUCTSOPERATIONS LOOPFreshness · schema drift · failed dependenciesRELEASE DISCIPLINERunbooks · environment promotion · rollbackLINEAGE: SOURCE → ENTITY → TESTED PRODUCT → DECISION USEInsurance source-to-product lineageIllustrative, anonymized insurance lineage. Policy administration, claims, billing, and broker sources enter incremental capture. A canonical model relates policy, coverage, exposure, claim, and reserve. Tests, freshness monitoring, and release approval govern reusable data products for underwriting, claims, actuarial, and finance. Data operations use alerts, runbooks, promotion, and rollback.SOURCESONTOLOGYPRODUCTSPPolicyCClaimsBBillingRBrokerMODELPolicyClaimTEST + RELEASEUnderwritingActuarialOBSERVE FRESHNESS / DRIFT / FAILED DEPENDENCIES
Trace / source to entityControl / test and releaseUse / shared domain products
ClientRegional commercial insurer
IndustryInsurance
EngagementLakehouse + data products
DeliveryStrategy through implementation

The insurer's policy administration, claims, billing, and broker systems evolved independently. Teams used disconnected extracts and manual reconciliation for nearly every major reporting cycle.

Underwriting, claims, actuarial, and finance stakeholders often arrived at different answers to the same business question because entity definitions and timing rules were inconsistent.

AI and advanced analytics initiatives were repeatedly delayed by unstable data inputs and weak lineage.

  • No governed source of truth for policy, claim, and exposure entities
  • Manual handoffs and brittle scripts in recurring reporting cycles
  • Weak observability for freshness, schema drift, and failed dependencies
  • Limited confidence in data used for pricing and risk strategy

Stakforge executed in structured releases: domain modeling, ingestion modernization, transformation hardening, and adoption enablement with business users.

  1. 01Defined policy, coverage, exposure, claim, and reserve entities with business sign-off.
  2. 02Replaced legacy extract chains with incremental ingestion patterns and repeatable contracts.
  3. 03Built modular transformations for underwriting, claims, actuarial, and finance marts.
  4. 04Implemented data tests and anomaly alerts tied to operational runbooks.
  5. 05Established release workflows, environment promotion, and rollback standards.
  6. 06Delivered stakeholder dashboards and onboarding sessions for new data products.

How the system works

Technology implementation

The platform architecture emphasized transparent operations and long-term maintainability over one-off optimization.

INSURANCE / DATA LANDSCAPEFrom fragmented streams to governed domain productsIllustrative model / anonymized engagement
Insurance data as a governed landscapeIllustrative, anonymized insurance lineage. Policy administration, claims, billing, and broker sources enter incremental capture. A canonical model relates policy, coverage, exposure, claim, and reserve. Tests, freshness monitoring, and release approval govern reusable data products for underwriting, claims, actuarial, and finance. Data operations use alerts, runbooks, promotion, and rollback.01 / FRAGMENTED SOURCES02 / CANONICAL INSURANCE MODEL03 / GOVERNED RELEASEPolicy adminClaimsBillingBrokerINCREMENTALCAPTUREPolicyCoverageExposureClaimReservehashas claimcovershas reserveQUALITY GATEtest · freshnessrelease approvalUnderwriting1Claims2Actuarial3Finance4REUSABLE DOMAIN PRODUCTSOPERATIONS LOOPFreshness · schema drift · failed dependenciesRELEASE DISCIPLINERunbooks · environment promotion · rollbackLINEAGE: SOURCE → ENTITY → TESTED PRODUCT → DECISION USEInsurance source-to-product lineageIllustrative, anonymized insurance lineage. Policy administration, claims, billing, and broker sources enter incremental capture. A canonical model relates policy, coverage, exposure, claim, and reserve. Tests, freshness monitoring, and release approval govern reusable data products for underwriting, claims, actuarial, and finance. Data operations use alerts, runbooks, promotion, and rollback.SOURCESONTOLOGYPRODUCTSPPolicyCClaimsBBillingRBrokerMODELPolicyClaimTEST + RELEASEUnderwritingActuarialOBSERVE FRESHNESS / DRIFT / FAILED DEPENDENCIESGoverned insurance data lineageIllustrative, anonymized insurance lineage. Policy administration, claims, billing, and broker sources enter incremental capture. A canonical model relates policy, coverage, exposure, claim, and reserve. Tests, freshness monitoring, and release approval govern reusable data products for underwriting, claims, actuarial, and finance. Data operations use alerts, runbooks, promotion, and rollback.01 / SOURCE SYSTEMSPolicy adminClaimsBillingBroker02 / CONFORMED INSURANCE MODELPolicyCoverageExposureClaimReserve03 / TEST · FRESHNESS · RELEASE APPROVALUnderwriting · claimsActuarial · financeOBSERVE / RUNBOOK / ROLLBACK
Trace / source to entityControl / test and releaseUse / shared domain products
01

Cloud object storage lake foundation with curated lakehouse layers

02

Incremental ingestion for policy and claims sources using CDC and scheduled batch flows

03

dbt and SQL model packages for canonical insurance logic and finance-ready outputs

04

Orchestration with dependency-aware workflows and automated failure notifications

05

Infrastructure as code for environment consistency and controlled releases

06

Quality and freshness monitoring with on-call visibility for data platform operations

Verified outcomes

Results

The insurer moved from reactive reconciliation to proactive data operations, which unlocked faster analytics iteration and stronger executive confidence.

Teams now launch new reporting and modeling efforts from reusable domain products instead of recreating extraction logic.

01

81% faster time-to-data for net-new analytics requests

02

62% reduction in recurring manual preparation for board and actuarial reporting

03

Governed cross-domain view of policy, claims, and exposure data

04

Stronger reliability through observability-first pipeline operations

Engagement goals

The engagement targeted both speed and trust: faster delivery of analytics-ready data, with enough governance for high-stakes insurance decisions.

Client benefits and business impact

Executive teams gained a more stable reporting cadence and fewer last-minute reconciliations before strategic reviews.

Underwriting and claims leaders had earlier access to trend signals, enabling faster adjustments in pricing strategy and operational staffing.

Most importantly, the insurer now has a durable foundation for AI initiatives without repeating foundational data work each cycle.

Why Stakforge

The client needed insurance domain understanding and hands-on modern platform execution in the same team. Stakforge provided senior practitioners who could align business definitions and production engineering.

We delivered an operating platform with governance, speed, and maintainability designed for long-term ownership.

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