Thinklytics

Insurance Analytics Consulting

Insurance data analytics for P&C, life, and health carriers. Unified claims data, automated regulatory reporting, and a metric layer actuaries trust.

Topics

  • insurance data analytics consulting
  • actuarial data governance
  • claims data unification
  • NAIC reporting automation
  • insurance AI consulting
  • underwriting analytics

Work delivered in this industry

Regional P&C Insurer

We combined claims data from four outdated systems to unlock $8.4M for AI underwriting.

1.2M Policy records unified across 4 systems.

National Life Insurance Carrier

Cut NAIC statutory reporting from 6 weeks to 4 days by automating processes, saving $1.1M in yearly compliance labor costs.

6 weeks to 4 days NAIC filing preparation cycle.

Mid-Market P&C Carrier

Deployed a fraud detection model that flags $6.2M in suspicious claims each year

$6.2M Suspicious claims identified annually.

Regional Commercial Lines Insurer

Deployed loss ratio analytics to 42 underwriters, cutting combined ratio by 3.2 points in six months.

3.2 points Combined ratio improvement in two quarters.

What clients in this industry usually arrive with

  • Reporting cannot be trusted, described as sales and finance numbers do not match or inconsistent KPI reporting. Resolved by Metric governance, data cleaning, master data management, data engineering.
  • Reporting takes too much work, described as automate monthly reporting or combine reports from multiple systems. Resolved by Executive reporting, integrations, ERP consolidation.
  • Employees waste time finding information, described as search internal company documents or find policies and procedures quickly. Resolved by Knowledge assistant, RAG, unstructured data platform.
  • Systems require manual handoffs, described as eliminate manual data entry between systems or automate approval workflows. Resolved by API integration, workflow automation, human review design.
  • AI projects are not delivering value, described as measure AI ROI or AI pilot not moving to production. Resolved by Strategy, readiness assessment, AI PMO, adoption.
  • AI is too risky to deploy, described as AI confidential data protection or control AI agent permissions. Resolved by Governance, AI security, agent identity, evaluation.

Other industries

Start with the 30-day Analytics Truth Audit.

Insurance data work that holds up under regulatory scrutiny.

Why insurance data projects stall.

What insurance clients ask before they engage.

Often paired with insurance engagements.

Ready to fix your insurance data layer?

How do you handle data from multiple legacy policy administration systems?

We build a unified data layer that normalizes and reconciles data from multiple source systems without requiring a platform replacement. We map each source to a common semantic model and document the lineage so every number is traceable to its origin.

Yes. We build automated pipelines that produce regulatory outputs directly from your certified data layer. This eliminates the manual reconciliation that typically consumes weeks of actuarial and finance team time before each filing cycle.

Most insurance data foundation engagements run 12 to 16 weeks. We start with a 30-day Analytics Truth Audit that maps your current data landscape and identifies the highest-priority gaps before any build work begins.

We work with both. The data structures differ significantly between lines of business, but the underlying governance and metric layer challenges are consistent. We have experience with personal lines, commercial lines, specialty, and health insurance data environments.

Insurance data analytics consulting for P&C, life, and health carriers. Unify claims data, automate regulatory reporting, and build trusted metrics.

Data analytics for insurance carriers and MGAs. Claims, underwriting, and risk metrics.

Insurance companies sit on decades of claims, underwriting, and policyholder data that is rarely unified or governed. We build the metric layer that makes regulatory reporting, loss ratio analysis, and AI-driven underwriting possible.

Insurance data work that holds up under regulatory scrutiny.

Start with a 30-day Analytics Truth Audit. We identify exactly what is broken and give you a 90-day roadmap to fix it.