Data is the lifeblood of the insurance industry, yet most carriers, MGAs, and TPAs are still making critical decisions from fragmented systems, stale reports, and manual extracts. At Pomerol, our data and analytics services support insurers across P&C, life, health, and specialty lines — integrating data from policy administration systems, claims platforms, billing engines, and third-party data sources into a unified analytics environment.

Imagine moving beyond standard loss run reports. The insurers winning today are building data infrastructure that supports real-time underwriting decisions, proactive claims management, and regulatory reporting that doesn’t require a team of analysts working overtime. We build that infrastructure — and we build it to scale.

Executive and actuarial dashboards put combined ratio trends, reserve adequacy, and premium-to-loss analysis in one place. Leaders can drill from portfolio-level KPIs down to individual lines, geographies, or agent performance without waiting for the next monthly close.

Fraud detection and claims analytics are where data can move the needle fastest. We help insurers build anomaly detection models and claims triage workflows that flag suspicious patterns early — reducing leakage, shortening cycle times, and improving SIU efficiency without adding headcount.

Underwriting performance analytics connect policy data with loss outcomes so underwriters and product teams can identify which risks are performing, which segments are underpriced, and where appetite should shift. Pricing decisions backed by data, not instinct.

Agent and distribution analytics give sales and distribution leaders visibility into producer performance, pipeline health, and retention rates by channel — so growth strategies are built on what’s actually working.

For insurers navigating NAIC, state DOI, or internal governance requirements, our data engineering teams build compliant, auditable pipelines that reduce the manual burden of regulatory reporting while improving accuracy and timeliness.