Claims leaders know this moment well. When a concern is raised – rising severity, increasing representation, or shifts in claimant behavior. Heads nod. Then comes the familiar response: “Do we have data to support that, or is this anecdotal?”
The irony, of course, is that claims is already one of the most data-rich functions within an insurance organization. Claims represent roughly 70% of an insurer’s premium and involve thousands of individual claims. Every loss comes with structured fields, unstructured narratives, documents, invoices, and dozens of adjusters’ decisions – all data points.
The issue is not a lack of data. It is a lack of accessible, usable, and trusted analytics, driven by fragmented legacy systems, inconsistent data structures, and reporting processes that make it difficult to turn raw claims data into timely insights.
As many carriers move to modern Claims management platforms, these barriers are finally coming down, unlocking opportunities that were previously out of reach.
This blog outlines a practical way to think about the Claims analytics journey – how claims experts can move from anecdotal insight to evidence-based decisions, and how that foundation enables more advanced analytics, including predictive, generative, and agentic AI.
To shift from anecdotes to analytics, Claims organizations need more than isolated dashboards or one-off models.They need a structured progression that builds capability over time while delivering value, such as improved cost control, reduced cycle time, and enhanced customer experience as market dynamics change.
As a Claims leader working with insurers across the U.S. and Canada, I typically see successful Claims organizations progress through four phases. While the pace and sequencing vary, skipping steps almost always creates adoption issues that later lead to low adoption and unrealized ROIs.
Phase 1: Data foundations – making claims data usable
This phase is about answering a deceptively simple question: Can we see what’s happening in claims?
Key steps:
Use cases:
What success looks like:
Phase 2: Proving value with insight and early wins
Once data is usable, the focus shifts from visibility to credibility. The goal is to use analytics to solve visible, high-impact claims issues and demonstrate tangible value early, earning trust by solving real problems and influencing behavior.
Key steps:
Use cases:
What success looks like:
Phase 3: Embedded analytics – from insight to action
In this phase, analytics becomes part of the workflow itself. Insights guide decisions in real time rather than being reviewed after the fact. The emphasis shifts to operationalizing insights within adjuster workflows.
Key steps:
Use cases:
What success looks like:
Phase 4: AI & agentic enablement – scaling expertise
This phase builds on embedded analytics and introduces agentic AI to reduce friction and increase consistency at scale.
Explore where AI can create measurable value in claims.
Key steps:
Use cases:
What success looks like:
Key sign you’re getting it right:
AI improves speed and consistency without undermining trust or judgment.
Claims organizations often operate in multiple phases at once, but long-term success relies on respecting the progression:
Strengthen your analytics to improve decision‑making, efficiency, and outcomes.
When insurers skip steps – especially jumping straight to AI – the result is often low adoption, rework, skepticism, and unrealized benefits.
Those who build deliberately transform claims from being seen as “anecdotal” to becoming a credible, data‑driven engine of organizational performance, delivering:
Where to begin:
The journey typically takes two to three years of deliberate effort, and it’s not strictly linear. Foundations are revisited, models are refined, and use cases expand as conditions change. But building a strong analytics foundation is essential for the next‑generation claims operating model — and the best time to start is now.
