What Aggregate Claims Data Reveals About Carrier Settlement Patterns

Two policyholders. Similar damage. Similar policies. Same carrier. Same state. One claim closed within 90 days at or near demand. The other went to suit, then appeal. From the outside of either file, there is no obvious explanation for the difference. But at the portfolio level — across thousands of similar claims with this carrier — the pattern is legible.

Settlement outcomes in first-party bad faith cases are not random. Carrier conduct follows patterns, and those patterns surface when you look at enough claims at once. Carriers have always known this about themselves. The plaintiff side rarely has. That gap is the one DAIS was built to close.

What aggregate carrier settlement data is — and what it is not

Aggregate carrier settlement data is not a claim lookup tool. It does not retrieve individual claim files, identify individual policyholders, or reconstruct any particular loss event. It organizes patterns across thousands of claims by carrier, claim type, jurisdiction, and outcome, and makes those patterns readable as market-level context.

Speed-to-resolution distributions show how long a given carrier typically takes to close first-party property claims under defined conditions. Escalation frequencies show how often similar claims progress from demand to litigation. Pre-suit versus post-suit resolution rates, broken out by claim type, show where in the process a carrier tends to resolve and where it tends to dig in. These are portfolio-level observations derived from aggregate, anonymized data. They are market context, not individual claim records.

Keep prediction and description apart, because the law cares about the difference and so should you. The data does not predict what any carrier will do in any particular case. It describes what a carrier has done across a large population of comparable claims. Prediction versus description is the line between junk science and market intelligence.

What patterns appear at the portfolio level

The most consistently useful patterns involve timing and escalation. Different carriers resolve first-party property claims at markedly different speeds, and those speed distributions are not uniform across claim types. A carrier that resolves roof-damage claims quickly may have a documented pattern of protracted handling on water-intrusion claims. A carrier with low escalation rates overall may show elevated escalation rates in a specific jurisdiction or for claims above a certain dollar threshold.

Escalation frequency — how often similar claims cross from demand to litigation — is one of the more actionable signals in the data. A carrier whose aggregate escalation rate on comparable claim types runs low is telling you something about its institutional posture toward that claim type. A carrier whose rate runs high is telling you something different. Neither observation determines the outcome of any individual matter, but both carry information about the environment your client's claim is moving through.

Pre-suit resolution rates round out the picture. Some carriers settle the overwhelming majority of comparable claims before a suit is filed. Others settle at similar rates but almost exclusively post-suit. That distinction has practical consequences for how you structure a demand, how you read a reservation of rights response, and what the timeline for resolution realistically looks like from the moment a claim is submitted.

How plaintiff attorneys use pattern data in practice

The application is not evidentiary. You do not stand up in court and argue that aggregate data shows this carrier always does X. The data does not go in a pleading. It informs the work that happens before any pleading is filed.

When you frame a demand, knowing where your client's claim trajectory sits relative to the carrier's documented pattern for this claim type and jurisdiction gives you a calibration point. A demand that reflects the carrier's typical resolution window, rather than a generic timeline, is a more precisely framed demand. When a reservation of rights response arrives, knowing what that carrier typically does after issuing a similar reservation in comparable circumstances gives you a baseline for reading whether the response signals impending denial or marks a routine procedural step.

The data also flags when a claim is tracking abnormally. If a carrier's aggregate pattern shows 70 percent of comparable claims resolving within 120 days and your client's claim is at 200 days with no substantive response, that divergence is meaningful market context — not proof of bad faith, but a concrete data point for what the carrier's own historical conduct says is typical.

The limits of this intelligence

This is market intelligence, not a prediction engine. Every case turns on its own facts, its own policy language, and the law of its jurisdiction. A carrier's aggregate historical pattern does not determine how it will handle any individual claim. A claim that diverges from the carrier's typical trajectory may have a straightforward explanation that has nothing to do with bad faith, and a claim that tracks the carrier's typical pattern exactly may still involve actionable conduct. The aggregate data contextualizes. It does not determine.

The data is also historical. It reflects conduct over the period covered by the underlying records. Carrier behavior shifts — in response to regulatory pressure, reinsurance conditions, management changes, or litigation outcomes. The aggregate picture describes a documented pattern; it does not guarantee that pattern holds going forward.

These limits are not reasons to avoid the data. They are reasons to use it correctly: as one input into a disciplined case evaluation, not as a substitute for it.

DAIS Analytics, LLC is a data-analytics company and is not a law firm. David M. Greene, its principal, is a member of The Florida Bar. His Florida Bar membership does not create an attorney-client relationship between DAIS and any user or subscriber. Nothing on this website constitutes legal advice. DAIS services are nonlegal services as defined under Rule 4-5.7 of the Rules Regulating The Florida Bar. Data is delivered in aggregate, anonymized form and does not predict, guarantee, or valuate any individual claim or case.