How Carrier-Level Data Changes Pre-Suit Strategy in Bad Faith Cases

The demand deadline is two weeks out. The attorney has the claim file in front of her — the policy, the adjuster's letters, the expert report. She knows this insurance carrier by reputation. What she does not have is a picture of how this carrier has handled thousands of claims like hers across the state over the past three years. That information exists. It just has never been organized for her.

What plaintiff attorneys have had to work with

Bad faith strategy — the argument that an insurer failed to handle a claim fairly — has always been built from the inside out. The attorney works from the file: what the carrier knew, when it knew it, and what it did next. On top of that sits experience, the feel a lawyer develops after years of pushing similar claims.

That experience matters, but it is informal. It lives in one attorney's head and reflects only the cases that reached her desk. Public court records add some structure, but they only capture cases that were filed and fought. They say little about what carriers do before suit — which is exactly when strategy matters most.

What the data shows at scale

Aggregate carrier data asks a different question: not what happened in this claim, but what this carrier has done across thousands of similar claims over time.

At that scale, patterns appear. How fast does the carrier decide coverage, and does that speed change with the size of the claim? How often does it send a reservation of rights letter — a notice that it may later deny coverage — and how late in the claim does that tend to happen? How often do its claims resolve before suit versus after?

None of this touches any individual claimant. It is pattern data, stripped of personal information. The result is a behavioral profile: how this carrier tends to move, compared to similar carriers handling similar claims.

How that context changes the demand

The data does not change the law. What it changes is judgment: how to set the demand number, how long a response window to give, and when to escalate.

If a carrier's aggregate history shows it usually resolves claims like this one before suit, that shapes what a credible demand looks like. If its history shows it tends to raise coverage defenses late, that shapes how the demand letter frames the carrier's duties. And when the carrier goes quiet, the data helps distinguish a carrier that moves under pressure from one that digs in. Experience and data are not substitutes. They work together.

Leveling a structural imbalance

The carrier has always known its own book. Its claims teams can compare any single claim against every claim they have ever handled. The plaintiff attorney has had the file, her experience, and a filtered slice of court records. That imbalance is structural, and it grows with every claim the carrier processes.

Aggregate carrier data, built from public records, gives the plaintiff side a parallel picture — the carrier's track record as it appears in the public record, so one claim file can be read in market context. That is what DAIS Analytics was built to provide, and it is available now to plaintiff attorneys in more than a dozen states, including Florida, Texas, New York, and Colorado.