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

The demand deadline is two weeks out. The attorney has the file in front of her — the policy, the adjuster correspondence, the expert opinion on damages, a handful of comparable verdicts she pulled from public court records. She knows this carrier by reputation. She has deposed their adjusters before, watched how they behave in litigation, developed a feel for how long they tend to sit before they move. What she does not have is a picture of how this carrier has handled claims like this one across its entire state portfolio over the past three years. That information exists. It just has not been organized for her.

What the plaintiff’s information set has been

Pre-suit bad faith strategy has historically been built from the inside out. The attorney works from the four corners of the claim file: what the carrier knew, when they knew it, what they did with the information, and whether the gap between the two is large enough to support a statutory or common-law bad faith theory. Layered on top of that is attorney experience — the accumulated intuition that comes from having pushed similar claims against similar carriers in similar postures.

That experiential layer matters. A seasoned plaintiff attorney who has litigated bad faith for fifteen years has processed hundreds of claim files, watched hundreds of carriers respond to hundreds of demand letters, and developed pattern recognition that no individual file can supply. But that knowledge is informal, non-transferable across a firm, and anchored in the past. It reflects what the carrier did in cases that made it to that attorney’s desk — not a systematic picture of what the carrier does across the full population of comparable claims.

Public court records add some structure. Jury verdicts, appellate opinions, and docket-level data from civil filings create a reference class of outcomes. But that reference class is filtered through the cases that got filed and litigated — the ones that did not settle pre-suit. It tells you relatively little about what the carrier does in the pre-suit window, which is precisely where bad faith strategy is most consequential.

What carrier behavior looks like at the portfolio level

Aggregate, anonymized carrier behavior data starts from a different question: not what happened in this claim, but what this carrier has done across thousands of claims like this one over time. The patterns that emerge at scale are qualitatively different from what any individual file shows.

At the portfolio level, it becomes possible to see how quickly a carrier moves from claim receipt to coverage determination across a defined claim type — and how that speed varies by claim severity, coverage amount, or policy vintage. It becomes possible to see how often a carrier escalates from standard handling to reservation of rights in claim populations that resemble a given fact pattern. It becomes possible to see the ratio of pre-suit to post-suit resolutions across the carrier’s book, and whether that ratio has shifted over time or differs across geographic markets.

None of that analysis touches any individual claimant or any individual claim. It is pattern data — aggregate behavior read off the population of claims the carrier has handled, delivered without any personally identifying information. The output is a behavioral signature: how this carrier tends to move, and how that movement compares to the market baseline for similar carriers handling similar claim types in the same states.

How this context changes the pre-suit demand process

Carrier data in the pre-suit window does not change the legal theory. The statutory bad faith framework, the reservation of rights analysis, the common-law standards for an insurer’s duty of good faith — none of that moves because of market-level data. What moves is the frame around the demand: the calibration of the ask, the timing of the escalation, and the decision about how to position the claim in the demand letter itself.

If the data shows that a carrier at this coverage level, for this claim type, resolves claims pre-suit at a materially higher rate than post-suit — and at a materially lower dollar amount pre-suit than what verdicts in the same claim category have averaged — that is relevant information for structuring the demand. It speaks to what the carrier is likely to treat as a credible number versus a number that signals the plaintiff is not seriously engaged. It speaks to whether a tight demand window puts pressure on the carrier or simply gives them a pretext.

The reservation of rights response is another point of contact. If the carrier’s aggregate behavior shows a pattern of initiating coverage defenses late in the claim cycle — after meaningful time has passed — that pattern is relevant to how the demand letter frames the carrier’s obligation. It does not substitute for the legal analysis of the specific policy and the specific facts. It adds a behavioral dimension that the legal analysis alone cannot supply.

The decision whether to escalate — to push the claim toward litigation rather than hold the demand window open — is also a place where carrier data has practical value. Experience tells an attorney when a carrier is stalling versus genuinely evaluating. Aggregate data tells her whether this carrier, at this coverage level, in this state, is a carrier that tends to move under pressure or one that tends to entrench when pushed. The two sources of information are not substitutes for each other. They work together.

The structural shift

The information asymmetry in bad faith cases has always run in one direction. The carrier knows its own book. Its pricing actuaries have modeled claim frequency and severity by coverage type, geography, and policy vintage. Its internal claims teams have access to every claim they have ever handled and can benchmark any individual claim against the population of similar claims in their system. Defense counsel, when they represent the same carrier across dozens or hundreds of matters, accumulates the same institutional picture. They know how the carrier thinks about its exposure, what it considers a credible demand, and where its tolerance for risk actually sits.

The plaintiff attorney has not historically had access to any of that. She has had the file, her experience, and whatever comparable verdicts she could pull from public records — which are filtered, as noted, toward the cases that did not settle. The asymmetry is not incidental. It is structural, and it compounds over time as the carrier’s institutional knowledge grows with each claim it processes.

Aggregate carrier behavior data — built from public records at scale, delivered without individual claim detail — is what begins to correct that asymmetry. It does not give the plaintiff attorney access to the carrier’s internal systems. It gives her a parallel picture: the behavioral signature of the carrier as it appears in the public record, across the full population of observable claim-adjacent events, organized so that a single claim file can be understood in market context rather than in isolation. That is what DAIS Analytics was built to provide — and it is available now to plaintiff attorneys in Florida, Texas, Nevada, Connecticut, New York, Colorado, Michigan, Missouri, Illinois, Indiana, Pennsylvania, South Carolina, Kentucky, South Dakota, Virginia, and North Carolina.

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.