Reading carrier behavior from CRN data.

Florida is one of the few states in the country with a statutory framework that generates a publicly accessible, detailed record of insurance bad-faith claims before they reach the courthouse. The Civil Remedy Notice (CRN) — required by §624.155, Florida Statutes — is both a jurisdictional prerequisite for bad-faith litigation and, read in aggregate, one of the most informative public datasets available for plaintiff-side coverage analysis.

What the Civil Remedy Notice is

Florida Statutes §624.155 gives insureds the right to pursue a civil action against a carrier for bad-faith handling of a first-party claim. But to do so, the insured must first file a Civil Remedy Notice with the Florida Department of Financial Services (DFS) and serve it on the insurer. The notice initiates a 60-day cure period during which the carrier may correct the violation. If the carrier does not cure within 60 days, the insured may file suit.

The CRN is a standardized form that captures specific information: the identity of the insurer, the policy number, the type of coverage at issue, the claim number, the alleged statutory violation (from a defined list under §624.155), and a description of the claimed damages. Every CRN filed with DFS becomes a public record, maintained in DFS’s searchable database.

The cure-period mechanism is important: it means CRNs document not just the claim but the carrier’s response, or absence of a response, to an enumerated statutory violation. That is a specific, documented act of carrier conduct — not an allegation, but a regulated interaction with a statutory framework.

The dataset at the market level

Individual CRNs tell you something about a specific claim. Tens of thousands of CRNs, aggregated by carrier, claim type, violation category, and jurisdiction, tell you something about the market.

At that scale, CRN data begins to reveal patterns that are invisible at the level of any individual matter:

• Carrier propensity by line of business. Some carriers generate CRNs at rates materially above the market average for comparable lines. That signal, measured consistently over time, reflects something about how those carriers handle claims — not in any one instance, but as a systematic pattern of conduct.
• Violation type concentration. §624.155 lists specific categories of bad-faith conduct: failure to pay, failure to investigate, denial without reasonable basis. A carrier that consistently generates CRNs of the same violation type is showing a specific behavioral pattern, not isolated failures.
• Geographic and temporal variation. CRN volumes correlate with external events — catastrophic weather seasons, major construction defect enforcement periods — but also reveal which carriers respond differently to those same events. Cross-carrier comparison within the same event window surfaces the outliers.
• Cure-rate behavior. Carriers that cure at different rates, or that cure in particular violation categories but not others, are revealing their strategic calculus. That pattern is legible in the aggregate data.

What this means for bad-faith litigation strategy

A plaintiff attorney preparing a bad-faith claim benefits from knowing the carrier’s aggregate CRN history before drafting the notice. Not because the prior CRNs are admissible evidence of anything — they are not individually determinative — but because the pattern shapes the strategic context.

A carrier with a documented pattern of high CRN volume in the same line, the same violation category, and the same jurisdiction as the current matter is a different strategic situation than a carrier with a clean record. The former is potentially dealing with a systemic conduct issue; the latter may represent an outlier. That distinction should shape demand strategy, cure-period expectations, and litigation planning.

The aggregate CRN record also supports expert and damages analysis. A carrier’s pattern of conduct across the market is relevant to the “reasonableness” standard that bad-faith analysis turns on. Knowing how this carrier compares to others operating in the same line, in the same jurisdiction, over the same period is the kind of market-level context that the aggregate data can supply.

The de-identification layer

An important constraint in using CRN data for intelligence purposes is the distinction between aggregate analysis and individualized records. The value of the data is at the market level — carrier behavior patterns, propensity signals, benchmark comparisons — not at the level of specific claimants or individual claims. DAIS Analytics applies rigorous de-identification standards to its CRN-derived outputs, delivering aggregate, anonymized intelligence that reflects market patterns rather than individual claim records.

This is consistent with the public nature of the data: the CRN filing itself is a public record, but the intelligence value lies in the patterns, not the individual records. The same information that DFS makes publicly available, assembled and analyzed at scale, produces market intelligence of a different quality than any single filing could supply. For more on DAIS’s approach, see the Methodology page and the Data Sources overview.

Coverage health and solvency signals

CRN volume alone is a propensity signal. Combined with carrier financial health data — NAIC financial filings, Florida Insurance Guaranty Association (FIGA) status, surplus-lines solvency indicators — CRN patterns take on additional meaning. A carrier with elevated CRN volume and deteriorating surplus ratios is a qualitatively different situation than a financially healthy carrier with the same CRN profile.

DAIS’s Carrier Intelligence product integrates these layers: propensity signals from CRN data combined with solvency and financial-health indicators, delivering a composite view of carrier posture for bad-faith matters in Florida and Washington.