Why Florida’s human-in-the-loop mandate matters now
When automation reshaped claims in pursuit of speed and savings, few expected the next big upgrade would be a return to visible human judgment in the most consequential moments of a claim’s life cycle. Florida’s House Bill 527, filed on November 24, 2025, meets that moment by drawing a bright line: no insurer may deny a claim—or a portion of a claim—based solely on an automated system, and a qualified human professional must take responsibility for the final determination. Slated to take effect July 1, 2026, the measure shifts the emphasis from throughput to accountability, transparency, and fairness without discarding the powerful tools that have improved efficiency.
The core message is balance rather than backlash. Algorithms, artificial intelligence, and machine learning remain welcome as decision-support inputs, but the bill insists on independent human analysis before a denial proceeds—no “rubber-stamping” of a machine suggestion, and no delegating the final call to code. For a market as large and complex as Florida’s, this introduces meaningful operational changes: carriers must redesign workflows, bolster documentation, and prepare for deeper regulatory review while maintaining service levels that policyholders expect.
Why this shift in claims technology oversight matters to Florida
The last decade rewarded automation for reducing cycle times and manual touchpoints, yet regulators are now elevating explainability and consumer protections where decisions can materially affect people and businesses. HB 527 fits that recalibration by reinforcing that an insurer’s duty runs through a human decision-maker who can interpret policy language, weigh facts, and own the outcome. Rather than dampening innovation, the law clarifies the terms of responsible use, aiming to prevent opaque tools from setting outcomes without meaningful review.
Clarity starts with definitions. By defining “algorithm,” “artificial intelligence system,” and “machine learning system,” the bill removes guesswork about which tools are covered and what oversight is required. It also layers in robust recordkeeping: denial notices must identify the responsible human reviewer and state that no automated tool was the sole basis for the result. Claims manuals must be updated to explain how tools operate and how compliance is maintained. Oversight rests with the Office of Insurance Regulation, with the Financial Services Commission empowered to issue implementing rules, creating a durable framework in which innovation can continue under visible, verifiable human control.
Implementing HB 527: A Step-by-Step Playbook for Carriers
This guide helps claims leaders translate policy into practice by building a compliant, efficient, and auditable human-in-the-loop process that protects consumers while preserving the benefits of decision-support technology. Each step focuses on operational clarity, sustainable staffing, and clean documentation, so teams can meet regulatory expectations without sacrificing service quality.
The aim is straightforward: ensure that every denial reflects an independent human assessment, supported by explainable tools and backed by a complete audit trail. Moreover, the steps below position organizations for evolving rules, minimizing rework and shortening the path from pilot to steady state.
Step 1 — Identify Covered Tools and Map Current Workflows
Begin with a system-wide inventory. Catalog every automated system that influences claims outcomes, from fraud scores and coverage verifiers to damage estimators and payment blockers. Document how each system’s output is consumed and flag the points where those outputs inform denials, whether direct or indirect. This visibility anchors compliance and guides where governance controls must sit.
The value of this mapping is twofold. It reveals pockets where automation may already be acting as the de facto decision-maker, and it spotlights opportunities to enforce human checkpoints without stalling throughput. Think of it as a blueprint that ensures no model or rule engine quietly nudges a denial across the finish line without deliberate human review.
Clarify scope with precise definitions
Use the bill’s definitions to classify each tool as an algorithm, an artificial intelligence system, or a machine learning system. This step reduces ambiguity about what requires human oversight and ensures compliance is consistent across departments and lines of business. A shared taxonomy also helps vendors understand the expectations placed on their solutions.
With clear categories, gaps become easier to spot. If a tool is marketed as “assistance” but materially drives outcomes, it should be treated as covered. In contrast, systems limited to clerical support—like document formatting—fall outside the scope. Explicit classification prevents scope creep and avoids accidental noncompliance.
Trace decision touchpoints end to end
Diagram the full claim flow, from first notice of loss to resolution, to identify where decisions branch toward denial. Mark the exact moments where human review occurs and where automation currently drives outcomes or presents recommendations. Include all integrations: vendor portals, appraisal engines, and internal rule sets.
This end-to-end tracing should reveal choke points and orphaned steps where reviewers might be tempted to rely on a model output without analysis. Addressing those gaps early helps maintain velocity while ensuring that the human review is genuinely substantive and not a perfunctory click-through.
Step 2 — Assign Qualified Human Decision-Makers
Designate licensed adjusters or other qualified professionals as the final decision-makers for denials, and ensure their authority is recognized in system permissions and workflows. This formal assignment communicates ownership and clarifies who must assess facts and policy language before any denial is issued.
The designation also supports accountability. When individuals know their names will appear on denial notices, they are more likely to apply careful scrutiny, consult policy provisions directly, and validate the evidence underlying model outputs. Ownership drives diligence.
Define qualifications and authority clearly
Spell out the required credentials for final reviewers, from adjuster licenses to line-specific expertise (property, auto, health). Provide role descriptions that list responsibilities, decision boundaries, and escalation paths for complex or ambiguous cases. Clear definitions help staffing, training, and performance management stay aligned.
When roles are unambiguous, reviewers are empowered to challenge automated suggestions that do not fit the facts or policy language. Authority that is both documented and enforced avoids shadow approvals and sustains the independence the bill demands.
Avoid proxy approvals
Prohibit proxy or courtesy approvals that simply echo another person’s sign-off or mirror a prior determination without fresh analysis. Make it explicit that each final reviewer must conduct their own evaluation, even if multiple professionals have already handled the file.
This rule safeguards against a chain of nominal reviews that collectively amount to rubber-stamping. The final decision-maker should bring a fresh, independent perspective, confirm the underlying evidence, and articulate a reasoned basis for the conclusion.
Step 3 — Embed Independent Human Analysis Before Any Denial
Require the final reviewer to independently analyze the facts, policy language, and claim record, then evaluate any automated outputs for accuracy and relevance. The model may inform, but it must not decide. Establish a standard that the reviewer can defend the decision without relying on a model’s phrasing or score.
The payoff is better decisions and improved defensibility. When reviewers synthesize facts and policy provisions themselves, they are more likely to catch mismatches, data entry errors, or out-of-scope model applications that could otherwise skew the outcome.
Guard against “rubber-stamping”
Introduce structured review checklists that capture rationale in the reviewer’s own words rather than auto-filling with system recommendations. Require explicit confirmation that the model’s suggestion was considered and either accepted with reasons or overridden with reasons.
These checklists should be concise but demanding. By forcing a short narrative that ties policy language to evidence, they make it harder to pass model outputs through unexamined and easier for auditors to confirm independence.
Require source verification
Direct reviewers to verify underlying evidence—photos, vendor reports, adjuster notes, recorded statements—rather than rely on summaries produced by automated systems. Verification reduces the risk of cascading errors from misclassified documents, OCR misreads, or data mapping issues.
The added step also strengthens consumer protections. When evidence is validated, denials rest on reliable foundations, and appeal outcomes are less likely to expose preventable mistakes or brittle automation.
Step 4 — Build a Complete Audit Trail
Create a denial log that records who made the decision, who reviewed it, when those actions occurred, the specific basis for the decision, and the automated inputs considered. This audit trail should be easy to retrieve and aligned with system-of-record standards.
Comprehensive logging builds confidence with regulators and consumers alike. It also supports continuous improvement by revealing patterns—where automation is strong, where reviewers frequently override, and where training can close gaps.
Capture rationale in plain English
Require reviewers to store a concise explanation that links the policy provisions to the facts. Keep this narrative distinct from any model output or recommendation text so it stands on its own as a human-authored rationale.
Plain-English explanations reduce friction in appeals and mediations. They help consumers understand the decision and give supervisors a clear window into the quality of the analysis.
Ensure system-of-record alignment
Synchronize logs across claims platforms, AI tools, and document repositories so timestamps, user IDs, and versions match. Avoid parallel, unsynced notes that create gaps or contradictions.
Alignment simplifies examinations and internal audits. When data sources tell the same story, compliance reviews move faster and disruption to frontline teams is minimized.
Step 5 — Update Denial Notices and Templates
Revise denial notices to identify the responsible human decision-maker and affirm that no automated tool served as the sole basis for the decision. Make this identification a required field so notices cannot be finalized without it.
Stronger notices improve trust. When consumers see a named reviewer and a clear explanation, they gain a path to ask informed questions, and disputes can focus on substance rather than process.
Make disclosures unambiguous
Include the reviewer’s name and title, a brief summary of the determination, and a statement that automated tools were not the sole basis for the outcome. Keep the structure clean and the language straightforward.
These disclosures reduce confusion and demonstrate compliance transparently. They also set a standard format that customer service teams can support with consistent answers.
Standardize for consistency
Use templates with required fields to reduce variability and common errors. Build validation checks into the template to confirm all mandatory disclosures are present before issuance.
Consistency helps consumers and regulators compare cases and improves training for new staff. It also makes quality assurance more efficient by establishing what a complete, compliant notice looks like.
Step 6 — Revise Claims Manuals and Model Governance
Update claims-handling manuals to explain how automated tools operate, their intended use, and the controls in place to ensure compliance. Document the roles responsible for monitoring performance and the procedures for periodic review.
These manuals become the central reference during audits and examinations. Clear guidance prevents teams from stretching a model beyond its approved context and preserves the integrity of the human-in-the-loop design.
Explain model purpose and limits
Describe the model’s objective, data inputs, known limitations, and the decision contexts for which it is approved. Note any segments where performance is weaker or where additional verification is required.
By acknowledging limits, carriers reduce reliance on black-box behavior and encourage thoughtful human intervention when confidence drops. Transparency inside the organization reduces downstream risk.
Set change-control protocols
Establish governance for model updates, requiring testing, documented approvals, and stakeholder sign-offs before deployment. Track version history and ensure training materials reflect the latest configuration.
Change control prevents unvetted updates from altering outcomes in subtle ways. It also ensures that reviewers know when a tool’s behavior has shifted and how to adapt.
Step 7 — Train, Test, and Monitor
Deliver training tailored to adjusters, supervisors, and quality assurance teams on independent review standards, documentation expectations, and the correct use of assistive tools. Reinforce that model outputs are inputs—not orders.
Training should emphasize practical scenarios, helping reviewers balance speed with rigor. When teams understand the why behind each step, compliance becomes a habit rather than a hurdle.
Scenario-based drills
Create drills in which model suggestions conflict with key facts or policy provisions. Ask reviewers to explain and document why the automated recommendation was wrong and how they resolved the discrepancy.
These exercises sharpen judgment and build confidence to override when appropriate. Over time, drills can be rotated across lines of business to address different claim types and complexity levels.
Ongoing QA and sampling
Sample denial files regularly to check for substantive human analysis, complete audit trails, and accurate disclosures. Measure quality, not just completion, and provide targeted coaching where gaps appear.
Monitoring closes the loop. By feeding insights back into training and tool configuration, carriers steadily raise decision quality while maintaining compliance.
Step 8 — Prepare for Regulatory Examinations
Assemble readiness materials for the Office of Insurance Regulation and align practices with anticipated rules from the Financial Services Commission. Organize documentation so it can be produced quickly and without disrupting day-to-day operations.
Effective preparation reduces stress during examinations and shortens review timelines. It also signals a culture of accountability that regulators appreciate.
Readiness packets
Maintain a packet that includes policy manuals, governance artifacts, audit logs, sample denial files, training curricula, and vendor documentation. Keep it current and centrally accessible.
A living packet saves time and avoids last-minute scrambles. It also provides an internal checklist to ensure practices stay aligned with evolving expectations.
Evidence of control effectiveness
Track metrics on reviewer interventions, overrides of automated outputs, corrections of errors, and turnaround times post-implementation. Present trends and remediation steps where needed.
These metrics demonstrate that controls are not just documented but actually working. They also highlight where process tweaks or staffing adjustments can improve outcomes.
Step 9 — Pilot, Iterate, and Go-Live Before July 1, 2026
Run controlled pilots to test the new workflow, identify bottlenecks, and refine staffing plans. Start with high-impact lines or denial types where oversight will matter most and lessons will generalize quickly.
Treat the pilot as a learning engine, not a formality. Iterate on checklists, notice templates, and escalation paths based on real-world friction, and validate that throughput remains acceptable as quality standards rise.
Measure turnaround and quality
Track cycle times, denial accuracy, and appeal rates. Balance speed with depth of review, setting service-level goals that reflect the additional human analysis while protecting customer experience.
These measurements keep teams honest about trade-offs and help explain to leadership where investments in staffing or tooling will produce the biggest gains.
Stage the rollout
Expand in waves, prioritizing high-volume lines and complex denials. Use each wave to stress-test training, adjust workloads, and verify that audit trails remain complete under pressure.
A staged rollout lowers risk and supports steady adoption. By the time the effective date arrives, the process should feel routine rather than new.
Quick reference: the compliance checklist
Compliance hinges on nine pillars: visibility into covered tools, clear assignment of qualified decision-makers, independent analysis before any denial, robust audit trails, upgraded denial notices, revised claims manuals and model governance, thorough training and monitoring, proactive regulatory readiness, and a disciplined pilot-to-go-live plan. Each element reinforces the others, creating a system where human judgment is both present and provable.
Think of the checklist as a compact version of the playbook. Inventory AI/ML/algorithmic tools and map decision points; assign humans with explicit authority; require analysis that resists rubber-stamping; capture who, what, when, and why alongside the automated inputs; name the decision-maker on every notice with required affirmations; document tool purpose, limits, and controls in the manuals; train staff, sample files, and track performance; prepare examination evidence for OIR and potential FSC rules; and pilot early, refine quickly, and complete implementation before July 1, 2026. The goal is consistent: human oversight that is visible, verifiable, and durable.
What this means for insurtech, consumers, and regulators
For insurers and vendors, HB 527 signals higher demand for explainable tools, robust logging, and human-centered workflow features. Black-box models without auditability will face headwinds, while decision-support systems that surface evidence, expose confidence levels, and simplify human rationale entry will gain traction. Vendors that help carriers prove independence—not just speed—will be well placed.
Consumers can expect clearer denials with named reviewers, accessible explanations, and a fairer path for appeals. Increased transparency should raise trust, and better documentation should reduce disputes that stem from misunderstandings or hidden logic. In practice, that means more consistent experiences across carriers and fewer surprises when questions arise about how a conclusion was reached.
Regulators may see HB 527 become a template for other states, encouraging harmonization around human oversight and model governance. For national carriers, that trend raises cross-state compliance questions, but it also offers a path toward standard practices that satisfy multiple jurisdictions. In the near term, expect growth in documentation automation, override analytics, and training support, along with challenges in staffing, coaching, and managing review times.
Closing the loop: key takeaways and next steps
HB 527 does not ban automation; it ensures that a qualified human makes and owns every denial decision, supported by transparent records and explainable tools. The most successful carriers treat this not as a constraint but as a quality upgrade that improves fairness, strengthens defenses in examinations and disputes, and elevates the customer experience through clearer communication.
The practical path forward is already visible. Map tools and workflows, embed independent human analysis, modernize notices and manuals, train for judgment, monitor for substance, and pilot early to hit the July 1, 2026 deadline with confidence. Treated as a differentiator rather than a hurdle, strong oversight, clean documentation, and fair decisions become strategic advantages that reduce friction, build trust, and reinforce brand reputation in one of the nation’s most demanding insurance markets.
