Florida insurers lean into AI as oversight rules advance
Florida's property market is crowded again, and the smartest carriers are winning on speed and clarity. One example: a St. Petersburg startup growing its book with AI-supported underwriting while pledging human control over key decisions.
The timing matters. Lawmakers are weighing rules that would require a person-not a model-to make final calls, especially on denials. That's the line the market will be asked to hold.
Market context: growth, Citizens depop, and appetite
Since the 2022-2023 reforms, 17 new carriers have entered the state. Patriot Select is one of them, leaning on Citizens depop to scale quickly.
They projected about 25,000 policies in their first year and report roughly 26,000 today. The next stage of growth is competing in the open market for switchers and shoppers.
Where AI fits in the workflow
The company applies AI early in the intake process to sort opportunities by fit and condition. The goal: say "no" fast with a clear reason, and say "yes" with the data to bind confidently.
Data comes from public sources-geospatial roof imagery, and interior photos found on real estate sites like Redfin and Zillow. That supports faster prefill, cleaner risk signals, and fewer back-and-forths with agents and homeowners.
On the underwriting desk, an in-house roof condition analyzer ingests wind mitigation reports and images. It scores roofs with a simple color system: green for good, blue to monitor, and orange/pink for issues needing attention. It's built for the gray area roofs that usually eat up time.
What this means for underwriting teams
- Quote speed: triage submissions by appetite fit and condition before a human touches the file.
- Consistency: standardize how "borderline" features (age, shingle type, visible wear) are treated.
- Data hygiene: pull imagery and public records once, reuse across rating, underwriting, and inspection.
- Agent trust: clear reasons for declines and tighter documentation when you bind.
Guardrails: Florida's push for human oversight
New bills in the House and Senate focus on AI's role in insurance decisions. The headline: no claim should be denied based solely on an automated output, and a human must make the final call.
The House bills moved through committees; the Senate has yet to advance. Meanwhile, a KPMG survey of 110 insurance CEOs indicates AI sits near the top of investment priorities-so adoption will continue, with scrutiny.
Patriot Select's stance is clear: AI helps with intake and analysis, but humans finalize decisions, and AI isn't used to decide claims. The tradeoff is simple-operational efficiency without gambling on regulatory or reputational hits.
Practical checklist to adopt AI responsibly
- Define the decision boundary: AI suggests; licensed staff decide. Document who owns the final call.
- Adverse action playbook: when AI informs a decline or restriction, capture the specific reason and disclose it in plain language.
- Data rights and provenance: verify licenses for aerial/MLS images; log the source and timestamp of each asset.
- Model governance: keep version control, training data summaries, known limitations, and performance metrics.
- Bias and fairness testing: measure disparate impact by geography, construction type, and socio-economic proxies. Remediate and re-test.
- Human-in-the-loop: route "borderline" scores to senior underwriters; require dual sign-off for denials.
- Appeals process: make it easy for consumers and agents to challenge AI-informed assessments with new evidence.
- Claims firewall: if you use AI in claims triage, confine it to assistive roles-never as the final decision-maker.
- Audit trails: store inputs, intermediate outputs, and user actions. You'll need them for regulators and internal QA.
- Third-party vendor diligence: contractually require transparency, performance SLAs, security, and data deletion terms.
- Security: lock down imagery and consumer data; encrypt in transit and at rest; limit access by role.
- Retraining cadence: set a schedule for model refresh and monitoring drift-especially after major CAT events.
- Agent enablement: provide clear appetite guides and fast "reason codes" to reduce friction and resubmissions.
Operational patterns that work
- Pre-bind image review: AI flags suspect roofs; humans confirm with a quick checklist.
- Two-pass underwriting: AI for intake scoring; human for rate/eligibility, endorsements, and exceptions.
- Post-bind QC: sample review of AI-assisted approvals to catch drift and confirm loss performance.
What to watch next
- Final bill language on human oversight and adverse action notice requirements.
- Guidance from the Florida Office of Insurance Regulation on acceptable AI use and documentation.
- Citizens depop trends and how AI-driven appetite shifts affect takeout volumes.
- Standardization of property imagery data across vendors for cleaner interoperability.
Level up your team's AI skills
If you're formalizing AI use in underwriting, claims triage, or agency operations, align training with job roles and governance. Start with practical courses and frameworks your team can apply on live workflows.
See AI courses by job role for structured paths that pair well with the governance checklist above.
Bottom line
AI can help carriers quote faster and explain decisions with more clarity. Florida is signaling a simple rule: humans own outcomes.
If you keep decisions accountable, document the process, and treat AI as assistive-not absolute-you'll move faster without tripping over compliance.
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