David Tobias, Chief Product Officer at Nearmap, is a 20-year insurance veteran with expertise in comprehensive products and solutions.
As regulators evaluate their oversight and consumers seek greater accountability, the traditional underwriting playbook is becoming less effective.
Recent regulatory developments across states like California and Colorado paint a clear picture: Insurers are being asked to demonstrate more than just compliance. Because of this, transparency in underwriting practices is now vital, but it presents both challenges and opportunities for carriers.
The Regulatory Reality
As mentioned, regulatory scrutiny of insurance underwriting practices has intensified, particularly with the use of aerial imagery and AI. Why the escalation? Experts, including myself, largely attribute it to the way climate-related losses are creating affordability crises, forcing regulators to balance consumer protection with insurer solvency. Simultaneously, the rapid adoption of AI has outpaced regulatory frameworks, creating uncertainty about fairness and accountability.
California’s proposed legislation requiring disclosure of aerial imagery use exemplifies this shift. It mandates that insurers notify policyholders about potential image capture and provide instructions for obtaining copies. This represents just one move toward proactive transparency.
Navigating Multi-State Requirements
The National Association of Insurance Commissioners (NAIC) is increasingly focused on algorithmic accountability, working to establish standardized frameworks for AI explainability and bias detection.
State-level initiatives add another layer of complexity. For example, Florida is employing enhanced data reporting for catastrophe models while Texas is demanding stricter oversight of non-renewal practices. In Colorado, there is now legislation requiring a detailed justification for significant rate changes.
Insurers operating across multiple states must navigate these varied requirements while maintaining consistent underwriting standards.
From Assumptions To Evidence
The shift toward transparency parallels a reimagining of risk assessment. Traditional approaches relied on broad geographic categories, often leading to unfair cross-subsidization among policyholders.
Modern property intelligence helps offer a more precise path forward. High-resolution aerial imagery combined with AI-powered analysis can allow insurers to assess individual property risks with unmatched clarity. This supports more accurate pricing and provides defensible evidence for regulatory review.
Consider two neighboring homes: one with a pristine roof and another needing repairs. Traditional underwriting might price them similarly. Property intelligence helps reveal the true risk differential, ensuring appropriate pricing for each and providing transparency about what drives rate differences. This allows properties with well-maintained features and defensive landscaping to receive appropriate rate reductions.
An Insurance Research Council survey reveals that 90% of homeowners see benefits in using aerial imagery, including early problem detection and faster claims processing. Over half believe this technology leads to fairer pricing when implemented correctly.
From Climate Risk To Climate Resilience
Property intelligence can also help offer a powerful alternative to blanket market exits in high-risk areas. Instead of withdrawing from entire regions, insurers can use detailed data to identify individual properties with risk-mitigating features, like defensible space or fire-resistant materials.
This approach allows insurers to maintain coverage in previously “uninsurable” areas while demonstrating to regulators that risk selection is based on objective property characteristics, not broad geographic exclusions.
Building Explainable AI Systems
True explainability in AI-driven underwriting extends beyond documenting algorithms. Regulators expect insurers to demonstrate not just what decisions were made, but why they are appropriate, fair and defensible.
I find that the most effective approach combines multiple layers of transparency:
• Technical Documentation: This documentation includes details on AI training data, decision trees and validation.
• Decision Traceability: Systems should link underwriting decisions to specific imagery and data points.
• Ongoing Monitoring: Create processes for continuous bias detection and performance evaluation.
Leading carriers are investing in explainable AI platforms that generate reports showing exactly which property characteristics influenced decisions, complete with supporting imagery.
Finding Confidence In Independent Validation
Third-party validation by respected actuarial firms can help with regulatory acceptance, providing an objective evaluation of AI model performance, bias detection and decision fairness. In fact, some insurers now proactively share these validation results with regulators during rate filings. This can expedite approvals and help seamlessly align with state regulations.
A Foundation For Transparent Underwriting
But the integrity of these AI-driven underwriting models is only as strong as the data that informs them. And every AI-generated insight should be verifiable against the actual conditions of a property.
When regulators question a decision or consumers seek an explanation, the source data must provide clear, defensible evidence. To achieve this, insurers can implement several key strategies, especially when choosing a third-party partner:
• Prioritize high-resolution data. Insist on high-resolution imagery that allows for detailed and accurate assessment of property features and risks. The level of detail should be sufficient to validate any automated decision.
• Verify data freshness. The value of property intelligence diminishes over time. Partner with providers who guarantee frequent updates, ensuring that underwriting decisions are based on the most current property conditions available.
• Demand source transparency. Ensure that any data provider can offer access to the original source data behind their analytics. This traceability is crucial for auditing, defending decisions, and building a foundation of trust.
Ultimately, it is not just about having data. It’s about having data that can withstand scrutiny. By demanding these standards, insurers can build consumer trust, satisfy regulatory requirements and support the long-term sustainability of insurance markets.
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