Why AI Governance Is Essential for Insurance Claims Organizations

As AI use grows across P&C carriers, TPAs, and claims operations, so do concerns about fairness, accuracy, and accountability. Clear AI governance, strong compliance practices, and keeping claims professionals involved in high-impact decisions are no longer optional.

Artificial Intelligence (AI) is changing how insurance claims work, from processing medical records to spotting fraud and handling sensitive claimant data. For P&C carriers, TPAs, and the claims professionals who work within them, AI has become a daily part of doing business. 76% of US insurers are already using generative AI, but only 45% feel confident the benefits outweigh the risks. As AI use grows, so do concerns about fairness, accuracy, and accountability.

With new laws on the rise and clients expecting more oversight, claims organizations need to be thoughtful about how AI is used. Clear governance, strong compliance practices, and keeping people involved in high-impact decisions are no longer optional. These are now the basics for building systems that are powerful and trustworthy.

AI governance means putting the right rules and processes in place to guide how AI is built, used, and overseen. It covers everything from how systems are trained to how outcomes are reviewed and who takes responsibility. The goal is to make sure AI stays fair, safe, and grounded in real-world needs.

For claims teams, this matters more than ever. In 2024, US federal agencies introduced 59 AI-related regulations, more than double the year before and coming from twice as many institutions. Strong governance helps claims organizations navigate shifting rules, reduce bias, and earn confidence with clients, partners, and regulators.

AI regulations are advancing quickly, and for organizations working across regions, that brings new challenges. In the US, a 2025 executive order moved away from federal oversight to promote innovation. This change left many firms navigating a growing mix of state-level laws without a clear national standard.

At the same time, the US government is raising expectations. In April, the Office of Management and Budget called on agencies to strengthen AI governance and ensure people stay engaged in decisions that affect public services. Many states followed by proposing laws focused on transparency, accountability, and safety in advanced AI. The NAIC (National Association of Insurance Commissioners) has also been actively developing model bulletins on the use of AI in insurance, adding another layer of oversight specific to carriers and claims operations. For insurers and claims teams, this points to a future where responsible AI use is expected, not optional. Strong internal standards are becoming a key way to align with policy and build long-term trust.

In 2026, the NAIC launched a nine-state pilot of its AI System Evaluation Tool—running January through September 2026—to assess how insurers govern their AI systems and whether those practices effectively manage risk across property/casualty, life, and health lines. Strong governance helps claims organizations navigate shifting rules, reduce bias, and earn confidence with clients, partners, and regulators.

As AI capabilities expand across claims workflows, the question isn’t whether to adopt it—but how to do so responsibly. In high-stakes industries such as claims and legal, technology should support people, not replace them. A human-in-the-loop (HITL) approach adds a layer of human expert review that supports validating AI’s decisions, by catching errors, flagging bias, and making sure actions can be explained and adjusted when needed.

This is most important in high-stakes areas like underwriting, claims, and health data. Wisedocs’ own survey report in partnership with ALM Property & Casualty 360 surveyed claims professionals on this very matter and found that HITL was a 4x trust multiplier when expert human oversight is added to AI outputs. The insights are clear: HITL gives organizations a practical way to maintain compliance, protect users, and reinforce reliability in how their AI systems work.

Looking ahead, the rules around AI are only going to continue to develop. Keeping up with new laws, having clear processes in place, and making sure people stay part of important actions can help your team avoid problems and strengthen credibility along the way. For claims organizations specifically, that means building AI governance frameworks that satisfy both regulators and claimants—ensuring that AI-generated outputs are auditable, explainable, and backed by human review. These choices shape how your AI performs, how it’s understood, and how your organization shows up in this changing landscape.

Wisedocs governs AI through two complementary documents: a formal Artificial Intelligence Policy establishing company-wide principles, and a detailed AI and ML Governance Procedure that applies those principles across every stage of product development.

Six Guiding Principles

The Wisedocs AI Policy is built around six principles that apply across all AI systems: Transparency (clearly explaining when and how AI is used, including training methodologies); Reliability (ongoing assessment for accuracy, robustness, and remediation of errors); Accountability (trained staff oversight and compliance with applicable laws and standards); Privacy and Data Security (SOC II Type 2 and HIPAA compliance, secure AWS infrastructure, and regular security audits); Ethics and Fairness (deploying AI to minimize bias and promote inclusion); and Continuous Review (regular policy review for ongoing relevance and effectiveness).

A Four-Stage Development Framework

Every AI feature follows a structured lifecycle: in design, cross-functional PRDs incorporate customer feedback and domain expert validation from medical, legal, and insurance professionals, versioned and reviewed before development is approved; in development, SDLC best practices combine with ML-specific success metrics (Accuracy, Precision, Recall, ROUGE scores) and subject matter expert validation; in testing, features move through unit, integration, and UAT environments with iterative expert-in-the-loop prompt validation; and in deployment and monitoring, a gradual rollout process, a versioned prompt store with bi-weekly update cycles, and continuous human-in-the-loop monitoring for model drift ensure ongoing quality post-release.

Balancing Innovation Speed with Compliance

In a recent Insurance Thought Leadership webinar on AI Governance in Insurance, Denys Linkov, Wisedocs’ Head of Machine Learning, was asked directly how the company balances innovation speed with compliance demands. He pointed to the governance document as the answer:

“Whenever we release a feature, we have our AI governance document that we share with our carriers—how we follow good software development life cycle practices, AI development life cycle practices. We have a number of experts on hand who are validating our prompts, our responses, our process overall through…We have all these different gates and steps that we check going from development to pre-production to production environment. We do rollouts with customers who have a little more risk tolerance first, get their feedback as part of a beta program… The goal isn’t just to see something shiny and updated. There’s a thorough process and it all goes back to business outcomes… Sometimes you can move quickly, sometimes you can’t. But it’s very deliberate.”

Security and Compliance Infrastructure

Wisedocs’ security posture is built for the demands of insurance and healthcare: SOC II Type 2 certified and fully HIPAA compliant, with Business Associate Agreements (BAAs) available; all data stored and processed on U.S.-based AWS infrastructure with advanced encryption; multi-factor authentication, advanced password policies, and admin audit logs enforced across all accounts; and AI model deployment using anonymized and aggregated training data with human oversight embedded throughout AI processes.

What to Ask Any AI Vendor in Insurance Claims

For P&C carriers, TPAs, and claims teams evaluating AI partners, governance questions matter as much as capability: Does the vendor have a documented AI governance procedure and a published AI policy? Are they SOC II Type 2 and HIPAA certified? Is human expert validation built into the development process—or only applied at the output level? Wisedocs makes both its AI Policy and its AI and ML Governance Procedure available to carrier partners as standard practice. As Denys put it: “Light is the best disinfectant. We encourage all vendors and technology partners to be transparent, because it’s very hard to execute on a plan if things aren’t transparent.” AI governance in insurance claims is not a compliance checkbox. For Wisedocs, it is the operational foundation that makes responsible, scalable AI deployment possible.

For more on how leading claims organizations are navigating AI governance and employing responsible use through human-reviewed claims decision intelligence, visit Wisedocs at wisedocs.ai.

Author: Joe Kevens, Head of Marketing, Wisedocs

Bio: Joe Kevens leads Marketing at Wisedocs. With 10+ years of experience in B2B tech, he’s helped grow a number of go-to-market B2B SaaS companies such as PartnerStack, Influitive, LevelJump, and Eloqua. When he’s not working on growth marketing for Wisedocs, he’s either spending quality time with his girl, Clara, or he’s exploring the online world of business software reviews on B2B SaaS Reviews.