GenAI: Moving to the forefront of claims management

Generative AI has moved well beyond the margins of claims management and is now woven into everyday workflows. Claims teams use AI-assisted tools to sort incoming materials, summarize large files and support litigation strategy, while defense counsel uses GenAI to draft outlines, organize chronologies and prepare initial drafts.

The benefits include faster document review, shorter cycle times and more consistent case valuations. But for risk managers overseeing claims operations, the question is no longer whether AI should be used, but how to deploy it without introducing new operational, legal or reputational risks.

Let’s explore how AI is being used across claims handling and defense, where it delivers tangible value, where it still falls short, and what recent cases and regulatory developments mean for companies managing risk.

Where AI adds value in claims operations

When it comes to handling and defending claims, AI is already proving most useful in three core areas: document review, case summarization and litigation support.

Document review

In complex casualty or coverage disputes, claims teams often face hundreds if not thousands of pages of discovery, medical records, prior correspondence and regulatory filings. AI‑powered tools can quickly flag key documents, identify patterns (e.g., inconsistent medical histories or contradictory statements), and prioritize materials that may influence liability or damages. When AI handles some of these basic tasks, it can speed up early review and free up adjusters to focus on higher-level work, such as working with injured parties, negotiating settlements and managing mediation.

AI also helps deal with the challenge of large volumes of paper or scanned files. Tools that can read and organize these documents turn static PDFs into searchable, usable information, making it easier to find key terms, build timelines and connect the dots. That can speed up long-running claims and help keep costs down.

Case summarization and status reports
AI is increasingly used to generate concise case summaries from pleadings, deposition transcripts, expert reports and internal emails. These summaries help underwriters and risk managers quickly assess exposure, identify potential coverage issues and monitor portfolios at scale. In some programs, AI‑assisted summaries are now embedded in internal dashboards, allowing executives to track emerging trends such as spikes in certain jurisdictional issues or recurring causation theories without manually reviewing individual files.

For multistate or multi‑jurisdictional portfolios, this capability is especially valuable. AI‑driven summaries may help identify jurisdiction‑specific patterns, such as rising punitive‑damages demands in certain states or evolving judicial attitudes toward policy interpretation, so that risk managers can adjust underwriting, reinsurance or claims‑handling strategies proactively.

Litigation support and drafting
For defense counsel, generative AI is most often used as a drafting tool. Attorneys and paralegals use AI to outline motions, draft discovery responses and prepare initial settlement demand letters. In some firms, AI helps generate preliminary briefs or organize deposition exhibits and timelines. When used thoughtfully, these tools can reduce the time spent on routine writing and free attorneys to focus on strategy, client counseling and courtroom advocacy.

Some insurers have begun to integrate AI‑driven litigation support into their panel management workflows. For example, AI tools may be used to compare briefs across similar cases and estimate typical settlement ranges based on past results.

The efficiency gains are real, but so are the risks.

The risks: Hallucinations, bias and over‑reliance

The three biggest concerns in AI risk management are hallucinated legal citations, built-in biases and over‑reliance on AI‑generated analysis.

The most visible risk is hallucination. AI systems can produce false citations, inaccurate summaries, or confident-sounding explanations that are simply wrong. A widely cited example is a 2023 federal case from the Southern District of New York (Mata v. Avianca., United States District Court, S.D.N.Y, June 22, 2023), where lawyers relied on ChatGPT-generated case citations that did not exist. This resulted in sanctions and reinforced that responsibility for accuracy remains with counsel, not the tool.

Recent disciplinary actions underscore similar concerns. In March 2024, the U.S. District Court for the Middle District of Florida disciplined an attorney for filings that included fabricated case law (In re Thomas Grant Neusom; United States District Court, Middle District of Florida, March 8, 2024), reinforcing the risks associated with unverified content, including that generated with AI assistance.

Bias presents a subtler but equally important risk. Models trained on historical claims data may reflect past valuation patterns, jurisdictional tendencies or demographic disparities. In areas such as workers’ compensation or disability claims, this could unintentionally skew estimated payouts or settlement outcomes.

Over-reliance compounds these risks. If adjusters treat AI-generated exposure assessments or liability predictions as definitive, they may overlook data gaps or flawed assumptions. This could result in incomplete or error-laden outcomes, regulatory scrutiny or reputational harm.

Professional guidance takes shape

Ethics bodies and courts are beginning to clarify expectations. The American Bar Association’s Formal Opinion 512 treats generative AI as a form of nonlawyer assistance, requiring lawyers to maintain competence, supervise outputs, protect confidentiality, communicate appropriately with clients and ensure fees remain reasonable.

State-level guidance is developing along similar lines. For example, Florida Ethics Opinion 24-1 permits the use of AI tools with appropriate safeguards. Delaware adopted an interim policy in 2024 for judicial officers and court personnel, restricting the use of nonpublic data in unapproved AI tools and emphasizing that decision-making must remain with humans.

For claims organizations, these principles translate into clear governance requirements: vet and approve designated tools, require human validation of outputs and maintain documented oversight processes.

Data security and confidentiality

Claims files often contain medical records, wage information, employment history and other sensitive information. Many AI tools require users to upload material to cloud-based systems. This raises questions about retention, training and unauthorized sharing.

Risk managers should not assume that every vendor handles data the same way. Contracts should address encryption, retention, access controls and whether submitted materials may be used to train the model. Where possible, organizations should require tools that do not store their data or use anonymized or test data for higher-risk situations.

This is especially important in litigated claims, where sensitive records may later be scrutinized in discovery or by regulators. Weak data governance can quickly turn a productivity tool into a liability.

Emerging liability risk

Legislative and regulatory attention to AI risk is increasing. For example, proposed federal legislation such as the AI LEAD Act (S.2937) would subject AI developers to liability like that faced by product manufacturers, including for defective design or failure to warn. While still in the proposal stage, such efforts signal a broader trend toward accountability in AI deployment.

For insurers, third-party administrators and defense firms, this evolving landscape raises important considerations. Organizations that customize or rely heavily on AI tools may face scrutiny over how those tools are used and whether adequate oversight is in place. As a result, protections on the front end, like clear contract terms and the ability to review vendors, along with documenting human oversight on the back end, are becoming increasingly important.

Practical risk controls

The best way to use AI in claims is to treat it like any other high-impact operational system: with written rules, testing and accountability.

First, organizations should adopt clear use policies. Those policies should define what AI may and may not do, require human review of all outputs used in legal or claims decisions, and set training expectations for users.

Second, any AI-generated citation, fact statement or settlement recommendation should be independently verified before relying on it. That safeguard is especially important considering recent disciplinary actions.

Third, vendors should be reviewed as part of a broader governance process. Claims leaders, counsel and technology teams should understand how a tool is trained, what data it uses, how bias is tested and how updates are handled.

Fourth, organizations should monitor performance over time. Periodic audits comparing AI results against human review can reveal whether the system is drifting, reinforcing bias or missing key issues. If errors occur, they should be documented and corrected promptly.

The responsible path forward

Generative AI is here to stay in claims management. For risk professionals, the challenge is not whether to adopt the technology, but how to use it responsibly. Recent cases, ethics guidance and emerging legislation all point toward a future where AI‑assisted work is treated with the same level of scrutiny as any other form of professional judgment.

The most resilient organizations will be those that insist on human oversight at every critical decision point, embed AI governance into broader risk‑management and compliance frameworks and treat AI vendors as partners in risk management, not just cost‑saving tools. For risk managers, the question is no longer whether to use AI. It is whether they can implement it in a way that balances efficiency with accountability and innovation with disciplined risk management.

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