By Melissa Rageth, AIC-M, AIM, AINS, CISR, CLP-A, CIIP, TRIP, DAE, CRIS, MRIS
2025-2027- Regional Vice President- Region V
Artificial intelligence is transforming the insurance industry. Insurers are using AI to process claims, detect fraud, estimate damage, evaluate risk, analyze documents, and improve customer service. These tools can make insurance operations faster and more efficient.
At the same time, the growing use of AI may be influencing the increase in litigated claims. In some situations, AI can help prevent disputes by improving accuracy and consistency. In others, automated decisions, limited transparency, data concerns, and errors may create new reasons for policyholders, claimants, regulators, or attorneys to challenge an insurance decision.
The relationship between AI and litigation is complex. AI is not necessarily causing every increase in litigated claims, but its use is changing how claims are evaluated, disputed, documented, and defended.
The Expanding Role of AI in Claims
AI is now used in many areas of the claims process, including:
- Reviewing policy language and claim documents
- Estimating vehicle and property damage
- Identifying potential fraud
- Prioritizing claims for adjuster review
- Predicting claim severity and litigation risk
- Summarizing medical or legal records
- Automating correspondence and payment processes
- Evaluating images from inspections or accident scenes
- Supporting reserve recommendations
- Routing claims to specialized teams
These applications can reduce administrative work, and help claims professionals manage large volumes of information. However, when AI influences a coverage, liability, valuation, or settlement decision, the process may receive greater legal scrutiny.
How AI May Contribute to More Litigated Claims
1. Lack of Transparency
One of the most significant concerns is that some AI systems are difficult to explain. If an algorithm produces a recommendation or classification, policyholders may want to know how that result was reached.
For example, a claimant may question why a claim was flagged for investigation, why a repair estimate was reduced, or why a settlement offer differs from the claimant’s expectations. If the insurer cannot clearly explain the relevant factors, the disagreement may escalate.
Courts, regulators, and attorneys generally expect insurers to demonstrate that claim decisions were based on appropriate facts, policy terms, and applicable requirements. An unexplained automated output may not be sufficient on its own.
2. Incorrect or Incomplete Data
AI systems depend on the information used to train and operate them. If the underlying data is incomplete, outdated, inaccurate, or not representative of a particular claim, the system may produce an unreliable result.
Potential data problems include:
- Incorrect policy information
- Missing medical records
- Incomplete loss histories
- Outdated repair pricing
- Poor-quality photographs
- Incorrect geographic assumptions
- Inaccurate claimant or vehicle information
- Data that does not reflect unusual or complex losses
A small data issue can have a significant effect if it influences claim prioritization, damage estimates, fraud indicators, or settlement recommendations.
3. Automated Valuation Disputes
AI-powered estimating tools can analyze photographs, videos, vehicle information, construction data, and market pricing to estimate the value of a loss. These tools may improve consistency, but they cannot always identify every aspect of damage.
A photograph may not show hidden structural damage. An image-based model may misidentify a material, overlook a code requirement, or fail to account for regional labor costs. In a complex property claim, a standard estimate may not reflect the actual cost of restoring the property.
When policyholders believe an AI-generated estimate is too low, they may seek legal representation rather than continue negotiating through the ordinary claims process.
4. Fraud Detection and False Positives
AI can help identify suspicious claim patterns more quickly than manual review. However, no fraud detection system is perfect. An algorithm may flag a legitimate claim because it resembles other claims associated with fraud indicators.
Being flagged for investigation can be frustrating for a policyholder or claimant, particularly if the insurer does not explain the reason for additional scrutiny. If the investigation causes delays or creates the perception that the claimant is being treated unfairly, the matter may become a legal dispute.
Fraud analytics should therefore be treated as an investigative resource, not a final determination that a claim is fraudulent.
5. Inconsistent Treatment of Similar Claims
AI is often introduced to promote consistency. Yet different systems, vendors, data sources, or model updates may produce different outcomes for similar claims.
Inconsistency can create questions such as:
- Why did two similar claims receive different estimates?
- Why was one claim approved while another was delayed?
- Why did one claimant receive a different settlement evaluation?
- Did the system treat certain types of claimants differently?
If an insurer cannot explain these differences, attorneys may use them to challenge the fairness or reasonableness of the claims process.
6. Concerns About Bias and Discrimination
AI systems may reproduce or amplify patterns present in historical data. If the data reflects past inconsistencies or unequal treatment, the system may generate outcomes that disproportionately affect certain groups, locations, occupations, or types of claims.
Potentially discriminatory outcomes can create serious regulatory and litigation exposure. The risk may exist even when protected characteristics are not directly included in the model, because other variables—such as geography, income-related indicators, language, or prior claim history—may act as proxies.
Insurers must evaluate whether AI systems produce fair results and whether their use complies with applicable laws and regulatory expectations.
7. Automated Communications That Escalate Frustration
AI-generated emails, text messages, chat responses, and claim updates can improve speed. However, automated communication may appear impersonal or fail to address the specific concerns of a claimant.
A generic response may be particularly problematic when:
- Coverage is denied
- A claim is delayed
- A settlement offer is disputed
- The claimant has suffered a serious injury
- The policyholder is facing displacement or business interruption
- The claimant has already provided the requested information
When customers feel that no one is listening, they may turn to an attorney, regulator, or public complaint process.
8. AI-Generated Legal and Demand Materials
AI is also increasingly available to policyholders, attorneys, and claims representatives. Claimants and law firms can use AI to prepare demand letters, organize medical records, analyze policy language, draft complaints, and identify potentially relevant legal arguments.
This may make it easier to pursue claims that previously would not have been litigated. AI can lower the cost of preparing legal materials and allow attorneys to manage larger numbers of cases.
On the insurer side, defense counsel can use AI for document review, legal research, and litigation strategy. As both sides become more efficient, litigation may become less expensive to initiate and more scalable.
AI Can Also Reduce Litigation
Although AI may contribute to disputes, it can also help prevent litigation when used appropriately.
Faster Claim Resolution
AI can automate routine tasks and help identify missing information quickly. Faster processing can reduce frustration and prevent minor disputes from becoming formal legal matters.
Better Documentation
AI tools can summarize claim activity, organize records, and identify inconsistencies in file documentation. A clear and complete claim file can help support the insurer’s position and make settlement discussions more productive.
Improved Damage Analysis
When combined with adjuster expertise, image recognition and estimating technology may identify damage more accurately and reduce disagreements over scope.
Earlier Identification of Complex Claims
Predictive analytics can help identify claims requiring specialized attention. Early involvement by experienced adjusters, engineers, medical professionals, or attorneys may improve outcomes and reduce avoidable escalation.
More Consistent Decision Support
AI can help claims professionals apply guidelines consistently and identify relevant policy provisions. Used as a support tool, it may reduce unintentional inconsistencies in claim handling.
The Importance of Human Oversight
The most effective approach is not to replace claims professionals with AI, but to use AI to support their work. Human oversight remains essential, especially when a decision involves:
- Coverage interpretation
- Serious bodily injury
- Complex liability
- Total or partial claim denial
- Significant financial impact
- Potential fraud
- Vulnerable policyholders
- Regulatory or litigation risk
Claims professionals should review AI recommendations, consider information the system may have missed, and document the reasoning behind the final decision.
An AI output should be treated as one source of information—not as an unquestionable conclusion.
Building a Defensible AI-Assisted Claims Process
Insurers can reduce litigation risk by establishing strong controls around AI use.
Explain Decisions Clearly
Customers should receive understandable explanations of important claim decisions. The explanation should identify the relevant policy provisions, facts, estimates, and factors—not simply state that an automated system made the determination.
Maintain Complete Records
Insurers should retain information about:
- The data used in the decision
- The version of the model or system
- The AI output
- Human review and modifications
- Communications with the claimant
- The final rationale for the decision
Detailed documentation can be critical if a claim is later challenged.
Test for Accuracy and Fairness
AI models should be tested before deployment and monitored afterward. Testing should evaluate accuracy, consistency, bias, performance across different claim types, and the effect of model updates.
Establish Escalation Procedures
Policyholders should have a meaningful way to request human review. Escalation processes should be clear, accessible, and appropriate for the severity and complexity of the claim.
Train Employees
Adjusters and claims leaders need to understand both the capabilities and limitations of AI. Training should cover how to interpret AI recommendations, recognize potential errors, explain automated processes, and avoid relying on technology without adequate review.
Manage Vendors Carefully
Many insurers use third-party platforms for estimating fraud detection, document processing, and analytics. Vendor contracts should address data security, system performance, explainability, audit rights, regulatory compliance, and responsibility for errors.
What Policyholders Should Know
Policyholders should not assume that an AI-assisted decision is automatically correct or final. If they disagree with a claim outcome, they can request an explanation and ask how the decision was reached.
Helpful steps may include:
- Reviewing the relevant policy language
- Requesting a detailed explanation of the decision
- Providing additional documentation or photographs
- Obtaining an independent repair or professional estimate
- Asking for human review
- Keeping records of all communications
- Seeking professional advice when appropriate
Prompt, organized communication can help resolve many disputes before litigation becomes necessary.
Looking Ahead
AI will continue to influence the insurance industry and the claims process. As the technology becomes more sophisticated, insurers may process claims more quickly and identify patterns that would be difficult to detect manually.
However, greater use of AI will also create greater expectations for transparency, fairness, security, and accountability. Courts and regulators may increasingly examine how automated tools affect coverage decisions, claim payments, fraud investigations, pricing, and settlement practices.
The central challenge for insurers will be finding the right balance between efficiency and judgment. A claims process that is fast but impossible to understand may create more disputes. A process that combines technology with clear explanations, reliable data, and meaningful human oversight is more likely to build trust.
Conclusion
Artificial intelligence is influencing the increase in litigated claims by changing how insurance decisions are made and challenged. Automated estimates, fraud flags, predictive models, and digital communications can improve efficiency, but errors, bias, limited transparency, and poor communication may also create new sources of conflict.
AI itself is not the only factor behind rising litigation. Increasing claim severity, medical and repair costs, complex coverage issues, legal advertising, and social inflation also play important roles. Nevertheless, the way insurers implement AI can either reduce disputes or contribute to them.
The best path forward is responsible adoption. Insurers should use AI to support—not replace—professional judgment, maintain strong documentation, provide clear explanations, monitor systems for bias and error, and ensure that customers have access to meaningful human review. When technology and human expertise work together, insurers can improve claims outcomes while reducing unnecessary litigation.