It is no secret that insurance companies are looking for new and creative ways to deny claims. Doing so saves on their own bottom line, often to the detriment of the insured. But are insurance companies now using AI to deny claims even faster? You bet they are. AI is shifting the landscape across every industry, and insurance claim analysis is no different. Often called “decision-support” tools, too often companies use AI to do the decision-making for them.
If insurance companies are using AI to deny claims faster, what does this mean for consumers and the attorneys who represent them? Here, we take a look at what insureds and claimant attorneys are seeing and how it may impact their lives.
The Rise of AI in Insurance Claims
AI adoption in the insurance sector is not theoretical. It is happening at scale. Carriers are deploying machine-learning models to:
- Predict claim severity
- Flag claims for “special investigation”
- Estimate medical treatment duration
- Evaluate medical necessity
- Score claimant credibility
- Recommend settlement ranges
- Identify “high-risk” claimants
- Automate denial letters
Insurance companies use these tools by integrating them into their claims management platforms. Used by adjuster’s, these programs now give AI recommendations, risk scores, and even suggested actions. Often, the adjuster is trained to follow these recommendations unless they have a very good reason to deviate from it.
These tools are often integrated into claims management platforms used by adjusters, meaning the adjuster’s screen may show AI-generated recommendations, risk scores, or suggested actions. In many cases, the adjuster is expected to follow these recommendations unless they can justify a deviation.
Claimant attorneys report that this shift has created a new dynamic: claims are being denied or under-valued earlier, faster, and with less human review.
What Claimant Attorneys Are Seeing in Real Cases
Claimant attorneys are already seeing the impacts in real life. These include:
1. Faster Initial Denials
Attorneys report that first-round denials, especially in disability, health insurance, and auto injury claims, are arriving more quickly than before. In some cases, denials are issued within hours of claim submission.
This speed suggests that automated systems are performing initial triage and generating denial decisions without meaningful human evaluation. Claimants who previously might have received requests for additional documentation are instead receiving immediate “insufficient evidence” or “not medically necessary” determinations.
2. Denials Based on Algorithmic “Risk Scores”
Several attorneys have encountered denial letters referencing internal scoring systems. These scores may relate to:
- Claim complexity
- Expected cost
- Likelihood of litigation
- Claimant “behavioral indicators”
- Medical treatment patterns
While carriers rarely disclose how these scores are calculated, attorneys suspect they are derived from machine-learning models trained on historical claims data. The concern is that these models may embed bias, penalize certain claimant profiles, or misinterpret legitimate medical treatment as “excessive.”
3. Automated Medical Necessity Determinations
In health insurance and auto injury claims, attorneys increasingly see denials that appear to rely on algorithmic assessments of medical necessity. These determinations often contradict treating physicians’ recommendations and rely on generalized treatment timelines rather than individualized medical facts.
For example, a claimant may be denied coverage for physical therapy because the AI model predicts that “most patients” recover within a shorter period. Attorneys argue that this approach ignores the claimant’s actual medical condition and substitutes population-level predictions for individualized care.
4. Increased Use of “Special Investigation Unit” Flags
AI systems are frequently used to flag claims for potential fraud or exaggeration. Claimant attorneys report that SIU referrals are increasing, even for straightforward claims with clear documentation.
These flags can delay claims, trigger intrusive investigations, or justify denials based on “inconsistencies” identified by the algorithm. These inconsistencies may not be meaningful or accurate.
5. Settlement Offers That Mirror Algorithmic Recommendations
In personal injury cases, attorneys have noticed settlement offers that appear to follow rigid patterns. Offers may be:
- Lower than historical averages
- Issued earlier in the process
- Resistant to negotiation
- Justified using vague references to “internal evaluation tools”
This suggests that AI-driven valuation models are influencing negotiation strategy, potentially reducing adjuster discretion and limiting the ability to consider unique case factors.
Why AI-Driven Claim Denials Are Concerning
AI systems can provide more efficiency for the insurer, but this really only amplifies the existing problems with insurance claims handling.
1. Lack of Transparency
Carriers rarely disclose how their AI systems work. Claimants and attorneys cannot see:
- What data the model used
- How the model weighed evidence
- Whether the model is biased
- Whether the adjuster relied on the model
- Whether the denial was automated
2. Potential for Bias
AI models are only as good as the data they are trained on. They have a habit of replicating:
- Past patterns of underpayment
- Denial of claims
- Discriminatory outcomes
Certain demographics have historically received lower payouts, and the AI internalizes those disparities. It then parrots them with devastating consequences for claimants.
Attorneys worry that claimants with certain medical histories, occupations, or socioeconomic backgrounds may be disproportionately flagged as “high-risk” or “non-credible.”
3. Over-Reliance on Predictive Models
Predictive analytics are not medical evaluations. They cannot assess pain, functional limitations, or individual recovery trajectories. Many attorneys are reporting that adjusters simply defer to the AI recommendations, and often ignore the actual evidence provided by the claimant and their medical provider.
4. Reduced Human Review
When adjusters rely heavily on AI recommendations, claimants may lose the benefit of individualized evaluation. Complex cases that require nuance may be oversimplified by models designed for efficiency rather than fairness.
5. Faster Denials Increase Claimant Burden
Quick denials force claimants to:
- Gather additional documentation
- File appeals
- Seek legal representation
- Navigate complex administrative processes
How AI Systems Influence Adjuster Behavior
Even when AI does not directly issue denials, it changes how the adjuster does their job. Attorneys report several behavioral shifts:
Adjusters Follow AI Recommendations
Many adjusters are evaluated based on adherence to internal guidelines. If the AI system recommends denial or a low settlement range, adjusters may feel pressured to comply.
Adjusters Cite “System Limitations”
Attorneys increasingly hear adjusters say they “cannot deviate from system recommendations” or that “the system will not allow” certain actions. This suggests that AI tools may be embedded in workflow systems that restrict adjuster discretion.
Adjusters Provide Less Detailed Explanations
Denial letters and settlement justifications often contain generic language, suggesting that automated templates are being used. This reduces the ability to understand the true basis for the decision.
Legal and Regulatory Concerns
AI-driven claim denials raise several legal issues that claimant attorneys should monitor.
1. Compliance With State Insurance Regulations
Many states require:
- Reasonable investigation
- Fair claims handling
- Clear explanation of denial reasons
- Consideration of all submitted evidence
If AI systems shortcut these requirements, carriers may be vulnerable to regulatory scrutiny.
2. Potential Violations of Bad Faith Standards
Bad faith claims may arise when:
- Denials are issued without proper investigation
- AI models override medical evidence
- Claimants are denied based on flawed algorithms
- Carriers fail to disclose reliance on automated systems
Attorneys should evaluate whether AI-driven decisions meet the standards of good-faith claims handling.
3. Privacy and Data Use Concerns
AI systems often rely on extensive data, including:
- Medical records
- Social media activity
- Consumer behavior data
- Geolocation information
- Historical claims patterns
Claimants may not be aware that their data is being used in this way, raising privacy and consent issues.
4. Algorithmic Accountability
If a denial is based on an algorithm, who is responsible?
- The carrier?
- The software vendor?
- The adjuster who followed the recommendation?
This question is likely to become central in future litigation.
What Claimant Attorneys Can Do
Attorneys representing claimants can take several steps to address AI-driven denials.
Request Detailed Denial Explanations
Attorneys should ask carriers to specify:
- Whether AI tools were used
- What evidence the system relied on
- Whether adjusters reviewed the claim independently
Even if carriers refuse, the request itself creates a record.
Challenge Generic Denial Language
If denial letters contain vague or boilerplate language, attorneys can argue that the carrier failed to conduct a reasonable investigation.
Demand Human Review
Attorneys can insist that a qualified human adjuster review all evidence and provide a detailed explanation of the decision.
Use Discovery Strategically
In litigation, attorneys may seek:
- Internal guidelines
- AI model documentation
- Training materials
- Adjuster workflows
- Communications about AI recommendations
This can reveal whether the carrier relied on flawed or biased systems.
Educate Clients
Claimants should understand that:
- Fast denials may be automated
- Appeals are often necessary
- Documentation is critical
- Legal representation can help counter algorithmic bias
The Future: AI Will Continue to Shape Claims and Legal Strategy
AI adoption in insurance is accelerating. Carriers view these tools as essential for reducing costs and increasing efficiency. Claimant attorneys should expect:
- More automated triage
- More algorithmic valuation models
- More SIU referrals
- More reliance on predictive analytics
- More pressure on adjusters to follow system recommendations
This means attorneys must adapt their strategies, understand how AI influences claim outcomes, and be prepared to challenge decisions that appear to rely on flawed systems.
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