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Insurance has always been a paper-heavy business built around deadlines, judgment calls, and pressure to close files.
Over the last few years, artificial intelligence has moved into the center of that machinery. Algorithms now classify claims, flag risk, suggest settlement ranges, and help decide which cases deserve closer review.
For some policyholders, that shift feels invisible. For others, especially people dealing with injuries, the change feels very real. Decisions may appear faster on the surface, yet resolution drags on.
Requests for more paperwork pile up. Settlement offers look eerily similar from case to case, even when injuries and life impact clearly differ.
The core concern is not abstract. When AI systems are used to triage, score, and value claims, they can quietly tilt outcomes toward slower resolutions and lower payouts.
That risk rises in injury claims, where pain, recovery, and long-term consequences rarely fit into neat categories. Regulators are watching. Courts are involved. Patients, drivers, and clinicians are speaking up.
Highlights
- AI speeds up claim handling on paper but often creates longer delays and more paperwork for injury cases.
- Automated valuation tools tend to compress complex injuries into narrow ranges, pushing settlements lower.
- Low transparency and low appeal rates allow flawed automated decisions to stand unchallenged.
- Regulators are responding, but oversight still lags behind how heavily AI now influences outcomes.
Where AI Shows Up In Claim Review
AI in insurance is not a single tool. Most insurers use a stack of systems that touch multiple points of a claim’s life cycle. Each layer brings its own efficiency promise and its own set of risks.
Intake And Triage
Claims now arrive through apps, portals, call centers, and provider billing systems. AI tools classify the claim, extract details from documents, assign a severity score, and route the file to a specific queue.
When triage works well, simple claims move quickly. When it fails, claims bounce between queues, triggering repeated requests for missing or reformatted information. Every bounce adds time.
Fraud Detection And Suspicion Scoring
Predictive models scan claims for patterns associated with fraud. Extra scrutiny can be justified. Problems arise when models rely on weak proxies or overly broad signals. Legitimate claims then face delays simply because they resemble something the system dislikes.
For an injured claimant, a fraud flag often means slower responses and higher proof demands, even when nothing improper occurred.
Coverage Determination And Utilization Management
In health insurance, AI plays a major role in prior authorization and post-acute care review. Automation can help manage volume, yet investigations and lawsuits have raised concerns that systems effectively decide first, with meaningful human review occurring only after an appeal.
When care decisions hinge on automated screening, delays translate directly into delayed treatment.
Valuation And Settlement Recommendations For Injury Claims
Auto and liability insurers have used evaluation software for years. Modern AI pushes that further by generating tighter settlement ranges based on structured inputs.
Personal injury firms like Anidjar&Levine frequently see cases where algorithm-driven settlement ranges fail to reflect long-term pain, wage loss, or future care needs.
The controversy is not about having tools. It is about how rigid those tools become in practice, and how difficult it is to move beyond a recommended number when the injury story does not fit the model.
Regulators have scrutinized such practices before, including a multistate settlement involving Allstate and bodily injury claims software.
Appeals, Complaints, And Decision Support
Automation also shapes what happens after a denial or low offer. Systems generate templated explanations, route appeals, and prompt staff with standardized rationales.
If the underlying model is flawed, the appeal process becomes the only exit. That places a heavy burden on consumers and providers to push back.
Why Delays Happen Even When Systems Promise Speed
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Insurers often market AI as a way to shorten claim cycles. For straightforward claims, that can be true. For injury-related claims, several structural issues can stretch timelines instead.
“Additional Information Required” Becomes The Default
Automated workflows are unforgiving about missing fields, inconsistent codes, or documentation gaps. Rather than resolving ambiguity through a quick conversation, the system sends the file back for more paperwork.
Each request may seem minor. Together, they add days or weeks.
In health insurance, the Centers for Medicare and Medicaid Services highlighted how delayed prior authorization decisions directly affect patient care.
CMS finalized rules requiring expedited responses within 72 hours and standard responses within 7 calendar days for impacted payers.
Volume Expansion Creates Backlogs
Automation lowers the cost of review. Lower cost encourages more checks, more authorization steps, and more routing layers.
Even if each step is faster, the total number of steps increases. The result can be longer waits overall.
A Kaiser Family Foundation analysis reported that Medicare Advantage insurers made 52.8 million prior authorization determinations in 2024 and fully or partially denied 4.1 million requests, or 7.7%. A system operating at that scale pushes many people into follow up loops.
Appeals Are The Pressure Valve Few People Use
When automated decisions increase early denials, the appeal process becomes critical. Yet most people never appeal.
The same KFF analysis found that only 11.5% of denied Medicare Advantage prior authorization requests were appealed in 2024. A wrong automated decision often stands as the final word simply because challenging it takes time and energy many people lack.
Delay Shifts Leverage In Injury Cases
In bodily injury settlements, time matters. Medical bills arrive. Paychecks stop. Uncertainty grows.
Longer timelines can push injured people toward accepting lower offers. No one needs to state that intention. A high-friction process creates pressure on its own.
How AI Can Push Injury Settlements Lower
@accidentguru I would not recommend allowing ChatGPT to make these type of life decisions for a personal injury case. #personalinjurylawyer #chatgpt #slipandfalllawyer #losangeleslawyers #accidentlawyer ♬ original sound – Omid Khorshidi | 213-835-0301
Injury settlement value is not a fixed number. It reflects medical evidence, liability, wage loss, future care needs, and local jury expectations. AI systems compress that into structured inputs. Compression often leads to undervaluation.
| Problem | Explanation |
|---|---|
| Models Favor Codes Over Lived Impact | AI excels at processing standardized data such as diagnosis codes, procedure codes, imaging results, and discharge summaries. Pain, reduced mobility, caregiving burden, and uneven recovery patterns rarely fit cleanly into datasets. When a claimant’s strongest evidence lives in daily limitations rather than neat codes, the system may not fully register it. |
| Data Quality Problems Become Dollar Problems | Incomplete medical records or missing functional assessments lead directly to lower valuation ranges. If key facts are not extracted or entered correctly, the recommended settlement drops. The model does not know what it never sees. |
| Optimization Quietly Rewards Lower Payouts | AI systems are tuned to hit targets. In claims handling, targets often include average payout levels, close rates, and cycle times. Optimization toward lower average payouts is not speculation. It is a standard business objective. Without transparency, consumers cannot see how those priorities shape recommendations. Regulators have long worried about improper use of evaluation software. In 2010, Reuters reported that Allstate agreed to pay $10 million to 45 states after a review found inconsistencies in how it managed and used software to review bodily injury claims. |
| Settlement Ranges Harden Into Caps | Insurers often describe AI outputs as guidance. In practice, staff performance metrics may reward staying within recommended ranges. A suggestion becomes a ceiling. Moving beyond it requires extra approvals that slow the process further. |
| Local Context Gets Washed Out | Injury claim value varies by venue and jury behavior. Models trained on historical settlements can anchor to outdated patterns, even when legal environments change. |
A Concrete Example Outside Bodily Injury
Concerns about automated undervaluation appear in property and auto total loss claims as well.
In 2024, Reuters reported that Progressive agreed to pay $48 million to settle a class action alleging that it systematically undervalued total loss claims for New York policyholders by using third party software adjustments that reduced payouts.
Total loss valuation differs from bodily injury, yet the pattern is similar. When software-generated numbers dominate negotiation, consumers argue that the number itself drives the outcome.
Health Insurance Shows Where The Debate Is Headed
Healthcare coverage provides the clearest view of how automation affects real lives.
Senate Scrutiny Of Post Acute Care Decisions
A 2024 majority staff report from the U.S. Senate Permanent Subcommittee on Investigations examined practices at UnitedHealthcare, Humana, and CVS. Between 2019 and 2022, those insurers denied prior authorization requests for post-acute care at far higher rates than other services. The report described expanded use of automated processes in those decisions.
Litigation Over Predictive Tools
Lawsuits have alleged that UnitedHealth used an algorithmic tool called nH Predict to curtail coverage for elderly patients needing extended care.
Reuters and STAT reported on claims involving error rates and the difficulty of appealing denials.
Scale And Appeal Bottlenecks
The KFF Medicare Advantage analysis showed 52.8 million determinations and 4.1 million denials in 2024, with only 11.5% appealed. High volume combined with low appeal rates creates a system where automated decisions carry enormous weight.
Clinician Experience
The American Medical Association’s 2024 prior authorization survey reported that delays are common and sometimes linked to serious adverse events. Physicians consistently describe administrative friction as a barrier to timely care.
Also Read: Mental health technology is reaching a turning point with artificial intelligence!
What Regulators Are Doing About AI In Claims
Oversight is evolving. The central theme is governance. Insurers must explain how AI systems operate, how they are monitored, and how consumers can challenge outcomes.
NAIC And AI Governance
The National Association of Insurance Commissioners adopted a model bulletin on insurer use of AI systems in December 2023. It outlines expectations around accountability, transparency, compliance, and system safety. Claims handling falls squarely within its scope.
The bulletin serves as a template for state adoption rather than a single national rule.
Colorado And Algorithm Oversight
Colorado’s SB 21 169 and related regulations focus on external consumer data and predictive models. While much public discussion centers on underwriting, the broader signal is clear. States are paying attention to algorithmic decision making that affects consumers.
CMS And Standardized Timelines
CMS finalized rules aimed at reducing administrative burden and delays through electronic exchange and clear response deadlines. The agency explicitly linked delayed decisions to harm.
Europe And The High Risk Framework
In Europe, the European Insurance and Occupational Pensions Authority has discussed how the EU AI Act frames certain AI uses as high risk. Depending on application, insurers may face strict governance and risk management obligations.
Practical Risks Consumers Face in AI-Influenced Claims
As AI systems shape how claims are reviewed and decided, consumers often encounter practical risks that affect clarity, timing, and settlement outcomes in ways that are not always obvious at first glance.
| Izazov | Opis |
|---|---|
| Nejasna obrazloženja | Odbijenice i niske ponude često deluju kao šablonski dokumenti. Automatizovani sistemi mogu davati opšta objašnjenja, zbog čega podnosioci zahteva ne znaju koja bi dodatna dokumentacija mogla promeniti ishod. |
| Prebacivanje tereta dokumentacije | Strukturisani procesi zahtevaju precizno i obimno dokazivanje. Oštećene osobe moraju prikupiti kompletnu medicinsku dokumentaciju, procene funkcionalne sposobnosti, dokaze o gubitku zarade i usklađene izjave različitih pružalaca usluga. |
| „Zaglavljene“ niske ponude | Kada sistem inicijalno odredi iznos ponude, kasnije pregovaranje može biti otežano. Podnosioci zahteva ponekad dobijaju informaciju da korekcije nisu moguće, iako osiguravači tvrde da je ljudski faktor i dalje uključen. |
| Odugovlačenje kao pritisak | Produženi rokovi rešavanja zahteva stvaraju finansijski i emocionalni pritisak. Pritisak raste bez formalne odluke, ali sam protok vremena utiče na spremnost na kompromis. |
What To Do When A Claim Is Delayed Or Undervalued
General information only. Not legal advice.
For Injury Claimants
- Request the full claim file and valuation basis where permitted by state rules.
- Provide complete medical records, including functional limitations and recovery impact.
- Document wage loss with employer statements and pay records.
- Keep a communication timeline noting delays and information requests.
- Ask what specific facts would increase an offer and submit targeted evidence.
- Use formal complaint channels when response deadlines are missed.
For Health Coverage Denials
- Request the precise reason for the denial and the criteria used.
- Appeal quickly to avoid losing relevance to the underlying care.
- Track submission dates and pending notices.
- Document reversals and cite them if similar denials recur.
For Clinicians And Provider Offices
- Standardize documentation aligned with payer criteria.
- Track denial patterns by payer and service type.
- Use automation for record assembly, then verify outputs.
- Maintain clear escalation pathways.
Where Problems Tend To Cluster
| Claim Area | Common AI Usage | Delay Risk | Undervaluation Risk | Risk Mitigation |
| Auto bodily injury | Triage, severity scoring, settlement ranges | High | High | Complete medical narratives, functional documentation, escalation routes |
| Auto total loss | Vehicle valuation models | Medium | High | Comparable listings, valuation review, dispute processes |
| Health prior authorization | Automated routing and review | High | Medium | Fast appeals, criteria-based documentation |
| Post-acute care coverage | Predictive length of stay tools | High | Medium | Detailed clinical notes, appeal support |
What The Evidence Adds Up To
AI does not automatically harm consumers. Risk emerges from how insurers set incentives, how much authority automated recommendations carry, how transparent processes remain, and how accessible challenges are.
- Multistate regulatory action over bodily injury claims software.
- Senate investigations and litigation around automated coverage decisions with low appeal rates.
- Federal rules aimed at reducing authorization delays.
- Class action settlements alleging systematic undervaluation through software driven methods.
As insurers continue expanding AI in claims, one standard should remain non negotiable. Faster processing cannot mean less fairness, and automation cannot become a shield against accountability.
