Payer AI vs. Provider AI: How to Reduce Claim Denials in 2026

Healthcare revenue cycle management is entering a more automated era.

Health insurers are increasingly using artificial intelligence, machine learning, predictive analytics, and automated workflows across areas such as claims adjudication, prior authorization, utilization management, fraud detection, and risk adjustment. At the same time, providers are adopting AI to improve coding, eligibility verification, claim validation, denial prediction, and appeals.

The result is not simply a competition between "payer AI" and "provider AI." It is a shift from largely manual revenue-cycle processes toward systems that can evaluate large amounts of data and act much faster than traditional workflows.

For providers, that creates an important operational question:

Can your revenue cycle identify and correct a preventable problem before the claim becomes a denial?

That question matters because first-pass denials remain significant. HFMA reports that 11.65% of healthcare claims were denied on first pass in 2025, and some initial denials can occur within seconds of submission.[reference:0]

The opportunity for providers is therefore not just to work denials faster. It is to move more of the work upstream.

The 2026 Denial Landscape#

A useful starting point is separating verified industry measurements from vendor-specific claims.

Sources: HFMA, Experian Health, and NAIC.

Note

The NAIC survey covered 93 insurance companies across 16 participating states and was conducted from November 2024 through January 2025. The 84% figure should therefore be described as a survey finding, not as a census of every U.S. insurer.[reference:1]

These numbers do not mean that every denial is generated by AI. They show something more useful: automation is becoming part of the environment in which providers submit and manage claims.

How Payer-Side AI Is Changing Revenue Cycle Management#

AI is already being used by health insurers across several operational functions.

The National Association of Insurance Commissioners (NAIC) reported that 84% of surveyed health insurers currently use AI/ML in some capacity. The survey also found that insurers are using or exploring AI/ML in areas including utilization management, prior authorization, fraud detection, and other insurance operations.[reference:2][reference:3]

For providers, the practical implication is straightforward:

The claim is increasingly being evaluated inside a technology-heavy environment.

That makes accurate eligibility data, authorization information, documentation, coding, modifiers, and payer-specific rules increasingly important.

The "16×" Statistic Needs Context#

One statistic frequently cited in discussions about AI-assisted prior authorization is that AI-driven approaches can produce denial rates many times higher than typical rates in some circumstances.

However, that should not be presented as "payer AI denies claims at 16× the rate of human reviewers."

A 2026 peer-reviewed analysis in npj Digital Medicine discusses evidence concerning AI use in prior authorization and reports circumstances in which denial rates were substantially higher than typical. The statistic should be presented according to what the underlying study actually measured rather than converted into a direct AI-versus-human comparison.[reference:4]

This distinction matters when publishing healthcare content. Strong headlines are useful, but the underlying statistic must say exactly what the source measured.

Prior Authorization: A Major Area of Change#

Prior authorization is one of the areas where automation is developing particularly quickly.

The CMS Wasteful and Inappropriate Service Reduction (WISeR) Model began on January 1, 2026, and will run for six performance years through December 31, 2031, in Arizona, New Jersey, Ohio, Oklahoma, Texas, and Washington. CMS describes WISeR as a model that uses enhanced technologies such as AI and machine learning together with human clinical review.[reference:5]

CMS selected six participating organizations:

• Cohere Health — Texas
• Genzeon — New Jersey
• Humata Health — Oklahoma
• Innovaccer — Ohio
• Virtix Health — Washington
• Zyter — Arizona

CMS states that WISeR participants use technology to assess medical necessity for selected services and that all recommendations for non-payment are determined by appropriately licensed clinicians using standardized, transparent, evidence-based procedures.[reference:6]

Important

The WISeR model applies to a narrow set of typically elective services and sites of service (hospital outpatient, ambulatory surgery center, home, and office). It is not a blanket prior authorization program for all Medicare services.[reference:7]

For providers, the lesson is not that every automated authorization decision is wrong. The more actionable lesson is that authorization workflows need accurate, complete, timely information and a reliable escalation process when a decision needs human review.

Provider AI: Where It Can Help#

Provider-side AI can be useful when it is connected to actual revenue-cycle workflows rather than functioning only as a reporting dashboard.

1. Predictive Denial Detection#

Predictive models can identify claims that resemble previously denied claims.

The objective is to flag problems before submission, giving staff an opportunity to correct:

• Eligibility information
• Missing documentation
• Coding inconsistencies
• Modifier issues
• Authorization requirements
• Payer-specific claim rules
• Patient demographic or insurance data

HFMA describes provider-side AI applications that can analyze denial patterns by health plan and review claims before submission to help identify potential problems.

The key principle is simple:

A prevented denial is usually more efficient than an appealed denial.

2. AI-Assisted Medical Coding#

AI can support coders by identifying documentation gaps, suggesting coding possibilities, and checking claims against configured rules.

For example, an AI-assisted workflow could flag a psychotherapy claim when the documentation does not clearly support the required service time before the claim is submitted.

AI should not replace appropriate coding judgment or organizational compliance controls. Instead, it can provide another layer of validation.

HFMA reports that providers are already applying AI to medical coding, with medical coding among the leading provider-side revenue-cycle AI use cases.

3. Prior Authorization Automation#

AI and automation can reduce administrative work associated with prior authorization by helping staff gather information, complete forms, track requests, and identify missing information.

The regulatory environment is also moving toward greater electronic exchange. Providers should therefore view authorization automation as a workflow opportunity, while maintaining appropriate review and escalation procedures.

CMS's WISeR Model provides one current example of how enhanced technology and human clinical review are being combined in Medicare payment workflows.[reference:8]

4. Automated Denial and Appeal Workflows#

Modern RCM platforms can use AI to classify denials, identify likely root causes, prioritize work, summarize clinical information, and assist with appeal preparation.

HFMA notes that provider-side AI is being used both to prevent denials before they happen and to help produce appeal letters more quickly, with human review remaining important for complex or higher-cost claims.

Vendor-reported performance figures can also illustrate what is technically possible, but they should not be treated as universal industry benchmarks. For example, Waystar has reported results for its AltitudeAI platform, while Penguin Ai has reported results for its Intelligent RCM solution.

The Three Provider Infrastructure Gaps to Fix#

Gap 1: Front-End Data Problems#

Many denial problems begin before the claim is created.

Experian Health's 2026 research identifies front-end data accuracy as an important opportunity for reducing denials and reports that 25% of providers surveyed said denial rates had increased.[reference:9]

A strong denial-prevention workflow therefore begins with:

1. Accurate patient demographics
2. Insurance eligibility verification
3. Authorization verification
4. Provider and payer information
5. Complete clinical documentation
6. Coding validation
7. Modifier validation
8. Payer-specific claim rules

Tip

Experian Health found that 50% of healthcare organizations identify front-end accuracy as a key opportunity to reduce denials, and that incomplete documentation, coding, and eligibility errors rank among the top five most preventable causes of claim denials.[reference:10]

Gap 2: Reactive Denial Management#

A traditional workflow often looks like this:

Claim submitted → claim denied → denial discovered → denial researched → claim corrected → appeal/resubmission → payment

A more predictive workflow aims for:

Patient registration → eligibility → authorization → documentation → coding → prebill validation → claim submission → payment

The second workflow moves more intervention upstream.

That does not eliminate denials. It changes where the organization spends its time.

Gap 3: Detection Without Action#

A dashboard can identify a problem without actually fixing it.

For example:

"This claim has a high probability of denial."

That alert has limited value if nobody receives it, understands it, and has a workflow for correcting the issue.

The stronger model is:

Detect → explain → assign → correct → resubmit → measure → learn

This creates a closed feedback loop.

A Practical AI-Enabled Denial Prevention Workflow#

A provider organization does not necessarily need a fully autonomous revenue cycle to benefit from AI.

A practical implementation can begin with targeted workflows.

Step 1: Identify High-Volume Denial Categories#

Analyze denial data by:

• Payer
• CPT/HCPCS
• Diagnosis
• Specialty
• Location
• Provider
• CARC/RARC
• Authorization status
• Aging bucket

Step 2: Identify Preventable Patterns#

Separate preventable denials from denials that require payer policy interpretation or clinical review.

Step 3: Add Prebill Validation#

Use rules or AI to identify high-risk claims before transmission.

Step 4: Prioritize by Financial Impact#

Do not treat every denial equally.

A useful prioritization model can consider:

Dollar value × probability of recovery × filing deadline × clinical/documentation complexity

Step 5: Automate Repetitive Work#

Automation can assist with:

• Claim status checks
• Eligibility verification
• Work-queue routing
• Denial classification
• Documentation retrieval
• Appeal drafting
• Follow-up reminders

Step 6: Keep Human Oversight#

Healthcare revenue-cycle decisions can involve clinical documentation, payer policy, contracts, coding rules, and regulatory requirements.

AI should therefore be implemented with appropriate human review, auditability, access controls, and escalation paths. CMS's WISeR Model similarly combines enhanced technology with human clinical review for its selected services.[reference:11]

What Metrics Should Providers Track?#

A single overall denial rate is not enough.

Consider tracking:

• First-pass denial rate
• Denial rate by payer
• Denial rate by specialty
• Denial rate by CPT/HCPCS
• Denial dollars
• Preventable denial percentage
• Average days to identify a denial
• Average days to resolve a denial
• Appeal submission rate
• Appeal overturn rate
• AR over 90 days
• Net collection rate
• Downcoding frequency
• Authorization-related denials
• Eligibility-related denials
• Coding-related denials

The goal is to move from simply measuring how many claims were denied to understanding why they were denied, where the failure occurred, how much revenue is exposed, and whether the workflow prevented recurrence.

HFMA identifies denials management as a key revenue-cycle function and emphasizes accurate claims, eligibility verification, identifying denial causes, and preventing similar denials in the future.

Regulatory Developments Providers Should Watch#

Automation in claims and utilization management is also attracting regulatory attention.

Illinois Downcoding Law#

Illinois enacted the Transparency in Downcoding Act as Public Act 104-0568 in July 2026.

The law prohibits a health care payor from using an algorithm or other automated process to bypass evaluation of information submitted by a billing professional for purposes of downcoding. It permits automated tools to identify claims that may warrant review, but requires downcoding determinations to be made or reviewed by a natural person under the specified conditions.【10†L1-L5】

Warning

The Act states that its effective date is January 1, 2028. It should not be described as a fully effective 2026 requirement.【10†L1-L5】

Why This Matters#

The broader trend is important for revenue-cycle teams:

Automated claim review is developing, but transparency, documentation, human oversight, and appeal processes remain important parts of the regulatory conversation.

Providers should monitor applicable federal and state requirements rather than assuming that one AI workflow is appropriate across every payer and jurisdiction.

What "Winning" Looks Like in 2026#

The objective should not be to build an AI system that automatically fights every payer decision.

A better objective is to build a revenue cycle that is:

• Predictive rather than purely reactive
• Payer-aware rather than generic
• Integrated rather than fragmented
• Action-oriented rather than dashboard-only
• Auditable rather than opaque
• Human-supervised where judgment matters
• Continuously improving based on denial outcomes

The most valuable AI capability may not be an autonomous appeal writer.

It may be the system that prevents the claim from becoming an appeal in the first place.

Key Takeaways#

1. Denials remain a significant revenue-cycle problem. HFMA reported an 11.65% first-pass denial rate for 2025.
2. AI is increasingly part of the payer environment. NAIC found that 84% of surveyed health insurers reported using AI/ML in some capacity.
3. The 16× statistic requires careful wording. It should not be presented as proof that payer AI denies claims 16× more than human reviewers.
4. Provider AI can move work upstream. Predictive validation can help identify eligibility, authorization, documentation, and coding problems before claim submission.
5. Vendor results are not industry benchmarks. Vendor-reported performance should be clearly attributed rather than presented as universal outcomes.
6. Front-end accuracy matters. Experian Health's 2026 research identifies front-end data accuracy as an important denial-prevention opportunity.
7. Closed-loop workflows matter more than dashboards. Detection must connect to an accountable action.
8. Human oversight remains important. AI can accelerate revenue-cycle work without eliminating the need for qualified review.
9. Regulation is evolving. CMS's WISeR Model and state-level downcoding laws demonstrate that automated utilization and claims review will continue to receive scrutiny.

Final Thought#

The revenue cycle of 2026 is becoming increasingly data-driven.

Payers are using sophisticated technology. Providers are gaining access to predictive analytics, AI-assisted coding, automated authorization workflows, and intelligent denial management.

The strategic opportunity for a healthcare organization is not simply to "fight AI with AI."

It is to build a revenue cycle where technology helps the organization identify problems earlier, make better-informed decisions, reduce avoidable rework, and recover legitimate reimbursement more efficiently.

The key questions for your practice are:

1. Can you identify high-risk claims before submission?
2. Can you see denial patterns by payer, service, and root cause?
3. Can your workflow turn an AI alert into a corrective action?
4. Can you measure whether the intervention actually improves collections?
5. Do you have appropriate human review and audit controls?

If the answer to several of these questions is "no," the next step may not be another denial-management dashboard.

It may be a better-connected revenue-cycle workflow.

Curify Solutions supports healthcare providers with revenue cycle management, medical billing, coding, denial management, and accounts receivable workflows. If your practice is looking to connect denial prevention to a measurable operational workflow, contact Curify Solutions to discuss your revenue cycle.

Statistics and regulatory claims are presented with attribution and source links where applicable. Vendor-reported performance figures are not independent industry benchmarks, and individual results may vary. This article is for educational purposes only and does not constitute legal, coding, or payer-specific advice.