5 Red Flags When a Vendor Claims to Fully Automate Your Claims

Choosing a claims automation vendor is harder than it should be, because the pitches all sound the same. The problem they promise to solve is real: AJMC reports that 41 percent of providers now experience denial rates of 10 percent or higher, with missing data and authorization issues among the primary causes. Automation is not solving that core problem for most practices, yet vendors keep selling full automation as if it were a finished product. This is a buying guide. Here are five patterns that signal a vendor is overpromising, and what a credible pitch looks like instead. For the bigger picture, see our companion post on why full claims automation is a myth.

Red Flag 1: They Can’t Tell You Which Claims They Can’t Automate

Any honest automation vendor knows the limits of their own system. If a vendor cannot name which claim types require human intervention, or cannot give you an auto-adjudication rate broken out by claim category, they are either hiding the number or do not have one. GlobalLogic notes that even cognitive claims automation escalates ambiguous and high-risk cases to human reviewers rather than forcing them through. A vendor who cannot explain their exception handling process effectively does not have one. As Kognitos underlines, exception handling is where automation succeeds or fails. Ask for the auto-adjudication rate by claim type and the manual fallback workflow before you sign anything.

Red Flag 2: They Promise ROI on Day One

AI-driven claims tools learn from historical data, which means they need time and volume before they perform. The AJMC survey found that denial rates are rising for many providers despite increased adoption of digital tools, a reminder that improvement depends on how a tool is implemented and trained, not simply on buying it. A vendor promising immediate, dramatic ROI is either selling rules-based automation that does not adapt, or overpromising what their AI can do before it has learned the practice’s payer mix and claim patterns. Kognitos describes this learning curve as a feature of how these systems work, not a flaw. Ask what a realistic timeline to denial-rate improvement looks like and what data the system needs to get there.

Red Flag 3: They Conflate Digitization with Automation

Electronic claim submission is not automation. Scrubbing claims against a rules checklist is not AI. Conduent explicitly notes that marketing often conflates digitization with full automation, and that “minimal human intervention” does not mean no human intervention. Ask the vendor what specifically gets automated in the workflow: submission, verification, coding, adjudication, or all four. A specific, stage-by-stage answer is a green flag. A pitch about end-to-end automation with no breakdown of what each stage actually does is not.

Red Flag 4: They Can’t Explain How Their Denial Prediction Model Works

AI-driven denial management works by identifying risk factors in claims before submission and flagging them for correction. HFMA describes pre-submission tools that markedly improve first-pass clean claim rates by detecting risk and suggesting corrections in real time. A vendor who cannot describe what inputs their model uses, how it surfaces risk to staff, or how performance is measured over time is not really selling AI. As Camunda points out, plenty of products wrap a rules engine in a marketing layer and call it intelligence. Ask specifically how the model learns and how it is trained on the practice’s own claim data.

Red Flag 5: They Claim Automation Eliminates Denials

Automation reduces denial rates. It does not eliminate them. HFMA reports a 19 percent denial reduction within six months at one hospital using AI-driven denial prediction, which is a meaningful result. It is not zero. Prior authorization denials, documentation deficiencies and coverage edge cases continue to require human attention regardless of the platform, a pattern the AJMC data reinforces. A vendor who promises to eliminate denials has either redefined what a denial is or is describing a scenario their system has never encountered. Improvement is an honest claim. Elimination is not.

What Good Actually Looks Like

An honest automation vendor can describe specific claim types they handle well, specific claim types that require human judgment, a realistic timeline to performance improvement, and measurable outputs such as clean claim rate, first-pass rate and denial rate by category. That is the model behind Fuse claims automation: automation applied where it reliably works, exceptions surfaced to your staff rather than hidden, and outcomes reported at the claim level so you can see exactly where automation is working and where it is not. Kognitos and HFMA both make the same point in different words: the strongest programs measure what they automate. Ask any vendor to do the same, and the red flags sort themselves out.