Healthcare organizations are dealing with an ideal storm: rising declare denials, evolving payer guidelines, and sufferers anticipating suppliers to scale back error charges that impression affected person billing accuracy. Synthetic intelligence (AI) has raised the stakes, inflicting income cycle leaders to really feel the strain to modernize rapidly.
In accordance with Experian Health’s State of Claims 2025 survey, 73% of suppliers agree that declare denials are rising, which is a transparent sign that outdated processes value suppliers tens of millions. The highest-ranked causes for denials included coding errors, lacking or inaccurate knowledge, authorizations, and incomplete info, to call just a few. And with only 14% of providers utilizing some type of AI know-how of their processes, the message is obvious: the chance is excessive to get extra suppliers to embrace the know-how and reap the advantages of smarter automation.
To remain aggressive and financially viable, healthcare organizations should embrace AI-driven innovation that improves knowledge accuracy, streamlines workflows and proactively prevents income leakage. To discover how main RCM corporations are responding, we interviewed David Figueredo, Experian Well being’s VP of Innovation, to get a more in-depth have a look at how we’re serving to healthcare organizations use AI to sort out these challenges head-on.
Meet the Govt
David Figueredo, VP of Innovation at Experian Well being, has spent over 20 years driving transformation in healthcare finance. Identified for mixing tech-forward considering with operational experience, David is captivated with utilizing AI to unravel persistent challenges in income cycle administration, particularly round declare denials and knowledge accuracy. He believes that healthcare innovation should be each purposeful and scalable.
“We’re not simply chasing traits, and buzzwords don’t functionally remedy issues,” he says. “By specializing in constructing techniques that adapt to payer behaviors and addressing the labor prices and handbook inefficiencies suppliers face at this time, we are able to ship measurable enhancements in monetary efficiency.”
David is captivated with constructing instruments that empower income cycle groups to work smarter, not more durable. “We’re not simply layering tech on high of damaged processes,” he says. “We’re redesigning the workflows themselves to intuitively account for these rising AI capabilities and by doing so, we’re discovering methods to basically change these processes.”
Q1: “David, let’s begin with the massive image. How are you and your staff fascinated with innovation in income cycle administration proper now?”
David: “At Experian Well being, innovation is a strategic crucial, and the core to the whole lot we do. We’re targeted on fixing income cycle ache factors, particularly round claims administration and affected person entry by mixing AI, automation, and knowledge intelligence to streamline workflows. We’re not simply attempting to overlay new tech on yesterday’s processes; we’re reimagining how income cycle groups will function, to scale back handbook contact factors and improve automated decisioning. Which means leveraging AI to automate repetitive tasks, allow earlier and steady monitoring with well timed corrections, and equipping groups with actionable workflows backed by reliable, clear insights.
We’re additionally seeing a shift in mindset and attitudes round automation and utilized AI. Innovation was once a long-term purpose that took years to see measurable outcomes. Now, it’s a short-term mandate the place the tempo of progress must ship worth at this time and elevated worth tomorrow. Our shoppers anticipate to see and really feel the progress now, not simply the promise of worth in years to return. That’s why we’ve designed a modular resolution that permits shoppers to deploy AI instruments the place they ship probably the most rapid worth, whereas additionally supporting extra advanced workflows and integrations for the longer term. This contains integrating intelligence to enhance eligibility checks, coordination of benefits (COB) and id capabilities, enhancing declare scrubbing processes with correct denial prediction and prioritization, and strengthening monetary selections with higher knowledge modeling that builds belief.
Innovation needs to be cross-functional. This implies aligning product design with IT construct processes to scale back deployment instances and mitigate dangers, incorporating operations groups to make sure the best issues are being addressed, and enabling finance groups to raised perceive how know-how impacts main and secondary income streams.”
Watch our on-demand webinar to find out how healthcare organizations are utilizing AI to eradicate handbook payer chaining, detect and proper protection points in real-time, and cut back declare denials.
Q2: “AI is in every single place as of late, however how are you really utilizing it to scale back declare denials and enhance knowledge accuracy?”
David: “AI could be a game-changer, however there may be extra to fixing issues than simply making use of new know-how. In accordance with Experian Health’s State of Claims 2025 report, 41% of respondents say their claims are denied greater than 10% of the time. And 54% agree that errors in claims are rising. We’ve got to be considerate in how and the place we apply AI to enhance studying on the fly, promote built-in choice assist in actual time and automate actioning in order that extremely expert and restricted employees can deal with higher-value capabilities. AI is not only about automation; it’s about clever intervention utilized to actual issues, eradicating guesswork, early problem identification and eliminating missed steps to enhance the general yield of the income cycle.
Take into account the denial area, the place billions in income are misplaced annually. Whereas the causes of denials are very numerous, a lot of them are wonderful alternatives for utilized AI to enhance denial charges. Our flagship product, Patient Access Curator™, makes use of AI to deal with key drivers, equivalent to eligibility and COB errors that account for 15-30% of all denials. AI can surveil system and consumer exercise to detect missed protection or primacy points, then pursue these leads and replace the HIS in real-time — each at registration and at each different touchpoint within the affected person journey.
One other nice instance of utilized AI is our AI Advantage™ denial prediction and triage solution. Whereas declare denial screening and prioritization should not new ideas, AI takes this to a brand new degree by integrating behavioral analytics, machine studying processes and massive knowledge analytics right into a simplified course of. This resolution doesn’t simply detect denials; it prioritizes them primarily based on monetary impression and chance of denial restoration, pushed by a bigger choice assist framework that improves accuracy and reduces noise. Income cycle groups can then deal with high-value, revenue-protecting actions, fairly than low-yield procedural work.
Our fashions constantly be taught from evolving payer behaviors as they emerge, to foretell denial danger and suggest corrections in actual time. And since they’re constantly studying, they get smarter and vastly extra adaptive than legacy methods of prioritizing pre-denial and denial workflows. It’s a dynamic system that evolves with the payer panorama that maximizes restricted assets, which I believe is the hope and expectation of contemporary, AI-driven income cycle processes.”
Q3: “Are you able to give us a way of the impression? What sort of outcomes are shoppers seeing with AI instruments?”
David: “Completely. We’re seeing some superb early knowledge that clearly level to very differentiated outcomes over conventional know-how approaches. Since deploying our AI-driven denial prevention engine, we’ve seen a 15-60% discount in preliminary eligibility and COB declare denials, with a median efficiency of ~30% discount throughout our consumer base. Nevertheless, the impression is not only on declare denials; we have now to grasp there are populations of sufferers, equivalent to self-pay sufferers, that profit from improved automation and intelligence that AI utilized appropriately can deliver.
We’re additionally seeing important reductions in self-pay at registration charges when AI is driving the automation. Right here, we see ~25% reductions in self-pay on the time of registration. That is related and placing on so many ranges, as right estimates can now be offered pre-service, and authorization processes can now work extra successfully, which results in higher affected person experiences.
What’s most impactful is how these outcomes compound over time. As AI instruments mature, they begin figuring out systemic points—like recurring documentation gaps or payer-specific quirks—that handbook evaluations usually miss. That perception permits shoppers to repair particular person claims whereas optimizing workflows and upstream processes, resulting in long-term positive factors in effectivity and income integrity.”
Find out how Patient Access Curator streamlines affected person entry and billing, prevents declare denials, improves knowledge high quality, and makes real-time corrections to spice up your healthcare group’s backside line.
This autumn: “Let’s speak concerning the affected person aspect. Quite a lot of innovation is going on behind the scenes, so how does that translate into a greater affected person expertise?”
David: “That’s a terrific level. Quite a lot of what we do in income cycle innovation isn’t seen to sufferers, but it surely completely impacts their expertise. In lots of instances, our sufferers are the victims of damaged processes and fragmented knowledge that AI and associated know-how enhancements will assist to resolve.
Take declare denials, for instance. When a declare is denied due to a lacking authorization or incorrect insurance coverage info, it doesn’t simply delay fee; it creates confusion and stress for the affected person who could instantly obtain a shock invoice for one thing outdoors of their management. Resolving this problem requires a number of calls to the supplier or payer, which provides frustration. This creates a demanding expertise and negatively impacts the supplier’s model notion.
That’s the place AI makes the distinction. We use Experian Well being’s AI-powered registration optimization and claims administration instruments, like AI Advantage, to catch these points early, earlier than the inaccurate estimate is generated, earlier than the authorization is missed or earlier than the declare is submitted. This drives extra consistency and automation into the income cycle. By bettering knowledge accuracy on the entrance finish—with issues like insurance coverage verification, COB problem detection, automated protection surveillance and predictive analytics — we’re serving to suppliers get it proper the primary time. The consequence: fewer billing surprises, quicker resolutions and a smoother affected person journey.
Whereas the affected person could not see the AI working within the background, they really feel the distinction when their estimates are extra correct, duplicate or conflicting statements are diminished, and so they not should chase down solutions. This builds belief and improves affected person satisfaction – permitting them to deal with their well being, fairly than income cycle points they need to by no means should cope with.”
Q5: “For healthcare organizations which can be simply beginning to modernize their income cycle, the place ought to they start?”
David: “Begin by understanding your inside views, change threshold and restrictions. Many healthcare suppliers don’t ask onerous questions on their targets, the info they’re prepared to share or learn how to prioritize their wants. AI is barely pretty much as good as the info it has entry to, so guarantee your knowledge is clear, structured, and compliant with authorized and scientific necessities.
Subsequent, discover companions with the best technical instruments and healthcare expertise. Concentrate on measurable outcomes —not simply know-how—and prioritize areas with the best income leakage, excessive FTE investments or elevated affected person danger. Don’t underestimate the significance of change administration. Contain your operations, coaching and technique groups early, and make them a part of the innovation course of. Overemphasize the human component of change management to enhance outcomes.
Lastly, at all times hold the affected person in thoughts. Each enchancment within the income cycle impacts their expertise and entry to care. Design know-how options that simplify the affected person journey, cut back their burden, and assist decrease the price of care.”
The way forward for RCM lies in AI innovation
As healthcare organizations navigate mounting monetary pressures and the rising complexity of payer necessities, the necessity for smarter, AI-powered options has by no means been higher. By embracing clever automation, suppliers can cut back pricey errors and denials, strengthen their monetary stability and improve affected person experiences.
Find out how Experian Well being’s AI-driven options, like Patient Access Curator and AI Advantage, might help your healthcare group reduce declare denials, streamline workflows and unlock new alternatives for monetary success.
