Intro

Surprise medical bills are more than just a nuisance; they’re one of the biggest challenges in U.S. healthcare today. According to a 2024 Commonwealth Fund report, 45% of insured, working-age adults have faced unexpected bills or copayments for care they believed would be fully covered.

These unexpected charges aren’t just a financial burden for patients; they also show the enormous complexity insurers face in managing coverage decisions. Behind every denial is a delicate balance between controlling costs, preventing fraud, and ensuring patients get the care they need. It’s a tough line to walk, but one where AI can now help make a difference.

In this piece, I discuss how using AI can help secure prior authorizations, reduce coding errors, and even help patients challenge wrongful denials.

It shouldn’t hurt to use your insurance

Health insurance is supposed to offer protection. But for many, it brings confusion and sometimes disaster.
Take this story from Buzzfeed as an example. A woman with a rare autoimmune disorder depends on monthly IVIG infusions to stay out of the hospital. After years of coverage, her insurance suddenly stopped paying for it. Nothing had changed, except the bill. “The people at the insurance company couldn’t and still can’t give me an actual explanation,” she wrote. To make it worse, the insurer would cover the same treatment if she were hospitalized monthly, which costs more and puts her at greater risk.
She’s not alone. Another patient shared how she had to “get multiple CT scans because the first few weren’t authorized,” only to be billed thousands.
These stories hit hard because they’re not rare. The real issue isn’t just denial of coverage, it’s not knowing what’s covered in the first place. And that gap in understanding leads to stress, appeals, financial strain, even medical setbacks.
Fixing that communication gap could ease the pressure on both patients and insurers. And that’s exactly where AI may finally help.

How AI is making insurance coverage more predictable

Using AI to unpack what’s actually covered

Insurance policies are notoriously difficult to understand. They’re packed with dense legal language, ambiguous clauses, and disclaimers like “we’ll pay the lesser of…” that make it nearly impossible for the average person, or even industry experts, to know what will be paid for ahead of time.
AI has the potential to change that.
By analyzing how insurers actually respond to claims, not just what’s written in policy documents, AI can help identify patterns in approvals, denials, and gray areas. Companies like Anomaly are using machine learning models trained on real-world healthcare billing data to detect what’s likely to be covered and flag potential issues before a patient receives care.

The certainty gap in insurance

One of the biggest gaps in health insurance today is the lack of transparency. A survey by the American Hospital Association found that 83% of Americans want more clarity around what their insurance covers. That speaks to a widespread frustration – most people don’t know what their plan includes until after they’ve already received care. By then, it may be too late to avoid unexpected costs or denials (just like in the stories I shared earlier).
AI can help bridge this gap by providing real-time insights into coverage. With the right data and models, patients could know before stepping into a clinic whether a procedure, provider, or prescription will be approved by their plan.

How it works: learning from claims and remits

Anomaly, for example, focuses on the data that flows between providers and insurers – the claim that goes out (detailing the care delivered), and the response that comes back (either payment or denial). These exchanges offer a detailed picture of how coverage is actually applied, beyond what the official policies might say.
By training AI systems on this kind of transactional data, it’s possible to predict which services are most at risk of being denied, identify inconsistencies in payer behavior, and surface key coverage requirements like prior authorizations or referral needs.
AI can do more than just learn from the past, it can also operate in real time. By integrating with electronic health records (EHRs) and insurer databases, AI can:

• Instantly verify whether a service is covered under a patient’s specific plan.
• Flag missing referrals or pre-approvals before care is delivered.
• Alert providers and patients to high-denial-risk scenarios in advance.

These capabilities help reduce friction, prevent surprise bills, and increase trust in the system, all by shifting critical insurance knowledge upstream in the care process.

Faster response times

AI is also offering a solution to another common issue that results in coverage denials, i.e., meeting tight deadlines for taking formal actions.

The HFMA shares a great example from insurer, Care New England. By using bots to handle payer notifications when patients are admitted (a process that varies by insurer and requires quick turnaround) they cut authorization-related denials by 55%. As this step is just a formality and doesn’t involve any clinical decision-making yet, the company treated it as a natural fit for automation. The AI makes sure secure notifications go out within the required 24-hour window, and staff only step in for the more complex edge cases that actually need a call.

Working with a software development vendor, Care New England also went on to automate its broader prior authorization workflow. Within a year, they hit an 83% clean submission rate, cut authorization turnaround times by 80%, saved nearly 3,000 hours of manual work, and avoided $644K in write-offs and costs.

Fixing the small mistakes that cause big problems

Denial of insurance coverage is often framed as a clinical decision – was the treatment necessary, was it covered, did it meet the criteria? But peel back the layers, and a surprisingly mundane truth emerges. Many claims are rejected not because of what care was provided, but because of how the paperwork was done.

Andrew Witty, former CEO of UnitedHealth Group, put it bluntly. Reflecting on the company’s 2024 revenue and the broader industry landscape, he estimated that 85% of denied claims could have been avoided with better technology and more standardized processes. In other words, most denials come down to clerical errors. Forms filed incorrectly, codes misaligned, or details missing.

AI could be used to catch these mistakes before they happen, making sure every form is coded, formatted, and submitted according to an insurer’s exact specifications. That means fewer delays, fewer rejections, and less back-and-forth between providers and payers.

AI tools can suggest the most accurate CPT and ICD-10-CM codes based on clinical documentation, ensuring the billing narrative aligns with the medical record. This helps prevent common mismatches like an unsupported diagnosis code for a given procedure that lead to automatic rejections.

This could bring a bit more order (and fairness) into a system where small mistakes still block access to crucial care.

Easier appeal management

By now, it’s clear that many patients are denied coverage not because they don’t genuinely qualify, but because of administrative errors, misjudged “medical necessity,” or flat-out flawed systems. In theory, that’s what the appeals process is for. In practice though, it’s a black hole of time, money, and paperwork that most patients can’t navigate without expert help.
So, it’s hardly surprising that only 1 in every 500 claim denials is appealed. Instead, patients are often left choosing between an alternative treatment their insurer will cover, or paying out of pocket for the care their doctor originally recommended. Neither is a great option when well-being is at stake and healthcare costs are sky-high.
Ironically, some of the very AI systems designed to streamline claims and prior authorizations have also been the source of errors, i.e., denying coverage based on flawed assessments. And now, in a twist that feels very 2025, we’re watching what the HFMA calls “the battle of the bots”.
As denials grow more automated, so do the responses. Patients and advocacy groups are now turning to AI to push back. Tools like WayStar’s AltitudeCreate use generative AI to draft appeal letters automatically. There are also independent initiatives, with one engineer even releasing an open-source LLM designed specifically to help people, quite literally, “Fight Health Insurance.”
This second wave of AI doesn’t aim to replace medical judgment. It’s there to challenge flawed decisions and give patients a shot at reversing coverage denials that never should’ve happened in the first place.

While AI can be transformative, you must manage key risks

Generative AI is opening new possibilities in the insurance industry, from automating policy checks to improving claims processing. But while the potential is huge, there are also critical risks that organizations need to understand and mitigate.

1. Risk of experimentation without control

Even minor AI-driven errors can have significant consequences. If a system incorrectly interprets policy language, misses a crucial clause, or misguides a claims process, it can result in denied coverage, financial loss, or reputational damage.
That’s why deploying generative AI in insurance requires more than just experimentation. It demands well-tuned models, rigorous quality assurance (QA), and thorough validation pipelines. Controlled testing environments and human oversight are essential.

2. Data privacy and confidentiality

Public generative AI tools aren’t built for handling sensitive insurance data. Uploading things like underwriting rules, internal forms, or customer claims to open platforms can put private information at risk.
To stay secure, companies need to use AI systems that keep their data fully protected – processed in controlled environments, never shared, and never exposed to public models. Data privacy isn’t optional, it’s the foundation of trust.

3. Skills and knowledge gaps

Even though generative AI tools seem easy to use, building real, reliable solutions takes more than just clicking a button. It requires deep knowledge, not just of AI, but of how the insurance industry actually works.
Teams that skip the details or treat this like standard software development often hit roadblocks, make mistakes, or waste time on the wrong things.
To get it right, you need the right foundation including skilled people, strong data, and a clear understanding of the space you’re working in. When that’s in place, the possibilities are within reach.

Laying the groundwork for responsible AI in insurance

Success with AI in insurance isn’t just about moving fast; it’s about building smart. That means creating systems that are accurate, secure, and built to last.
Even if you plan to develop your own tools or train your own models, you don’t have to start from scratch. Partnering with someone who’s already built the core building blocks can save time, reduce risk, and help you move forward with confidence.
Look for a partner who knows the industry, brings real technical expertise, and offers a platform designed to meet the highest standards of privacy, security, and performance. Clurgo is that partner. With deep experience in both enterprise AI and the insurance industry, Clurgo delivers the tools, talent, and secured infrastructure to help you move fast without sacrificing control. Get in touch with our team to start a conversation.