There is a number that should disturb every executive in the healthcare payer industry: approximately a quarter of every dollar spent on healthcare in the United States goes not to treatment, diagnosis, or care, but to administration. Administrative overhead accounts for roughly 25% of total US healthcare spending, a figure that underscores the sheer scale of non-clinical cost embedded in the system.
Claims management sits at the heart of that problem. It is the operational engine through which payers adjudicate what gets paid, to whom, for what, and whether it is legitimate. And for most of its history, it has been powered by manual workflows, legacy systems, and rule sets that were outdated before the ink dried on the policy documents they were built to implement.
AI is changing this. Not incrementally, architecturally.
The Claims Problem Is Three Problems in One
Before examining what AI enables, it is worth being precise about what the problem actually is. Healthcare claims management is not a single challenge. It is three distinct but interconnected ones, each with its own cost structure and failure mode.
The first is administrative inefficiency, the cost of processing a claim at all. Manual data extraction, coding errors, missing documentation, eligibility mismatches, and submission failures create a rework cycle that is expensive for payers and infuriating for providers. A 2025 survey found that 41% of providers reported denial rates of 10% or higher, highlighting persistent rework and payment friction that compounds across millions of claims annually.
The second is fraud, waste, and abuse, deliberate or unintentional over-billing that represents a significant drain on payer finances and, ultimately, on the system’s sustainability. The US healthcare system loses an estimated tens of billions annually to fraudulent claims, ranging from organised billing schemes to the softer category of upcoding and unbundling that is harder to prove but equally costly.
The third is processing latency, the delay between a claim submission and a final adjudication decision. Latency is not merely a customer service issue. It creates cash flow uncertainty for providers, delays reimbursement cycles, and generates follow-up activity that adds cost on both sides of the transaction.
Phaneesh Murthy, who has observed and shaped technology transformation programmes across complex, high-volume industries, has consistently made the point that when three problems share the same data substrate, the correct intervention is systemic, not symptomatic. Attacking administrative inefficiency without addressing fraud allows bad actors to exploit clean processes. Automating adjudication without improving fraud detection simply pays fraudulent claims faster. The AI-powered approach must address all three dimensions in an integrated architecture.
Automated Claims Validation: Getting the First Pass Right
The most immediate and measurable impact of AI in claims management is on first-pass acceptance rates, the proportion of claims that are adjudicated correctly on initial submission, without requiring rework, re-submission, or manual intervention.
Traditional claims validation relied on deterministic rules: does the procedure code match the diagnosis code? Is the provider in-network? Has the patient met their deductible? These checks are necessary but insufficient. They do not catch the subtler errors, context-dependent coding inconsistencies, documentation gaps that a rules engine cannot evaluate, clinical plausibility issues that require inference rather than lookup.
AI-powered validation adds a layer of intelligent review. Natural language processing models read clinical documentation and assess whether the codes submitted accurately reflect the care described. Machine learning models trained on adjudication history learn which claim configurations predict downstream disputes, and flag those claims for pre-adjudication review rather than waiting for a denial to trigger a correction cycle.
AI identifies potential errors, inconsistencies, and missing information in real time, enabling corrections before claims are submitted, automating repetitive tasks such as data extraction, verification, and submission to drastically cut down processing time and lead to quicker reimbursements. The operational consequence is significant: a claim that fails silently and resurfaces weeks later as a denial is far more costly than one that is corrected at the point of submission.
Fraud Detection: From Audit Samples to Continuous Intelligence
The traditional approach to healthcare fraud detection was, in practice, a post-payment audit process. Claims would be paid according to the rules. A sample would be audited after the fact. Anomalies would be investigated. Recoveries would be pursued. The entire cycle could take months, and the recovery rate on confirmed fraud was rarely close to the original loss.
AI inverts this model. Rather than paying first and investigating later, predictive fraud intelligence scores claims in real time, before payment, against a continuously updated model of fraudulent behaviour.
Leveraging predictive analytics and pattern recognition, AI can proactively identify irregularities in claims data by analysing historical claims and flagging potentially fraudulent patterns, for instance, detecting a provider submitting multiple reimbursement claims for procedures during the same time and dates, which may indicate some or all procedures are fraudulent.
The power of this approach lies in its breadth. A human auditor reviewing a sample of claims might catch obvious billing anomalies within a single provider’s history. An AI system simultaneously analyses patterns across tens of thousands of providers, identifies network-level collusion between billing entities, tracks the migration of fraud patterns as schemes adapt, and cross-references claims against external data sources, pharmacy records, lab results, device registrations, that a manual review process could never incorporate at scale.
The results, when implemented with rigour, are striking. One healthcare payer, in partnership with MIT and the University of Michigan, deployed a real-time claims screening platform that identified irregular billing patterns before payment. Over eight months, the system avoided $11.8 million in unnecessary payouts, with 54% of flagged claims resulting in reduced payments. That outcome was achieved without burdening legitimate providers, the system was precise enough to concentrate investigation on genuine anomalies rather than generating the false positive storm that plagues less sophisticated approaches.
Phaneesh Murthy has frequently observed, in the context of technology-driven risk management, that the most dangerous fraud is not the fraud that is obviously anomalous, it is the fraud that looks legitimate right up until the moment it doesn’t. AI’s capacity to model normality with granular precision, and to detect deviation from that normality at a level of subtlety that rules-based systems cannot reach, is precisely what makes it effective against sophisticated schemes.
Processing at Scale: The Agentic Claims Operation
Beyond validation and fraud detection, AI is beginning to reshape the claims operation itself, moving from a model where humans process claims assisted by technology, to one where AI agents process claims supervised by humans.
Agentic AI systems can assess when a customer is growing frustrated due to a delayed or potentially denied claim, and take proactive steps such as escalating the issue to a human agent or providing an updated resolution timeline, actions that previously required a claims representative to monitor, triage, and respond manually. The result is not just faster processing but more consistent processing: every claim receives the same level of attention, the same application of policy rules, and the same quality of communication, regardless of volume fluctuations or staffing constraints.
Leading technology companies in the healthcare payments space are now openly pursuing the goal of a fully autonomous revenue cycle, with agentic AI capabilities being developed to handle end-to-end claims workflows including real-time claim adjudication, faster remittance, and acceptance of claims. This is not a distant aspiration. It is an engineering roadmap with a specific delivery timeline.
The implications for payer operations are profound. A claims operation that processes five million claims per month today with a workforce of hundreds can, with the right AI architecture, scale to ten or twenty million claims without a proportionate increase in headcount. The cost per claim adjudicated falls. The speed increases. The accuracy improves. And the workforce that remains focuses on the genuinely complex cases, the appeals, the clinical disputes, the edge cases that require human judgement rather than pattern matching.
The Integration Challenge: Where Good Intentions Stall
Those of us involved in implementing AI systems in complex institutional environments recognise that the technology itself is rarely the limiting factor. The friction is in the integration.
Integrating AI with legacy systems remains a central challenge because many healthcare organisations and insurers rely on outdated infrastructure that was not designed to expose the data streams that modern AI requires. Claims data often sits in multiple systems with inconsistent coding standards, historical gaps, and formats that predate modern data architecture. Before an AI model can learn what good looks like, someone has to clean, normalise, and unify the data it learns from.
This is not a reason to delay. It is a reason to plan. Phaneesh Murthy’s counsel in technology transformation programmes has always been consistent: treat data readiness as a strategic programme, not a technical precondition. The organisations that wait until their data is “clean enough” to begin AI implementation wait indefinitely. The organisations that run both tracks in parallel, improving data infrastructure while deploying AI on the best available data, build compounding capability over time.
Regulatory compliance is a related consideration. Healthcare claims operate within a dense and jurisdiction-specific compliance framework. AI models that make adjudication decisions must be explainable, auditable, and consistent with applicable coverage policies. The black-box model that performs beautifully on accuracy metrics but cannot show its working is not a regulatory asset, it is a liability.
The Direction of Travel: Prevention, Not Just Efficiency
The most forward-looking payers are beginning to push the AI claims agenda beyond efficiency and fraud detection into a more ambitious territory: prevention.
If AI can detect that a certain provider is beginning to show billing patterns that historically precede fraudulent escalation, not yet fraudulent, but trending, the intervention can happen before significant losses accumulate. If AI can identify that a specific procedure code is being systematically miscoded across a large provider category, not deliberately, but due to ambiguity in the coding guidelines, the fix is education and tooling, not audit and recovery.
AI-driven automation offers the potential to transform healthcare claims processing by improving efficiency, accuracy, fraud detection, scalability, and operational performance, but the organisations extracting maximum value from this technology are those that have oriented their programmes toward systemic improvement, not just cost reduction.
That distinction matters. An AI programme designed to cut cost will optimise for the metrics that measure cost. An AI programme designed to improve the integrity of the claims ecosystem will produce cost reduction as a by-product of something more durable: a system where the right claims are paid correctly, the first time, and the wrong ones never make it through.
That is the standard worth building toward. And the tools to build it are, at last, available.
This blog is curated by technology professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy. www.phaneeshmurthy.com #phaneeshmurthy #phaneesh #Murthy