Day: May 25, 2026

Insurance, in its foundational logic, has always been a bet about the future. Payers collect premiums today against the probability of claims tomorrow. The entire business model depends on the accuracy of that probability assessment, who is likely to get sick, how sick, at what cost, and when.

For most of insurance history, that probability was estimated at the population level. Actuarial tables. Demographic risk pools. Broad categories applied to millions of individuals who did not, in any meaningful sense, resemble each other. The model was not wrong, it was the best available approximation given the data and tools of the time. But it was an approximation. And approximations, at scale, are expensive.

AI is replacing approximation with precision. And the consequences for how healthcare payers operate, and what they can accomplish, are more significant than most of the industry has yet fully reckoned with.

The Limits of Retrospective Risk

The dominant risk stratification framework in healthcare payer operations has long been retrospective. Risk Adjustment Factor scores, the mechanism by which Medicare Advantage and other value-based programmes calibrate payments, are built primarily on historical claims data. What did this patient cost last year? What diagnoses were coded? What conditions are on record?

Traditional RAF scores often fall short in accurately predicting patient risk due to their reliance on historical claims and limited consideration of social determinants of health. This creates a structural problem: the patients most likely to generate significant future expenditure are often the ones whose risk has not yet manifested in the claims record. They are the undiagnosed diabetic. The individual in a high-stress, food-insecure household whose hypertension is building silently. The member whose mental health deterioration will, in eighteen months, produce an emergency department admission that costs fifty times what early intervention would have.

Retrospective models do not find these people. By the time the data that would identify them appears in the claims record, the preventable event has already occurred.

Phaneesh Murthy has long argued, across multiple industries he has advised and transformed, that the most costly failures of technology systems are not errors, they are absences. The insight that was not surfaced. The risk that was not flagged. The intervention that was never triggered because the data existed but the system was not designed to read it. In healthcare payer operations, this principle is not merely a philosophical observation. It is a financial and human reality playing out across millions of member lives every year.

What Predictive Risk Modelling Actually Does

The shift from retrospective to predictive risk stratification is, at its core, a shift in the question being asked. The old question was: what has this member cost? The new question is: what is this member likely to cost, and what can we do about it before that cost is realised?

Predictive AI uses machine learning, advanced analytics, and generative reasoning models to forecast future health events across entire populations, using historical and real-time data to predict deterioration in conditions like diabetes, heart failure, and COPD long before symptoms peak, and identifying frequent emergency department users before they become high utilisers, allowing preventive intervention.

The data inputs feeding these models are far broader than the claims history that powers legacy approaches. Electronic health records contribute clinical observations, lab trends, medication adherence signals, and care gap data. Pharmacy records reveal prescription fill rates, a powerful proxy for how actively a member is managing a chronic condition. Wearable and remote monitoring data, where consented and available, adds real-time physiological signals. And critically, social determinants of health, housing stability, food access, employment status, neighbourhood characteristics, contribute the contextual layer that purely clinical data cannot capture.

Prediction models combining claims data with social determinants of health and additional, more timely data sources using AI can better identify individuals with the highest future medical spending than traditional models alone. Critically, identifying preventable spending may require identifying patients with rapidly rising risk scores, not just patients whose scores are already high.

That last point deserves emphasis. The member whose risk score is already high is already expensive. The intervention opportunity, while still real, is constrained by the trajectory already underway. The member whose risk score is rising, still moderate in absolute terms, but trending upward at a rate the model can detect, represents the higher-value intervention opportunity. Catching the deterioration in progress, before it accelerates, is where predictive modelling generates its most significant returns.

Stratification Into Action: The Care Intervention Layer

Predictive modelling without a connected intervention infrastructure is an expensive exercise in producing worklists that nobody acts on. The capability that transforms risk scores into outcomes is the care management layer, the programmes, outreach mechanisms, and clinical partnerships that translate a model’s prediction into a tangible change in a member’s health trajectory.

Analysing large sets of clinical, behavioural, and demographic data enables earlier outreach, more precise care plans, and interventions calibrated to each patient’s actual barriers, ultimately leading to fewer avoidable hospitalisations and better chronic-condition stability. The key is embedding predictive intelligence into daily clinical decisions, not isolating it in reports.

In practice, this means integrating risk model outputs into the workflows of care managers, utilisation review nurses, and member engagement teams, so that the highest-priority members surface automatically in the right care management queue, with the relevant clinical context pre-populated, and with a suggested next action informed by what the model knows about that member’s situation.

AI tools empower healthcare leaders to continuously monitor risk factors in real time, automate the detection of early warning signs, and personalise outreach at scale, targeting interventions more precisely to reduce hospitalisations and drive better outcomes across diverse communities. A member flagged as a rising-risk diabetic with low medication adherence and evidence of food insecurity does not need the same outreach as a post-surgical recovery case or a member with a primary mental health diagnosis. The intervention is personalised not because a care manager had the time to do extensive research, but because the AI system has already assembled the relevant picture.

Phaneesh Murthy’s consistent guidance to technology implementation teams is that a system which produces intelligence but does not change behaviour has delivered analytics, not transformation. The measure of a predictive risk programme is not the accuracy of its predictions, it is the reduction in preventable adverse events. That reduction only happens when the prediction is connected, cleanly and quickly, to an action.

The Economics of Prevention: Why This Is Also a Financial Strategy

Healthcare payers operate in an environment of enormous cost concentration. A small percentage of members generate a disproportionate share of total expenditure. The goal of applying machine learning to identify members at risk of very high costs, exceeding $250,000 in total healthcare expenditure over the next twelve months, represents a focused attempt to guide limited intervention resources toward the highest-risk and highest-need individuals.

This concentration is both the problem and the opportunity. If a payer can identify the members heading toward catastrophic expenditure six to twelve months before the acute event occurs, and intervene effectively in even a fraction of those cases, the financial return on the predictive programme is substantial. The cost of a care management programme, outreach calls, care coordinator time, disease management enrolment, medication support, is a fraction of the cost of an avoidable hospitalisation, an ICU admission, or a preventable surgical procedure.

Predictive AI is one of the rare healthcare innovations that improves both quality and finance simultaneously, and every major healthcare system using predictive AI reports meaningful, measurable improvement in key quality and cost metrics. This dual return is important because it dissolves the false tension that has historically existed in healthcare between clinical improvement and financial sustainability. In preventive insurance, they are not competing goals. They are the same goal.

The value-based care movement has been building the contractual and incentive structures that make this economics visible. When a payer’s financial performance depends on keeping members healthy, not simply on paying claims efficiently, the investment case for predictive risk modelling becomes self-evident. The question is no longer whether to build these capabilities. It is how quickly, and how well.

The Data and Ethics Dimensions

No serious discussion of predictive risk modelling in healthcare can ignore the ethical dimensions of the capability being built.

When an AI system assigns a risk score to an individual member, and that score influences the intensity of care management they receive, the coverage determinations made on their behalf, or the way their insurer communicates with them, questions of fairness, transparency, and consent are not peripheral. They are central.

Emerging AI-driven tools offer a smarter, proactive approach by analysing diverse data sources for real-time insights, but successful implementation requires addressing regulatory, ethical, and operational challenges. Models trained on historical data inherit the biases embedded in that history. If a population has historically been under-diagnosed due to systemic barriers to care access, a model trained on their claims record will underestimate their clinical risk, not because the model is poorly designed, but because the data it learned from reflects a reality of unequal access, not a reality of unequal need.

This is not an argument against predictive modelling. It is an argument for building it with rigour, transparency, and ongoing bias monitoring. Payers that deploy these systems responsibly, with explainable model outputs, regular fairness audits, and clear member communication about how data is used, will build the trust that makes the programme sustainable. Those that treat algorithmic risk scoring as a purely technical exercise, insulated from governance scrutiny, will eventually encounter the regulatory and reputational consequences of that choice.

Phaneesh Murthy has been consistent in his view that the organisations that lead in technology transformation are those that earn their licence to operate it. In predictive risk modelling, that licence is earned through the quality of outcomes delivered and the integrity with which the capability is governed.

Building the Preventive Insurance Organisation

The shift from reactive payer to preventive insurer is not accomplished by deploying a risk model. It requires a different organisational design, one where data science, clinical leadership, care management operations, and member engagement work from a shared framework and a shared set of objectives.

It requires investment in data infrastructure: unified member records that bring together claims, clinical, social, and behavioural signals into a single longitudinal view. It requires care management capacity sized to act on what the models surface. And it requires measurement systems that can attribute outcomes, reduced hospitalisations, better chronic disease management, lower total cost of care, to specific interventions with sufficient rigour to guide continuous improvement.

One large payer organisation now applies real-time clinical and claims data to intervene proactively, while another uses predictive models to map Chronic Kidney Disease progression and tailor care plans over time, demonstrating that the workflow integration between payers and providers is where the real value of prediction is unlocked.

The technology is available. The evidence base is established. The financial case is compelling. What separates the payers building genuinely preventive insurance capabilities from those still talking about it is not access to tools, it is the organisational will to redesign around a different model of value creation.

The future of healthcare insurance is not a payer that processes claims efficiently. It is a payer that prevents the claims worth preventing, and can demonstrate, clearly and credibly, that it is doing so.

That is a different business. And the organisations building it today will define what insurance means tomorrow.

This blog is curated by technology professionals who are mentored by veteran Marketer, and industry leader, Phaneesh Murthy. www.phaneeshmurthy.com #phaneeshmurthy #phaneesh #Murthy

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