Healthcare fraud diverts resources from patient care, increases costs for employers and members, and undermines trust. Preventing it is both a financial priority and a responsibility to protect the resources patients depend on.
Artificial intelligence (AI) can help health plans identify suspicious claims earlier, focus investigations, and prevent improper payments. But success depends on more than technology. This article explores how healthcare organizations can use AI to strengthen fraud detection, measure business value, and protect resources through responsible implementation.
Why Traditional Fraud Detection Falls Short
Why should health plans move beyond traditional detection methods? Rules-based systems are still useful for identifying known billing irregularities, such as duplicate claims, unusually high procedure volumes, or charges that violate payment policies. However, these systems rely on predefined conditions, making it harder to detect schemes that fall outside established patterns.
Manual audits have a fundamental challenge: scale. Health plans process large volumes of claims, and investigators cannot examine every submission in depth. As a result, teams must prioritize cases, and suspicious activity may go undetected until after payment.
The financial impact can be substantial. In fiscal year 2023, improper payments across Medicare and Medicaid exceeded $100 billion, according to estimates reported by the U.S. Government Accountability Office (GAO) in 2024. These figures include payments made incorrectly or without sufficient justification, not just fraud, but they highlight the scale of the payment-integrity problem.
For healthcare leaders, the priority should be to identify emerging risks sooner, prevent avoidable losses, and ensure legitimate claims are processed without unnecessary delays. That’s where AI comes in.
How AI Improves Fraud Detection
As a subset of AI, machine learning enables systems to learn from data and adapt based on past experiences, making it possible to identify patterns associated with potentially fraudulent activity. Instead of assessing a claim against a single threshold, a trained model can consider provider billing history, patient records, procedure codes, referral patterns, and comparable activity across a network.
Upcoding is one example. A provider may bill for a more complex or expensive service than the medical records support. Rather than flagging every provider with high-value claims, AI can look for changes in billing patterns, unusual combinations of services, and gaps between documented care and billed services. This helps healthcare organizations focus on claims that warrant closer scrutiny.
However, a suspicious pattern does not automatically mean fraud has occurred. High claims volumes or costly treatments may be entirely justified by a patient’s condition or a provider’s specialty. Healthcare leaders still need to review the supporting evidence before deciding whether further action is necessary.
This proactive approach also supports earlier intervention. In a March 2026 report, the U.S. GAO described how the Centers for Medicare & Medicaid Services (CMS) uses claims analytics to identify suspicious billing patterns and inform actions to prevent potentially fraudulent payments. CMS estimated that administrative actions prevented $11.9 billion in potentially fraudulent Medicare payments during fiscal years 2022 through 2024. This is evidence of the value of data-driven program integrity, not a guarantee of equivalent savings from AI alone.
Strengthening Identity and Provider Verification
While data-driven claims analysis can help prevent losses, effective fraud prevention also depends on verifying the identities and credentials behind each claim. Healthcare organizations also need confidence that patients, providers, and facilities are who they claim to be.
AI can help cross-check provider credentials, practice locations, and billing records to flag inconsistencies that may warrant further investigation. For example, a newly credentialed provider submitting unusually high volumes of high-cost claims could trigger a review. Similarly, claims showing overlapping appointments at distant locations may indicate potential identity misuse or billing irregularities.
Digital identity tools may add further safeguards to online enrollment and claims-related transactions. Depending on the use case, these can include document verification, device-risk signals, and checks for automated or impersonated activity.
But no single signal should determine a fraud finding. Location data can be inaccurate, providers can legitimately practice across multiple sites, and patients may receive care while traveling. Healthcare organizations should use identity checks to trigger proportionate verification, not as automatic grounds for denying care or payment.
Reducing False Positives Without Compromising Care
A fraud detection system that incorrectly flags legitimate claims can create its own set of challenges. It may increase investigation costs, delay provider reimbursements, frustrate members, and divert valuable resources from higher-risk cases.
AI can help improve the balance by processing claims in context. Frequent, expensive treatments may look unusual when assessed in isolation but be entirely appropriate for a patient with a complex or chronic condition. A model that incorporates relevant clinical and billing information can help distinguish expected care patterns from activity that merits investigation.
The business objective is not simply to generate more alerts. It is to improve the quality of those alerts and help professionals resolve cases more effectively. Healthcare organizations should measure this through indicators such as the proportion of alerts that lead to substantiated findings, the time required to resolve an issue, false-positive rates, prevented or recovered payments, and the number of legitimate claims delayed. They should also monitor whether certain patient groups, specialties, or provider types face disproportionate scrutiny.
Automation can accelerate straightforward checks and route higher-risk cases to experienced reviewers. But organizations should not assume that AI will automatically improve processing times or detection rates. Those outcomes depend on model quality, workflow design, data access, and how teams use the results.
Building the Data Foundation for Reliable Results
Even the strongest AI model is likely to struggle if the information it receives is incomplete, inconsistent, or disconnected. Many healthcare organizations run claims, provider credentials, pharmacy transactions, and clinical records in disconnected systems. These gaps make it harder to connect suspicious activity and distinguish genuine anomalies from routine variations in care.
Therefore, data integration should be a core part of any fraud prevention strategy. Healthcare organizations need reliable links between claims and supporting documentation, consistent provider identifiers, and clear processes for correcting inaccurate records.
Natural language processing can help extract relevant details from clinical notes and other unstructured documents. This may help health professionals compare what the documentation describes with what the provider billed. For example, a mismatch between the recorded service and the submitted procedure code could trigger a closer review.
A connected data environment can also help identify duplicate billing, services that may never have occurred, and suspicious relationships across providers or facilities. But integration alone does not guarantee accuracy. Healthcare organizations must also establish data quality standards, manage access, and regularly check whether their information remains fit for purpose.
For buyers, the practical question is not simply whether a vendor offers advanced AI. It is whether the solution can work with existing claims and clinical systems, explain the evidence behind its alerts, and deliver useful results without requiring an unrealistic overhaul of the entire technology environment.
Ensuring Governance, Privacy, and Accountability
Healthcare fraud prevention requires trust. An inaccurate fraud alert can disrupt a provider relationship, delay reimbursement, or trigger an investigation that affects a legitimate business. The higher the impact of a decision, the more important it becomes to understand how the system reached its conclusion.
Health professionals should establish clear ownership for model performance, investigation decisions, and ongoing monitoring. They should document the data and factors used to generate alerts, test for bias, maintain audit trails, and define when a human reviewer must intervene. AI recommendations should support accountable decision-makers, not replace them.
Privacy safeguards are as important. In the United States, organizations subject to HIPAA must comply with requirements for protecting health information, including relevant privacy and security safeguards. Data access should match the task, and organizations should evaluate how vendors store, use, and protect sensitive information.
The National Institute of Standards and Technology’s AI Risk Management Framework provides a practical reference for managing risks related to reliability, transparency, privacy, accountability, and fairness. These principles matter in fraud detection because a model that catches more suspicious claims but produces unexplainable or unfair results can create new legal, operational, and reputational risks.
Responsible governance is not a final compliance check. It should shape how organizations select, test, deploy, and monitor AI from the beginning.
Making the Business Case for AI
AI investment should start with a defined healthcare problem before making a technology purchase. For example, a health plan struggling with a particular category of high-cost claims may get more value from a focused pilot than from a broad deployment across every line of business.
Before implementation, healthcare leaders should establish a baseline covering:
Financial performance: Potentially fraudulent payments prevented, confirmed overpayments recovered, and investigation costs.
Operational efficiency: Time to review claims, investigator workload, and the proportion of alerts that lead to substantiated findings.
Provider and member experience: Legitimate claims delayed, disputes raised, and time taken to resolve payment issues.
Risk and accountability: Model accuracy, performance across relevant provider and patient groups, and compliance with governance requirements.
These measures can help separate real value from impressive-looking activity metrics. A system that generates thousands of alerts may look productive but add little practical value without the capacity to resolve them efficiently.
Organizations should run pilots against a defined baseline, compare results with existing methods, and assess the full cost of implementation, integration, oversight, and ongoing maintenance. They should expand only when the evidence supports doing so.
Cross-functional ownership is also essential. Claims operations, fraud investigators, clinical experts, compliance, legal, data teams, and technology leaders must agree on what success looks like and who is accountable for achieving it.
Conclusion: Protect Healthcare Resources Early
AI may not eliminate healthcare fraud. But it can give health plans and other healthcare organizations a stronger way to identify suspicious activity, focus investigative resources, and prevent some losses before payment.
The real advantage will come not from adopting the most sophisticated model, but from combining reliable data, effective workflows, accountable human oversight, and measurable business outcomes. This should focus on a clear objective: protecting healthcare resources while preserving timely access to legitimate care.
Organizations must identify their highest-risk payment areas, establish credible performance baselines, close critical data gaps, and test AI against real operational challenges. They should also ensure that every deployed solution is accurate, fair, secure, and capable of delivering demonstrable value.
Inaction carries its own cost. Existing controls may continue to miss emerging fraud patterns, while fragmented data and manual processes constrain an organization’s ability to respond. The consequences may extend beyond financial losses to include unnecessary administrative burdens, delayed payments, and increased pressure on providers and members.
Healthcare leaders do not need to automate every decision to improve fraud prevention. They need a clear understanding of where current controls fall short and a credible, measurable plan to address those gaps. The question is no longer simply how organizations can address avoidable losses without disrupting legitimate care or payment.
