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Healthcare fraud remains a growing challenge, with health plans increasingly becoming attractive targets for sophisticated fraud schemes. In 2025, the Justice Department reported that the National Health Care Fraud Takedown identified more than $14 billion in intended ¹losses. Large enforcement actions continue to highlight the scale and persistence of fraudulent and abusive activity across the industry, underscoring that no organization is immune.

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How are fraud trends influencing detection and prevention strategies?

Enforcement actions highlight the scale of the fraud problem, but they also reinforce the difficult reality that many schemes are still discovered only after financial damage has already occurred.

Fraud takes many forms, from brazen spike billing to schemes that start more subtly — billing behavior that initially appears legitimate before gradually evolving into larger, coordinated patterns of abuse. The perpetrators adapt, schemes evolve and traditional rule-based detection methods struggle to keep pace.

At the same time, financial pressures across the healthcare system are intensifying. As medical spending continues to rise, fighting fraud is no longer viewed solely as a compliance requirement; it has become a critical lever for cost containment and healthcare affordability.

This shift is also changing how health plans approach fraud detection. Historically, special investigation units (SIUs) have focused primarily on retrospective investigations. These teams bring deep expertise in identifying schemes and gathering evidence, but they often operate after suspicious activity has already occurred.

Payment integrity teams, by contrast, sit directly within the claims process and are supported by large-scale data infrastructure and analytics.

When these capabilities work together, health plans gain a powerful advantage. Payment integrity systems can surface risk signals earlier, while SIU investigators apply their expertise to evaluate emerging schemes and determine the appropriate course of action.

The result is a new prevention mindset that focuses on identifying and stopping fraud as close to the first claim as possible.

How can AI help health plans combat growing pressures?

Solutions driven by AI can integrate data and human expertise into a powerful system to tackle evolving threats before losses mount. AI allows health plans to see patterns, anomalies and relationships across healthcare data that traditional detection methods simply cannot.

Health plans can strengthen their fraud prevention strategy by implementing three core strategies:

1. Stopping fraud at the front door: provider verification

New providers entering a health plan’s system can represent potential fraud risk, so it’s critical to identify which providers can be trusted early in the claims lifecycle. Provider authentication verifies provider identities and evaluates risk indicators before claims are processed.

By shifting these checks upstream, health plans can pause claims from new, unknown providers until authentication reviews are complete. This “shift left” approach prevents fraudulent providers from submitting large volumes of claims before detection occurs.

AI strengthens provider verification by expanding analysis beyond basic credentialing. By combining provider-submitted information with third-party data and historical intelligence, AI can identify:

Unusual demographic patterns
Shared identifiers
Links to previously flagged entities

These insights allow health plans to determine whether a provider should proceed through the claims process, require further investigation or be denied participation altogether.

2. Finding what others miss: AI-powered fraud and abuse detection

Even after providers are verified, fraud risk does not disappear. Many fraud schemes originate from providers who already participate in health plan networks but gradually alter billing behavior over time. Detecting these patterns requires continuous monitoring across large volumes of healthcare data.

A multidimensional suite of AI models can analyze provider behavior across millions of transactions, detecting anomalies and revealing hidden relationships that may signal emerging schemes. These models evaluate multiple dimensions of activity simultaneously:

Comparing providers to peer groups
Identifying unusual billing patterns
Mapping relationships across organizations, addresses and networks

By combining signals from internal claims data, external information sources and historical investigations, AI can amplify subtle indicators of fraud that might otherwise remain hidden. The result is earlier detection of suspicious behavior and higher-quality leads for investigative teams.

3. Turning insight into action: interventions and investigations

Detection alone is not enough. Once potential fraud signals are identified, health plans must determine how to respond. AI-powered insights can translate detection signals into investigation-ready leads, consolidating evidence and prioritizing cases based on risk and potential financial exposure.

Investigative tools and workflows then support analysts in:

Gathering documentation
Reviewing claims
Assessing provider behavior

Depending on the findings, health plans may take a range of actions:

Educating providers
Monitoring billing behavior
Suspending payments
Initiating recoupments
Referring cases to regulators and law enforcement

Supported by experienced investigators, clinicians and coders, these processes help organizations act quickly to contain potential fraud schemes before losses escalate.

How can health plans build a smarter fraud prevention ecosystem?

By combining payment integrity infrastructure, SIU expertise and advanced AI analytics, health plans can detect emerging threats earlier and intervene before losses occur. The goal is not simply to investigate fraud more efficiently, but to prevent fraudulent activity from entering the payment system in the first place.

Preventing fraud protects more than financial performance. It: