Healthcare fraud has always been costly. Today, it is also moving faster.

As bad actors adopt more sophisticated technology and exploit gaps between disconnected systems, traditional fraud detection methods are struggling to keep pace. Reviewing claims after payment, investigating suspicious activity across siloed data, and relying on manual handoffs can leave healthcare organizations reacting long after funds have left the system.

The Centers for Medicare & Medicaid Services (CMS) is demonstrating a more proactive model. As Guidehouse recently reported, CMS is using artificial intelligence to score claims by risk, identify suspicious submissions, and intervene before questionable payments are released. The agency is also exploring agentic AI that could recommend specific actions using policy, provider history, prior decisions, and expected outcomes—while keeping people responsible for the final judgment.

For healthcare leaders, the larger lesson is not simply that AI can detect anomalies. It is that effective fraud prevention requires a connected operating model capable of turning an alert into timely, governed action.

That is where Quandary Consulting Group and Workato can help.

The High Cost of Reactive Fraud Detection

Guidehouse describes the older fraud-recovery model as “pay and chase”: a claim is paid, suspicious activity is identified later, and investigators attempt to recover the funds. The weakness is straightforward. By the time an investigation is complete, the money—and sometimes the fraudulent entity—may be gone.

CMS faces this challenge at extraordinary scale. According to Guidehouse, Medicare fee-for-service processes approximately 4 million to 5 million claims each day, with only a 14-day electronic payment window. Within that narrow timeframe, the agency must distinguish legitimate reimbursement from claims that may indicate fraud, waste, or abuse.

CMS is responding with AI-enabled risk scoring and multidisciplinary review. Its Fraud Defense Operations Center brings together attorneys, analysts, investigators, contractors, and agency partners to examine high-risk cases and determine whether to suspend payment, place a provider under prepayment review, or escalate the matter. Guidehouse reports that this model helped suspend approximately $2 billion in potentially fraudulent payments during the center’s first year.

The implication for payers, providers, and other healthcare organizations is significant: detecting risk is only one part of the solution. Organizations must also deliver the right evidence to the right people quickly enough for them to act.

Fraud Detection Is a Workflow Problem, Not Just a Data Problem

Many healthcare organizations already possess valuable fraud signals. Claims platforms, electronic health records, provider enrollment systems, payment applications, analytics tools, and third-party data sources can all reveal unusual behavior.

The problem is that these signals often remain trapped inside separate systems.

An analytics model may identify an anomalous claim, but the supporting provider history lives elsewhere. An investigator may need to request records manually, search several databases, consult a policy document, and wait for an approval before taking action. Every additional handoff consumes time and creates another opportunity for evidence, context, or accountability to be lost.

AI alone does not repair that fragmentation. A model can assign a risk score, but it still needs a secure and auditable process around it. The organization must be able to gather supporting data, explain why the case was flagged, route it according to policy, preserve human oversight, record the decision, and initiate the appropriate downstream response.

In other words, healthcare organizations need orchestration—not another isolated tool.

How Workato Can Turn Fraud Signals Into Coordinated Action

Workato’s healthcare automation platform is designed to connect systems, processes, and data across the healthcare ecosystem. With the right architecture and governance, it can serve as the orchestration layer between fraud analytics and the teams responsible for investigation, compliance, payment, and provider management.

A Workato-enabled fraud prevention workflow could:

• Ingest a high-risk alert. A claims system, fraud model, or analytics platform identifies an unusual billing pattern and triggers a governed workflow.
• Enrich the case automatically. Workato retrieves authorized information from relevant systems, such as provider enrollment data, claims history, payment records, prior investigations, or credentialing details.
• Assemble an evidence package. The workflow consolidates the risk score, supporting records, policy context, and reason for the alert into a structured case for review.
• Route the case intelligently. Based on financial exposure, risk level, claim type, jurisdiction, or other criteria, the case is assigned to the appropriate investigator or multidisciplinary team.
• Require human approval for sensitive decisions. Payment suspensions, provider actions, referrals, and other consequential steps can remain behind configurable approval gates.
• Execute the approved response. Once authorized, Workato can initiate the next step across connected systems, notify stakeholders, update case records, and track deadlines.
• Preserve an audit trail. Activity logs and workflow records provide visibility into what information was used, who approved the action, and what occurred next.

This approach does not ask AI to make unchecked accusations or final determinations. It uses automation to reduce administrative delay, present decision-makers with stronger context, and help them respond consistently.

Curious if you are at risk? We recommend to our clients to download the Risk Assessment Template, created by the Centers for Medicare & Medicaid Services Center for Program Integrity

From Risk Scoring to Agentic AI

The next evolution goes beyond identifying a suspicious claim. Guidehouse notes that CMS is exploring agentic AI capable of recommending an action in real time based on policy, enforcement history, provider behavior, and likely outcomes.

That possibility is compelling, but it also raises the stakes for governance. Recommendations must be explainable. Access must be limited. Sensitive actions must have appropriate approval thresholds. Teams must be able to reconstruct how a recommendation was produced and determine whether the system is performing reliably across different providers and populations.

Quandary’s work with Workato and agentic automation centers on that operational foundation. Rather than adding an AI tool beside the existing technology stack, Quandary Consulting Group helps organizations connect applications, redesign workflows, define human-in-the-loop controls, and align automation with measurable business outcomes.

For fraud prevention, that can mean building agents and workflows that help investigators:

• Prioritize cases by risk and potential financial exposure.
• Summarize relevant evidence from approved data sources.
• Compare suspicious activity with historical patterns.
• Recommend a policy-aligned next step.
• Request authorization before consequential actions.
• Monitor a case through investigation and resolution.
• Feed outcomes back into reporting and continuous improvement.

The goal is not autonomous enforcement. It is faster, more consistent, and better-supported human decision-making.

Quandary Helps Build the Operational Path

Guidehouse’s analysis offers a valuable view into CMS’s evolving fraud strategy and the growing role of predictive and agentic AI. Quandary approaches the same challenge from an implementation perspective: How can an organization translate that vision into secure, connected workflows that work inside its existing environment?

That distinction matters. A successful fraud prevention program depends on more than selecting an AI model. It requires process design, integration architecture, data governance, security controls, change management, performance measurement, and continuous optimization.

Quandary helps healthcare organizations address those practical requirements by:

• Mapping the current fraud detection and investigation process.
• Identifying high-value opportunities for earlier intervention.
• Connecting claims, provider, clinical, financial, and case-management systems.
• Implementing Workato workflows around existing technology investments.
• Establishing role-based access, approval gates, and auditability.
• Designing human-in-the-loop processes for high-impact decisions.
• Defining metrics such as time to review, prevented loss, false-positive rate, investigator capacity, and case resolution time.
• Improving automations as policies, threats, and operational needs evolve.

This allows organizations to modernize without assuming that every legacy platform must be replaced at once.

Start With a Governed, High-Value Use Case

Healthcare organizations do not need to automate the entire fraud lifecycle on day one. A phased approach can establish trust and demonstrate value while limiting operational risk.

A strong first use case is typically high-volume, measurable, and constrained enough to govern effectively. Examples might include enriching high-risk claim alerts, screening provider enrollment data, routing cases by exposure, assembling evidence for investigators, or monitoring unresolved actions and deadlines.

Before implementation, leaders should define:

• The business outcome the workflow is expected to improve.
• The systems and data the process truly needs.
• Which decisions AI may recommend and which remain exclusively human.
• The conditions that require escalation or secondary review.
• How the organization will test accuracy, bias, security, and reliability.
• Which operational and financial metrics will determine success.

This foundation helps ensure that automation accelerates responsible action instead of magnifying a flawed process.

Build Fraud Prevention That Moves as Fast as the Threat

CMS’s evolving strategy reflects a broader reality: healthcare organizations can no longer rely solely on retrospective investigation. Effective program integrity increasingly depends on finding risk sooner, coordinating expertise faster, and acting before preventable losses occur.

AI can identify what deserves attention. Workato can connect the systems and orchestrate the response. Quandary can help design and implement the secure, human-centered operating model that brings those capabilities together.

The result is not simply better fraud detection. It is a more proactive, explainable, and resilient approach to protecting healthcare resources.

Ready to explore a smarter approach to healthcare fraud prevention? Schedule a call with Quandary Consulting Group to help identify the workflows, integrations, and governance controls that can move your organization from reactive investigation to coordinated prevention.

Additional Resources

• AP | The Trump administration expands its use of AI in the hunt for healthcare fraud
• Workato’s Healthcare Automation Platform
• CMS Fraud Prevention System
• Guidehouse : How CMS is using AI to stop healthcare fraud
• Centers for Medicare & Medicaid Services; Medicare Fee-for-Service Compliance Programs
• CPI Crushing Fraud Q1 2026 Facts, Figures, & Accomplishments
• Centers for Medicare & Medicaid Services Center for Program Integrity Risk Assessment Template

Top FAQs about AI Detected Healthcare Fraud

1. How does AI detect healthcare fraud?

AI detects potential healthcare fraud by analyzing claims, provider behavior, billing patterns, patient data, and relationships between entities. Machine-learning models can identify anomalies such as duplicate billing, medically unlikely services, unusual utilization, upcoding, phantom claims, or providers whose behavior differs significantly from their peers.

AI identifies risk rather than proving fraud. Flagged claims should be supported by explainable evidence and reviewed by qualified investigators before consequential action is taken. CMS uses predictive analytics and machine learning to generate alerts, prioritize leads, and support investigations.

2. How is CMS using AI to prevent healthcare fraud?

The Centers for Medicare & Medicaid Services uses AI-enabled risk scoring to identify high-risk claims and determine which submissions may require review before payment. CMS combines these fraud signals with multidisciplinary human review through its Fraud Defense Operations Center.

According to Guidehouse, the center helped suspend approximately $2 billion in potentially fraudulent payments during its first year. CMS is also exploring agentic AI that could recommend actions based on policy, provider history, previous enforcement decisions, and expected outcomes. Guidehouse’s CMS fraud-detection analysis

3. What is the difference between fraud detection and fraud prevention in healthcare?

Healthcare fraud detection identifies claims, providers, or billing patterns that may indicate fraud, waste, or abuse. Fraud prevention goes further by intervening before an improper payment is completed.

Traditional “pay-and-chase” programs investigate suspicious activity after payment and then attempt to recover the funds. A preventive model uses prepayment analytics, automated case enrichment, human review, and governed workflows to stop or examine high-risk claims before the money leaves the system.

4. What types of healthcare fraud can AI identify?

AI can help identify patterns associated with:

• Duplicate or phantom billing
• Billing for medically unnecessary services
• Upcoding and modifier abuse
• Unusual service volume
• Provider identity fraud
• Collusion and suspicious referral networks
• Inconsistent patient or provider information
• Services billed after a beneficiary’s death
• Abnormal geographic or temporal billing patterns

These signals are indicators for investigation, not automatic proof that fraud occurred.

5. What is agentic AI in healthcare fraud detection?

Agentic AI refers to AI systems that can pursue a defined objective, collect information from authorized sources, evaluate context, recommend next steps, and coordinate tasks across connected applications.

In healthcare fraud detection, an AI agent might assemble evidence, compare a claim with provider history, summarize the reason for a risk alert, recommend a policy-aligned response, and route the case for approval. Human investigators should retain control over payment suspensions, provider sanctions, referrals, and other high-impact decisions.

6. Can AI stop fraudulent healthcare claims before they are paid?

Yes. AI can score claims before payment and flag high-risk submissions for automated edits, payment holds, or human review. CMS has used predictive analytics to move from retrospective “pay and chase” investigations toward prepayment fraud prevention.

The effectiveness of this approach depends on data quality, model accuracy, explainability, timely case routing, and the organization’s ability to act within its payment window. AI detection must therefore be paired with connected and well-governed operational workflows.

7. How can Workato support healthcare fraud detection?

Workato can connect fraud-detection models with claims platforms, provider systems, case-management tools, financial applications, databases, and communication channels. When a model flags a high-risk claim, Workato can orchestrate the next steps by gathering authorized evidence, creating a case, routing it to an investigator, requesting approval, updating connected systems, and recording the outcome.

Workato does not have to replace an organization’s fraud model. It can provide the integration and workflow layer that turns a risk signal into a coordinated, auditable response.

8. What would a Workato fraud-investigation workflow look like?

A Workato-enabled healthcare fraud workflow could:

• Receive a high-risk claim alert.
• Retrieve authorized claims and provider information.
• Compile a structured evidence package.
• Assign the case based on risk and financial exposure.
• Request human review or approval.
• Initiate the approved payment, escalation, or investigation action.
• Update the case-management system.
• Preserve a record of actions and decisions.

This approach can reduce repetitive evidence gathering while keeping investigators in control.

9. Can Workato integrate with existing healthcare systems?

Workato can integrate applications, APIs, databases, and automated workflows across a healthcare technology environment. Depending on available connectors and APIs, this may include claims platforms, EHRs, payer systems, provider databases, CRM platforms, ERP systems, data warehouses, and case-management tools.

A phased integration strategy can help organizations modernize fraud operations without immediately replacing every existing system.

10. How can healthcare organizations reduce false positives in AI fraud detection?

Organizations can reduce false positives by combining multiple risk signals, comparing providers with appropriate peer groups, incorporating clinical and policy context, monitoring model performance, and requiring human review before high-impact actions.

Investigators should also receive an understandable explanation of why a claim was flagged. Confirmed outcomes should be captured and used to improve models, rules, thresholds, and workflows over time.

11. Is AI healthcare fraud detection HIPAA compliant?

AI fraud detection is not automatically HIPAA compliant simply because a particular platform or model is used. Compliance depends on how the complete solution is contracted, configured, secured, integrated, and governed.

A healthcare implementation may require appropriate agreements, role-based access, data minimization, encryption, audit logging, retention controls, secure integrations, and documented human-approval requirements. Organizations should involve their privacy, security, compliance, and legal teams before deployment.

12. Will AI replace healthcare fraud investigators?

AI is more likely to augment healthcare fraud investigators than replace them. It can automate repetitive work such as collecting records, checking data, summarizing evidence, prioritizing cases, and documenting activity.

Human investigators remain essential for interpreting ambiguous evidence, evaluating clinical context, protecting legitimate providers, making consequential decisions, and coordinating with legal or regulatory authorities.

13. What are the benefits of automating healthcare fraud investigations?

Automating appropriate parts of a healthcare fraud investigation can help organizations:

• Review high-risk cases faster
• Reduce manual evidence gathering
• Improve case consistency
• Prioritize greater financial exposure
• Strengthen auditability
• Monitor deadlines and unresolved actions
• Increase investigator capacity
• Intervene before suspicious payments are completed

Results depend on the selected process, integration quality, governance, and continued human oversight.

14. How should healthcare organizations measure AI fraud-detection ROI?

Healthcare organizations should measure both financial and operational performance. Useful metrics include:

• Potential improper payments prevented
• Investigation time per case
• Time from alert to decision
• False-positive and false-negative rates
• Cases reviewed per investigator
• Payment recovery rate
• Provider appeal outcomes
• Model precision and recall
• Automation error rate
• Percentage of cases requiring manual rework

Organizations should establish a baseline before implementation and monitor results by claim type, provider group, and workflow.

15. How can Quandary help implement AI-powered healthcare fraud prevention?

Quandary Consulting Group can help healthcare organizations assess fraud workflows, identify high-value automation opportunities, connect existing systems, and implement governed Workato automations.

The engagement can include integration architecture, process redesign, human approval controls, AI-agent orchestration, auditability, KPI development, change management, and continuous optimization. The objective is to transform disconnected fraud alerts into secure, measurable, and actionable investigation workflows.