AI Fraud Detection for Healthcare: From Claims Analysis to Real-Time Prevention

Key Takeaways: 

• Fraud isn’t just lurking in claims data. It’s entering voice conversations at unprecedented levels. Legacy fraud detection methods are missing early fraud indicators.
• Behavioral and voice analysis, powered by AI and done in real time, puts your teams in position to catch and stop fraud in progress.

Healthcare fraud is a lucrative crime. In the United States, losses run upwards of $300 billion annually, or as much as 10% of all healthcare expenditures, some estimates say. And according to the United States Sentencing Commission, health care fraud offenses increased 19.7% from fiscal year 2020 to 2024. That number is only increasing as healthcare becomes more complex, data is exploding at an exponential rate, and healthcare fraud is shifting beyond claims into real-time channels like phone and telehealth conversations.

In this post, we’ll discuss where healthcare fraud really happens today, why traditional fraud detection tactics fall short, and how criminals are using conversational channels to take advantage of vulnerabilities in identity and authentication. We’ll also cover how AI, voice and behavioral biometrics are changing healthcare fraud detection from reactive claim analysis to real-time prevention with examples of use cases and features you should be looking for in a modern solution.

In this guide: 

• What is Healthcare Fraud?
• Why Healthcare is a High-Value Target for Fraud
• Common Types of Healthcare Fraud
• Why Does Real-World Fraud Slip Past Traditional Fraud Detection?
• How Voice AI Strengthens Fraud Detection in Healthcare
• Key Use Cases of AI in Healthcare Fraud Detection
• Benefits of AI-Driven Fraud Detection in Healthcare
• Leverage Conversational AI to Stop Healthcare Fraud in Real Time
• Frequently Asked Questions

What is Healthcare Fraud?  

According to the Legal Information Institute (LII), healthcare fraud is “a type of white-collar crime that involves the filing of dishonest health care claims in order to turn a profit,” typically targeting healthcare related institutes like health insurance companies or government programs. Examples can include billing for services not provided, upcoding, misrepresentation of diagnoses, or using another individual’s information to receive health care or medicine.

Aside from stealing money, fraud creates numerous other issues. Fraud leads to increased premiums, drives up operating costs, and can result in unnecessary procedures that can harm patients. At the end of the day, it increases costs for everyone and can cause patients to lose trust in you and their care.

Why Healthcare is a High-Value Target for Fraud

Healthcare transactions happen at an enormous scale. They’re also extremely fragmented across claims systems, EHRs, patient communications, and more. When you factor in how complicated medical billing and coding can be, fraudulent activity has plenty of opportunities to hide in plain sight.

Fraud also benefits from the fact that many bogus transactions will appear identical to legitimate ones once they are input into the medical system. This is especially true for types that match typical provider behavior or imitate patient care.

Identity theft plays a big role, too. It’s easier than ever for criminals to use stolen identities or fabricated synthetic identities to obtain care, file claims, or make changes to patient accounts. Strict privacy laws also prevent verification information from being shared across records or systems in many cases. Toss in skyrocketing growth in telehealth and remote care, and you’ve got an even larger attack surface with less observability.

Common Types of Healthcare Fraud 

Fraud happens in many ways across health care. Some are easy to spot on paper. Others attempt to look like normal activity and occur gradually over time. Below are several common types that you’ll see.

• Billing schemes - Upcoding, double billing, or billing for services not rendered. Can be hidden by using complicated coding or flooding with claims.
• Prescription drug fraud - Forged prescriptions, “doctor shopping,” or modifying medical records to receive controlled substances or pricey medications.
• Identity theft / patient impersonation - Real or synthetic identities used to obtain care, submit claims, or receive prescriptions under someone else’s insurance.
• Provider fraud - Can include, but are not limited to phantom (never provided) services, unnecessary services, overtreatment and excessive testing, or misrepresentations about the type of treatment provided to increase payment from the payor. Overtreatment and excessive testing, in particular, are common, and they’re difficult to detect because they appear clinically justified and align with typical billing patterns. 
• Insider threats - Individuals with current or former ties to the workplace using their access and knowledge to commit fraud.

The Rise of Voice and Interaction-Based Fraud 

Fraudsters aren’t just hiding in claims data. They’re calling call centers, telehealth providers and support teams to launch attacks.

• Social engineering attacks against call centers and support teams. Fraudsters manipulate agents, policies, or urgency to bypass verification and approve payments or other services. These calls can also be used as information gathering and privacy breaches. During these attacks, sensitive information (such as date of birth, insurance information, or account history) is collected that can be used in future attacks such as impersonation or account takeover.
• Phone or telehealth scams where criminals pose as a provider or patient. Calls are made to request medications, change records, or approve services.
• Call center fraud and account takeovers. Call actors use stolen or synthetic credentials to speak with a call center to reset passwords, redirect benefits, or open an account.
• Telehealth scams exploiting weak identity verification. Loose identity verification requirements enable fake virtual visits, false claims, and prescription drug fraud.
• Doctor shopping linked to synthetic or manipulated identities. Individuals acquire prescriptions from multiple providers by using synthetic identities or modifying existing patient records.

Why Does Real-World Fraud Slip Past Traditional Fraud Detection?

Legacy fraud detection looks almost exclusively at claims data via transactions and paper records. They detect variances AFTER transactions have happened and are dependent on known fraudulent patterns. Because of this, they’re reactive by nature and can be slow to identify new fraud schemes. Also, without context, false positives are common. Something can look dodgy on paper but be completely innocent.

What they don’t see is the conversation. Fraud can occur in real time through interactions, and what is said and how it’s said can both be critical. Suspicious activity doesn’t always go in claims. Nor does identity always equal behavior.

Nervous, pressured, robo-like voices are suspect. They may pass verification but then behave in ways that aren’t normal for a legitimate patient/provider.

How Voice AI Strengthens Fraud Detection in Healthcare

Voice AI adds another layer many systems are missing: visibility into the interaction as it’s happening. You can listen to calls and telehealth visits in real time to detect risk before transactions are completed.

Speech pattern detection is one example. It uses things like speech patterns and cadence to identify when someone is behaving in a suspicious or atypical way. Detecting deepfakes or spoofed voices is another important component, especially as AI-generated voices become more realistic and easy to access.

Finally, there’s conversation intelligence, which can detect intent and context. This can determine if someone is being manipulative through urgency, stonewalling, or providing robotic responses that are uncharacteristic for patients or providers. These are nuances that are hard to fake consistently and are unavailable in structured data.

This is an important shift. Fraud detection can go from being reactive to stopping fraud in real time.

Key Use Cases of AI in Healthcare Fraud Detection

AI use cases span the entire fraud lifecycle from verifying identities up front to adjudicating claims back-end. Here are some examples where we’re already seeing tangible, measurable impact:

• Voice biometrics for authentication - Verify the identity of a patient/provider by voice during an interaction. Voice biometrics analyzes unique aspects of speech and voice  to determine the likelihood that a person speaking matches against a known person. This is an additional layer of authentication that works alongside passwords or knowledge-based authentication.
• Call authentication for telemedicine visits - Authenticate the identity of everyone on a call at start and throughout the visit. This prevents strangers from “drop in” on the call and verifies that a patient isn’t claiming to be a provider (or vice versa).
• Social engineering and scam detection - Detect urgent language, suspicious requests, or other “social engineering” tactics used by criminals during active calls so your teams can intervene as it’s happening.
• Impersonation detection - Stop patient and provider spoofing by detecting when a person is claiming to be someone they are not based on how they talk and/or behave and/or through heuristic pattern detection.
• Prescription fraud prevention - Receive alerts on suspicious activity surrounding requests for controlled substances or other types of high-risk medication, particularly when used in conjunction with identity fraud.
• Real-time fraud scoring/alerting - Assign a fraud score to patient/provider interactions as they happen. You can then trigger an alert to your team or kick off an escalation workflow if the risk of fraud passes a certain threshold.
• AI-driven claim review/adjudication - Flag claims using AI to rapidly identify outliers and patterns indicative of inappropriate billing.

Benefits of AI-Driven Fraud Detection in Healthcare

AI powers everyday fraud prevention activities. Take a look at some of the advantages of incorporating behavioral, transactional, and conversational signals into a wider fraud detection framework.

Faster Detection and Response

AI can detect and analyze suspicious behavior in concert with other signals during an interaction, allowing you to increase certainty in classification and take action on risks before they become harmful. Real-time detection and response also reduce the time between fraud happening and it being remediated.

Reduced Financial Losses

The faster fraud is detected, the less you’ll have to pay out on fraudulent claims, account takeovers, and abuse.

Fewer False Positives

Using behavioral and conversational context, you can better identify and validate true risks from false anomalies. Having human readable contextual signals available also means less time investigating false positives.

Scalable to Vast Amounts of Complex Data

AI can consider terabytes of often siloed information from claims data, medical history, and patient interactions with little to no manual effort.

Enhanced Patient-Provider Trust

Patient identity verification and fraud prevention can help ensure private information doesn’t get into the wrong hands.

Streamlined Operations 

An automated fraud prevention process means teams can catch easy-to-spot fraud without diverting employee time from their core competencies to try and do fraud checks.

Leverage Conversational AI to Stop Healthcare Fraud in Real Time 

Modulate’s Velma works differently than traditional fraud detection solutions. Rather than analyzing transcripts or reviewing calls after they happen or relying on singular signals such as Deep Fake Detection, Velma’s ensemble models detect synthetic audio while listening to live conversations and scores them based on what is said and how it’s said. 

By assessing behavioral and conversational cues as they occur, Velma surfaces risk in real-time. This includes identifying deepfake or spoofed voices, detecting pressure tactics and flagging statements that seem inconsistent with a caller’s alleged identity.

Teams gain visibility into emerging risks with real-time risk scoring, alerts, and escalation workflows that can be triggered during a call. Whether that means initiating step-up authentication, holding a transaction or routing the interaction to team members for review, Velma arms teams with actionable insights they need to stop fraudulent calls before they happen. Contact our team today to learn how Modulate can help your healthcare business prevent fraud.

Frequently Asked Questions

How does Artificial Intelligence (AI) identify healthcare fraud?

There are several different signals AI can use to detect different aspects of fraud. Some AI models can identify if audio has been generated or edited. Other AI models can look at underlying transactional patterns and identify when unusual or suspicious transactions are occurring. Other models look at behavioral patterns and try to identify known malicious or suspect patterns combinations aspects like tone, timing, content, and implied intent.

What are some examples of healthcare fraud?

Some examples of healthcare fraud include billing fraud, prescription fraud, identity theft, provider fraud, call center fraud, and telehealth imposters.

Why do we need AI and voice analytics to detect fraud?

Typical fraud detection solutions focus on reviewing structured transactional data after the activity has occurred. They fail to detect behavioral clues, intent, and manipulation that takes place during a conversation.

How does voice AI help stop healthcare fraud?

Voice AI can listen to calls as they’re happening, detect risk indicators such as impersonating a provider, using social engineering, or speaking too fast, and alert your teams to act while the call is happening.

What to look for in AI healthcare fraud detection?

Look for fraud detection that includes a diverse set of approaches, including synthetic audio detection and behavior analysis, to identify fraud signals during a conversation (such as voice AI). You want the capability to work in real-time, have low false positives, and technology that seamlessly integrates with your current ecosystem.