Healthcare fraud detection with AI: technologies, fraud analytics, and benefits

Healthcare fraud continues to create billions of dollars in losses, with recent U.S. enforcement actions highlighting the scale and complexity of the problem. In 2026, the Department of Justice and the U.S. Department of Health and Human Services announced charges related to healthcare fraud schemes involving more than $6.5 billion in alleged false claims. As fraud schemes become more sophisticated, healthcare organizations are turning to AI, Machine Learning, and fraud analytics to identify suspicious patterns, prevent fraudulent claims, and protect patient data.

According to a Centers for Medicare & Medicaid Services report, U.S. healthcare spending is growing by an average of 2.7% annually and has reached $4.3 trillion. This makes effective fraud detection increasingly important for redirecting resources from fraud-related losses toward better patient care.

This article explores the main types of healthcare and pharmaceutical fraud and examines how modern technologies, including AI and healthcare fraud analytics, help organizations detect and prevent fraudulent activities.

Types of healthcare fraud schemes shaping the fraud detection market

Healthcare fraud schemes are becoming increasingly complex, affecting providers, insurers, and patients through fraudulent claims, identity theft, prescription abuse, and other forms of fraud and abuse. As organizations face more sophisticated threats, the demand for fraud analytics solutions is growing, enabling healthcare companies to strengthen fraud detection and prevention efforts (see Fig. 1).

Recent enforcement actions highlight the scale and variety of healthcare fraud schemes. In January 2026, affiliates of Kaiser Permanente agreed to pay $556 million to resolve allegations that they submitted invalid diagnosis codes to increase Medicare Advantage payments, demonstrating how complex fraud and abuse cases can become. To better understand the scope of healthcare and pharmaceutical fraud, it is important to focus on four key areas: fraud committed by medical providers, fraud committed by patients, fraud involving prescriptions, and fraud in pharma.

Fraud committed by medical providers

Provider fraud occurs when healthcare professionals or organizations manipulate claims, billing information, or medical records to receive improper payments. These healthcare fraud schemes are among the most common targets for fraud analytics solutions, which help organizations identify suspicious patterns and strengthen detection and prevention efforts.

• Double billing. This practice involves submitting multiple claims for the same service or procedure. For example, a provider may perform one laboratory test but bill an insurer several times, resulting in unnecessary payments and increased healthcare costs.
• Phantom billing. Phantom billing occurs when providers charge for services, procedures, or medications that were never provided. It may also involve billing for a more expensive service than the one actually delivered.
• Unbundling. This fraud scheme involves billing separately for services that should be submitted together under a single discounted rate. Providers may use this approach to inflate reimbursement amounts.
• Upcoding. Upcoding occurs when providers submit claims for more complex or costly services than those a patient actually received. For instance, a routine examination may be billed as a more comprehensive visit to receive higher reimbursement.

These fraudulent practices primarily target insurers and government healthcare programs, but their financial impact extends to patients through higher healthcare costs. With robust fraud detection technologies, organizations can identify unusual billing patterns earlier and reduce losses caused by healthcare fraud and abuse.

Fraud committed by patients and other individuals

Healthcare fraud is not limited to providers and organizations. Patients, scammers, and other individuals can also commit fraud by misusing personal information, exploiting healthcare systems, or impersonating legitimate professionals. The most common healthcare fraud schemes in this category include:

1. Bogus marketing. This type of fraud involves deceptive practices designed to obtain a person’s health insurance identification number or Protected Health Information (PHI). Scammers may pose as healthcare representatives and convince individuals to share sensitive information, which can later be used to submit false claims or enroll them in fraudulent health plans. Overall, bogus marketing is part of the broader digital fraud landscape and contributes to growing concerns around healthcare data protection.

2. Identity theft and identity swapping. This type of healthcare fraud involves using another person’s health insurance information without authorization. It often occurs through unauthorized access to health data or data breaches and can result in fraudulent claims, inaccurate medical records, financial losses, and higher insurance costs for affected individuals.

3. Impersonating a healthcare professional. This fraud scheme involves individuals posing as licensed healthcare providers to submit fraudulent claims or provide unauthorized services. Fraudsters may pretend to be doctors, nurses, or other medical professionals, sometimes even creating fake clinics. Without effective fraud detection tools, organizations may struggle to verify whether claims come from legitimate providers or fraudulent actors.

While provider fraud typically targets insurers and healthcare programs, patient and individual fraud directly affects people through financial losses, compromised personal information, and identity-related risks. Addressing these forms of fraud and abuse requires robust detection and prevention measures across the healthcare ecosystem.

Fraud involving prescriptions

Prescription fraud is a type of healthcare fraud that involves creating, altering, misusing, or illegally distributing medical prescriptions. This fraud category sits at the intersection of healthcare and pharmaceutical fraud and can affect patients, healthcare providers, pharmacies, and insurers.

• Forgery. Prescription forgery involves altering or creating a prescription without proper authorization. Patients and medical professionals alike can commit this type of fraud by modifying prescription details or completely falsifying medical documents to obtain medications illegally. Recent enforcement actions highlight the risks associated with prescription fraud: in 2026, a pharmacy owner was sentenced for unlawfully dispensing controlled substances and defrauding healthcare programs, resulting in more than $1.4 million in restitution.
• Diversion. This type of pharmacy fraud involves diverting legal prescriptions for illegal uses. Essentially, it means prescribed medications are redirected from their initially intended and legal use to an illegal one through unauthorized channels. For example, a pharmacy technician might steal medications from inventory and sell them on the side. In another case, a patient with a legitimate condition may receive legally prescribed controlled substances and then sell them to third parties.
• Doctor shopping. Finally, this type of healthcare fraud involves a person visiting multiple medical providers or hospitals to obtain prescriptions for controlled substances that are later resold or used above the prescribed dosage. Doctor shopping is usually done within a short timeframe. A person with an opioid addiction might visit multiple doctors in a single month and try to obtain prescriptions for opioids, stimulants, and/or benzodiazepines. Later, these controlled substances may be used to support personal addiction or sold on the black market.

All in all, prescription fraud involves significant financial and patient safety risks caused by forged, misused, or illegally distributed medications. As these healthcare fraud schemes become more complex, organizations increasingly rely on fraud analytics solutions and robust fraud detection technologies to identify suspicious patterns and strengthen detection and prevention efforts.

Fraud in pharma

Last but not least, there is simply pharmaceutical fraud. Proper healthcare fraud detection involves dealing with the massive scope of pharma-related fraud. Because of this problem’s scope, there are at least eight types of this fraud:

1. Kickbacks. These are illegal payments offered by pharma companies to medical professionals or individual pharmacists in exchange for particular drug promotions. Kickbacks lead to biased decisions, thus affecting the quality of patient care. For instance, several years ago Insys Therapeutics, an opioid manufacturer, was found guilty of offering kickbacks to healthcare professionals who prescribed the manufacturer’s fentanyl-based painkiller.
2. Off-label marketing. This pharmaceutical fraud includes promoting pharma products not approved or regulated by the FDA. Off-label marketing is dangerous and can result in people taking these drugs only to discover some serious and/or undocumented side effects.
3. Billing for the brand. This practice encompasses charging governmental healthcare programs for particular brand-name medications while cheaper and more generic alternatives are available. Billing for brands leads to healthcare professionals, insurance companies, and patients paying more when they could have paid less. Moreover, this type of pharmaceutical fraud is directly related to phantom billing, a type of healthcare fraud mentioned previously.
4. Best price fraud. This fraudulent activity occurs when a pharma company misrepresents the product price when working with programs like Medicaid. Best price fraud is purely motivated by profit maximization. While legally defined as a white-collar crime, there are numerous examples when major pharma companies engaged in this practice. Perhaps, the most prominent example occurred with TAP Pharmaceuticals Products Inc., who paid $875 million for inflating the prices for Lupron, a prostate cancer drug.
5. Current Good Manufacturing Practice (CGMP) violations. CGMP ensures that pharma products follow quality standards within each step of the production process and supply chain. When pharma companies fail to adhere to CGMP regulations, it leads to defective and contaminated products. This could potentially cause harm to end-users.
6. Medicaid price reporting fraud. This type of pharmaceutical fraud involves companies providing inaccurate drug pricing information to government healthcare programs, resulting in incorrect reimbursement amounts and increased healthcare costs. In 2026, Ahold Delhaize USA agreed to pay $40 million to resolve allegations that it reported inflated prescription drug prices on claims submitted to federal healthcare programs.
7. Improper payments and promotional influence. This type of pharmaceutical fraud involves companies providing improper financial incentives to healthcare professionals or organizations to influence prescribing decisions. These practices can create conflicts of interest and affect patient care. In 2026, Takeda Pharmaceuticals agreed to pay $13.6 million to resolve allegations that it used improper payments to induce healthcare providers to prescribe its antidepressant medication, Trintellix.
8. Manufactured compound drug fraud. Finally, here is a type of pharmaceutical fraud that encompasses pharma companies manipulating or altering drug compositions or pricing of combined medications. These are often custom-made medications for patients with specific conditions. In these cases, the parties involved in this type of fraud try to maximize profits from distinct and rare conditions.

The pharmaceutical fraud schemes outlined above involve attempts to manipulate drug production, distribution, pricing, and promotion processes for financial gain. By analyzing large volumes of healthcare data, fraud analytics solutions can help organizations identify suspicious patterns and strengthen robust fraud detection capabilities across pharmaceutical operations.

AI-based technologies serving as healthcare fraud detection

After covering the multiple types of healthcare and pharmaceutical fraud, it’s time to speak about fraud detection healthcare measures. In general, technological tools, like Artificial Intelligence (AI), have already proven themselves to be worthwhile when it comes to mitigating risks, addressing fraudulent activities, and anticipating errors. AI in the healthcare market is booming, yet despite that, there is an even greater need for AI-based fraud detection in healthcare than ever before (see Fig. 2).

Currently, fraud detection technologies have made massive breakthroughs. It is all fueled by data-driven innovations in cloud computing, data mining, Machine Learning (ML), and data analytics. These are all parts of the AI prodigy. When used correctly, they serve as effective fraud detection and prevention mechanisms.

Now, it is time to understand how fraud prevention in healthcare actually works. The basic mechanism involves teaching AI how to engage in fraud detection procedures. This comes with the following steps:

1. Experts standing behind the AI-tool set specific rules and establish control over them.

2. Data analytics mechanisms are automated, anomalies are flagged, and all decisions are supported by a human data-driven approach.

3. An AI-tool learns from data sets and improves its fraud detection capabilities.

4. The next step is to double-check the flagged anomalies in order to distinguish between simple mistakes and fraud-backed anomalies.

With this basic understanding of the fraud detection mechanism, it is possible to develop an AI-tool that is comprehensive enough to detect even the most minimal traces of fraud. However, when speaking of measures to prevent fraud in heatlhcare in the first place, the following technologies can also be implemented:

1. Predictive modeling that uses predictive analytics, data mining, and quantitative analysis to detect fraudulent patterns in their germination stages.

2. AI-based pattern recognition to automate the process of detecting billing errors, which can help prevent a majority of the fraud committed by medical professionals.

3. Biometrics recognition includes iris scanners, facial recognition, and fingerprint scanning so as to improve and secure the patient identification process.

At the moment, the use of big data and AI is helping to push forward innovative fraud detection healthcare mechanisms. The power of ML and deep learning that come along with AI help provide a holistic view of interdependencies within a network under investigation. In simpler words, AI-based tools can help make sense of a myriad of healthcare-related data so as to recognize the patterns defined as fraudulent activities. Equipped with these tools, there is a higher chance to detect and prevent fraud in healthcare and pharma before any substantial damage is done.

How AI-based fraud detection improves healthcare payment integrity

AI-based fraud detection helps healthcare and pharmaceutical organizations strengthen payment integrity by identifying suspicious patterns, reducing manual work, and improving fraud detection and prevention processes.

• Reduced time that healthcare staff members need to invest in processing data and doing manual tasks. A hospital can adopt an AI-powered system to automatically go through patient records to flag potential fraudulent activities like double billing, unbundling, and upcoding. Respectively, with such a system in place, staff do not need to go through the records manually.
• Higher rate of real-time fraud detection. AI-based tools analyze data in real time. In doing so, fraudulent patterns and suspicious activities can be detected in real-time. As a result, an organization employing the tool can take immediate action to deal with the fraud at hand.
• Saving costs and reputation. This is an obvious advantage. Considering all the financial losses due to healthcare and pharmaceutical fraud, implementing AI-based technology saves the money associated with all the resources spent on legal proceedings, reimbursement of fraudulent claims, and internal investigations. Besides, by having effective fraud detection measures installed, an organization protects itself from potential reputational damages. And, it also leads to a more secure and trustworthy environment being created.

Overall, the advantages of AI-based fraud detection in healthcare are numerous. From a higher degree of automation to a preserved reputation, the more protected you are against fraud, the lower the chance of fraud-related issues occurring.

Concluding remarks

The alarming growth rate of data in healthcare makes it even harder to fight fraud. However, when appealing to AI tools grounded in big data, ML, deep learning, and predictive analytics, data can actually work for the benefit of the parties involved. Tech instruments can learn from the data and flag fraudulent activities as soon as they occur. Therefore, currently fraud detection in healthcare is a must, and there is not a single company operating in the industry that can afford to avoid their use.

When working with AI-based fraud detection, one needs to adopt the correct operational results. Moreover, with the growing complexity of fraudulent activities and more data being generated, it is also important to constantly review and adjust patterns at the core of any fraud detection mechanisms.

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Oleksandr Kryvenets

Business Consultant