How To Use AI Technology for Fraud Prevention in Healthcare

AI or artificial intelligence is popular among various industries not only in the fields of movies but as well as in businesses and the internet. In fact, AI is now also becoming a useful tool in the healthcare industry since it can process huge quantities of data in a matter of minutes or even less. And both healthcare professionals and law enforcers are using this technology to detect or even prevent healthcare fraud.

AI Technology for Fraud Prevention

AI Technology for Fraud Prevention

What is healthcare fraud?

Healthcare fraud is considered a type of white-collar crime. The process involves filing fraudulent health care claims just for profit sake. Unfortunately, a lot of healthcare fraud cases come from dishonest healthcare providers. Below are some of the examples of their fraudulent health care schemes.

  • They’ll alter their patient’s medical records so they can submit a higher amount for claims.
  • Perform unnecessary services with their patients to generate higher insurance payments
  • Accepting payments for any patient referrals
  • Falsifying diagnosis just to justify the medical procedure that isn’t actually necessary
  • Billing for claims that were actually never rendered on their patient.
  • Billing for expensive procedures and services
  • Billing for higher-priced treatment than the actual procedure
  • Unbundling procedures or billing for bundled procedures as if they were separate procedures
  • Waive their patient’s deductibles then overbill the insurance provider

Those above are just some of the fraudulent procedures that some healthcare providers do to obtain insurance payments. However, the Federal Bureau of Investigations or the FBI is handling every case to help fight and reduce healthcare insurance fraud in the country.

How can artificial intelligence help prevent healthcare fraud?

The healthcare industry can generate a huge amount of data daily coming from every patient’s data and diagnoses to diagnostic imagery where all of them are digitally stored on hospital servers. Artificial intelligence has the capability to categorize, sort, and analyze all of those data and information within a few seconds and more accurately, too, compared to human processing.  Since AI have almost a limitless memory and unmatched processing capability, they can provide more detailed data than any human can do. They can even give the patient’s medical history and any related data and patterns. With this, it would be easier to detect if there’s an anomaly in a healthcare data submitted for insurance claims.

Types of AI that detects health insurance fraud

Below we have listed some of the most used AI to analyze and detect fraud in an insurance claim.


The Friss uses predictive analytics to help healthcare providers to detect fraudulent claims. The AI collects data using various programming approaches to identify a fraudulent scheme that included image screening, geo-mapping, mining, and even social media analysis. It can provide a detailed analysis of why an insurance claim might is more likely a fraud. The developers of Friss make sure that the software was trained to detect fraudulent claims by injecting hundreds of thousands of insurance claims to learn from all those data and enhance its learning algorithm. With this, it can effectively discern specific data points that correspond to a fraudulent healthcare claim.

According to the study, Anadolu Sigorta, a private insurance company, was able to save more than $5 million by detecting fraudulent healthcare claims using Friss.\


SAS provides the AI called SAS Enterprise Miner also uses predictive analytics to help health insurance provider to detect fraudulent claims. The software is packed with thousand of insurance records that are marked as fraudulent. Those data will then be compared to the patient’s data who wish to make an insurance claim. With this, the program can easily tell if the claim is a fraud, suspicious, or legitimate. However, if there are new fraudulent ways to claim healthcare insurance, the software needs to be updated beforehand. DentaQuest has been using SAS for many years now, and they successfully identified over 50 customers with behavioral patterns linked to fraudulent claims. provides a machine learning platform that helps healthcare providers create their very own artificial intelligence software. However, we highly suggest trying the Driverless AI, an automated machine learning platform. The AI, similar to those two above, still needs to be trained by injecting thousands of data points in order to perform. These data points are also labeled as fraudulent or suspicious, so the AI algorithm can have a more straightforward process of detecting them. The learning process of is also limitless, and it can develop new fraud methods based on its user feedback if the AI provides certain flags.


LexisNexis is also called Relationship Mapping AI. The AI works by using different sources in order to find abnormal behavioral patterns associated with fraudulent claims. With this, the insurance provider can identify risky clients. It uses the “relationship data” approach since it gathers data from suspicious social groups, affiliations, and connections associated with their client, patients, and even employees within the client healthcare network to identify possible fraudulent behavior.

Final Thoughts

Most notably, Bell obtained the largest verdict in the United States in 2017 and the ninth (9th) largest verdict in United States history against JPMorgan Chase Bank for in excess of $6,000,000,000 (6 Billion Dollars).

Bell has become a recognized legal thought leader through projects such as co-authoring an article titled “Piercing the Corporate Veil” regarding property division in divorce and features in publications such as Forbes, Inc., and Entrepreneur and has been granted recognitions such as Best Personal Injury Attorney and Litigator of the Week.

Bell is in admission with the Bar in the States of Texas, California, and New York, and obtained his undergraduate and law degrees from Southern Methodist University. He continues to serve in a wide breadth of cases, including but not limited to healthcare disputes; Qui Tam litigation; white-collar criminal defense; catastrophic injury; ERISA; business fraud; bankruptcy; professional negligence/malpractice; oil & gas; complex securities disputes; divorce; child custody; and real estate fraud cases.