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8 Benefits of Insurance Fraud Analytics

November 2, 2021

Insurance claims fraud has been a consistent source of frustration for carriers for decades. Despite their best efforts, insurers are still left paying millions of dollars in fraudulent claims annually. According to the FBI, the total cost of insurance fraud is more than $40 billion per year. Unfortunately, that impact trickles down to consumers, who in turn are paying anywhere between $400 – $700 per year in increased premiums.

Fortunately, there is light at the end of the tunnel. While totally eliminating insurance fraud is highly unlikely, carriers have a new weapon that is changing the game — insurance fraud analytics. Through advanced data technology made possible by digital insurance tools, insurers are finally getting ahead of the criminals executing fraudulent schemes.

Beyond reducing fraud, insurance fraud analytics can help insurers in a number of different functions. Here is the latest on insurance fraud analytics and eight of the biggest benefits of implementing this advanced data technology.

The State of Insurance Fraud 

There are three primary levels of insurance fraud that carriers are regularly combatting:

  1. Individual Fraud: An individual who tries to get more money out of a claim than they are entitled to.
  2. Provider-Driven Fraud: A provider, such as a health care professional, who tries to bill insurers for unnecessary services or services they didn’t actually provide.
  3. Organized Crime Fraud: An orchestrated effort to continually collect claims for fraudulent incidents. Think staged accidents, falsified doctor appointments, and other schemes.

These types of fraud really boil down to opportunistic fraud vs. organized fraud. While both classes of fraud are equally damaging, the way they are handled and tracked by insurers are very different.

Overall, insurers are seeing a consistent increase in all types of fraud, making early and ongoing fraud detection even more critical. The key to early detection is high-quality referrals, which in laymans’ terms means almost certain evidence of fraud. And with insurance fraud analytics, insurance fraud departments are providing a higher number of these high-quality referrals. This, in turn, is helping carriers discover and stay ahead of trends in crime that they would have ordinarily missed.

Evolution of Insurance Fraud Detection

To understand how far insurance fraud detection has come, you first must understand where it has been. Traditional fraud detection models roughly followed these steps:

  1. Identify. Fraud detection teams would identify suspicious claims.
  2. Analyze. Suspicious claims would then be analyzed by special investigation units and claims adjustors.
  3. Recommend. Once a determination is made that a claim is indeed fraudulent, a recommendation would be made to a) deny the claim if it was caught in time, or b) pursue legal action.

This model proved to be very time-consuming, and therefore very costly for insurers, as claims were often largely paid out before the fraud was concretely proven.

Today, insurance fraud analytics is speeding up and improving the accuracy of fraud detection. Leveraging the capabilities of artificial intelligence (AI), machine learning, and predictive modeling, insurers are now able to identify instances of suspicious behavior and proactively protect themselves against fraudulent claims.

These technologies work together to learn over time and automatically flag claims that fit similar patterns of previous fraud. Fraud analytics can even use regionally-specific AI modeling to identify typical incidences of fraud based on laws and schemes common in a particular region. This method of analysis provides a host of benefits over traditional means of fraud detection.

Eight Benefits of Insurance Fraud Analytics

  1. Better assess risk: Simply put, an insurance fraud analytics AI is better equipped to identify risk than any one person. AI and predictive modeling systems can analyze massive amounts of data in fractions of a second, whereas a person would be combing through documents for days before they could identify the same patterns this technology can.
  2. Improve fraud detection: Similar to risk assessment, insurance fraud analytics is more effective at looking for anomalies and red flags that indicate potential fraud schemes. These flags help analytics teams to build high-quality referrals for their fraud teams. These algorithms can also identify high-risk areas that should be included in a fraud risk assessment.
  3. Speed up fraud detection: Fraud analytics greatly increase the speed at which insurers are identifying fraudulent claims or potentially fraudulent claims. This is critical in today’s economy, especially in cases of workers’ compensation (where fraud is increasing). The faster that fraud is identified, the faster insurers can respond and prevent any loss.
  4. Identify low-incidence events: Low-incidence events are the ones that slip through the cracks, and arguably cost insurers the most money. They can be hard to identify, because fraud detection is largely based on patterns and trends in behavior. With fraud analytics programs, outlier events can be more easily flagged and referred to a fraud team for further analysis.
  5. Increase fraud savings: The ultimate goal of fraud detection is to save insurers from incurring fraud-related losses. And when fraud is detected before it is processed, or in a manner that allows carriers to act quickly, less loss is incurred.
  6. Identify new fraud tactics: Unfortunately, just as technology advances to catch up with fraud, criminals discover new tactics that can go undetected. Some common schemes — like seeking small claims amounts that stay under the radar — won’t be caught by off-the-shelf insurance fraud analytics programs. However, advanced AI analytics can be configured to identify new and emerging abnormal claims using machine learning techniques like cluster analysis.
  7. Leverage social networks: Social media is like a free inside view into customer behavior, and insurance fraud analytics helps insurers leverage this valuable resource. Analytics programs can process social media data at a scale and speed beyond anything a human would never be able to do. This data acts as another resource for analytics to build referrals and fraud teams to cite when combatting fraud.
  8. Improves data enrichment: Data enrichment – bringing in additional data sources to inform analytics – is changing the game in fraud detection. Typically, analytics programs relied on singular data sources, which limited how accurate the program could be. But with data analytics programs that utilize diverse data sources, insurers can cover more ground, and therefore are more likely to change outcomes due to higher quality referrals.

No two carriers are built exactly alike, and that goes for their fraud departments as well. Different organizations have varying department sizes, budgets, and other factors that impact how well they can do their jobs. This makes having a robust digital insurance and analytics platform all the more important. These solutions give carriers a more level playing field when it comes to fraud detection, as they allow for plug-and-play fraud analytics that empower carriers to start protecting themselves quickly and effectively.

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Andy Yohn
Andy is a co-founder of Duck Creek Technologies and has been involved in the design and development of the solution offerings of the company. Andy brings a solid mix of technical and business skills together through his 30+ years of working in the insurance automation industry. Andy served as founding Chief Architect of the Duck Creek Platform and currently is actively involved with product management and research and development projects. Prior to Duck Creek, Andy had a thirteen-year tenure with AMS Rating Services. Andy holds a bachelor's degree in Computer Science with minors in Mathematics and Music.