8 benefits of insurance fraud analytics

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 from $400 to $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.

Evolution of insurance fraud detection

Traditional fraud detection models are very time-consuming and 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 behaviors 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 that would take a person days to comb through before identifying the same patterns.
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 build high-quality referrals for their fraud teams. The 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 fraud is identified, the faster insurers can respond and prevent any loss.
4. Identify low-incidence events: Low-incidence events can 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.

Andy Yohn is the vice president of product management and co-founder of Duck Creek Technologies. Contact him at andrew.k.yohn@duckcreek.com.

Related:

Machine learning: Practical applications for the insurance industry

Striking the perfect balance: How insurers need to think about AI