Battling Fraud With Automation
According to the FBI, the total cost of insurance fraud (not related to health) is more than $40 billion a year.
This costs the average family in the U.S anywhere between $400-$700 per year, recognized in the shape of increased premiums.
We all know by now that fraud can come in all shapes and sizes.
From an exaggerated claim, an entirely false or partly false claim, or omission of information in order to pay less.
No surprise that by now, every agency in the industry is aware of the growing threat of fraud.
The only way to prevent fraud is to get ahead of it.
The best way to get ahead of insurance fraud is to two-fold.
First, don’t underestimate them.
They are intelligent, which we can tell by the increasingly creative ways consumers cheat the system.
Second, while technology is the rope in this tug of war between business and fraudulent consumers, it is also your greatest asset; that is if it’s strong and up to date.
The up to date part is important.
Many insurance agencies are still using legacy technologies day to day and expect these tools to step up to the task.
Unfortunately, they won’t.
The truth is, legacy technology such as older generation devices cannot keep up with the systems and process that need to be performed in order to effectively monitor fraud.
They are not efficient or fast enough to get ahead of any false claim.
With up to date, modern technology, agencies can compare and confirm claims information with up to date pubic records, and online databases with the consumer’s report.
One of the most popular methods of using technology to battle fraud, is AI-based automation.
Artificial Intelligence has come a long way since it first made its way into the Insurance industry.
This technology has evolved and of course, learned a lot since its debut.
Utilizing AI automation for your agency is one of the best decisions you will make in the effort to detect fraud.
Fraudulent claims cost insurance companies tons of money that they otherwise wouldn’t be paying back if there were better ways of detecting the fraud before a large payout.
So how does AI automation software help you weed out the fake claims from the real ones?
AI uses advanced algorithms to detect any inconsistencies or quirks found in a claim.
By analyzing past fraud, insurers can use predictive modeling to produce what is called a “Suspicion Score,” a value for the propensity of fraud.
The process is simple, here’s how it works.
Adjusters simply enter data, and claims are automatically given a Suspicion Score to indicate the likelihood that fraud has occurred.
The technology behind this involves utilizing data-mining tools and applying quantitative analysis.
Visual analytics is another example of AI technology benefiting fraud as well.
By assessing images and videos submitted for claims, visual analytics cab determine if the damage is actually worth the claim amount based on the visual evidence and other data from similar claims.
This helps to improve the way agents are able to sort out the bad claims from valid ones, thus, saving the agency both money and time as well as looking out for other trustworthy customers.
Again, artificial intelligence is smart and is constantly learning as it works.
These tools have the ability to not only detect an inconsistency in a claim, but they also have the ability to spot out connected groups of people that filing strikingly similar claims with the same doctors, lawyers, etc.
Humans make mistakes.
They read too fast and are distracted easily.
So, it’s no surprise that oftentimes, fraudulent claims go unnoticed and the agency ends up doling out a ton of money for customer payout that could otherwise have been avoided if the AI algorithm was involved in sorting out the claim.
This doesn’t mean the agent’s job is useless or as unimportant as that of the automation software, but the role they play is different.
Instead of spending hours managing the same claim, by allowing the software to do most of the heavy lifting to organize and manage the information, the agent can begin other work and increase productivity.
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