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Insurers are looking to A.I. and machine learning to help spot suspicious claims in all new ways.
Insurance fraud technology is looking all the more futuristic these days with the introduction of artificial intelligence. Machine learning is helping the insurance industry to identify suspicious-looking claims. Moreover, A.I. can also make its way through social media posts to identify signs of fraud.
Fraudulent activities can include anything from falsified claims to sketchy brokers, and it’s expensive.
The FBI estimates fraud is costing insurance customers over $40 billion per year. The industry is seeking to use insurance fraud technology to help reduce that figure. That way, coverage will cost less for insurance companies to provide, allowing them to reduce their rates.
What’s more, the FBI’s estimate doesn’t include health insurance fraud. Many industry groups estimate that medical insurance fraud alone costs the industry tens of billions of dollars more. As a result, homeowners, health plan carriers and drivers alike are paying hundreds more in total every year.
The goal is to use insurance fraud technology to cut back on that number by as much as possible.
The FBI’s estimate suggests that the average person pays between $400 and$700 per year more than necessary as a result of insurance fraud. Without the use of A.I. technology, there simply aren’t enough investigators to search through every claim and every social media post to find signs of scams.
Teams of investigators do their best to catch as much fraud as they can. However, it would be impossible to pay enough people to find enough of these crimes to make a difference in the cost being passed on to customers.
Artificial intelligence, on the other hand, is a type of insurance fraud technology that may make this difference. A.I. can spot inconsistencies. It can identify unusual patterns. It is able to flag the claims that are most likely to need double-checking by human eyes. As a result, it has become an insurance industry standard. It helps to identify individuals embellishing on the extent of damage suffered to a property. That said, it is also programmed to spot larger operations involving multiple people and/or businesses.
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