Detecting insurance fraud through graph algorithms

While other forms of data analytics can also detect instances of fraud, these other machine learning algorithms often act as “black boxes” — spitting out predictions, but not always giving analysts the context necessary for them to immediately understand why it is likely that a given claim might be fraudulent. (Credit: Olivier Le Moal/Shutterstock.com)

Insurance fraud is an enormous problem — not only for insurance companies, but for law enforcement, banks and other financial institutions as well. According to some estimates, fraud accounts for up to 10%-20% of insurance losses. The FBI estimates that the total annual cost of insurance fraud exceeds $40 billion, with some other estimates exceeding $80 billion. Consumers end up paying for fraud through higher premiums, costing $400-$700 per year for the average U.S. family.

The vast majority of insurance providers have dedicated fraud investigation teams in place, and yet many insurers haven’t begun to leverage modern technology to detect instances of fraud. As recently as 2019, only 1 in 5 insurers planned to implement artificial intelligence for fraud detection over the course of the following two years.