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.
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