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.
Want to continue reading?
Become a Free PropertyCasualty360 Digital Reader
Your access to unlimited PropertyCasualty360 content isn’t changing.
Once you are an ALM digital member, you’ll receive:
- Breaking insurance news and analysis, on-site and via our newsletters and custom alerts
- Weekly Insurance Speak podcast featuring exclusive interviews with industry leaders
- Educational webcasts, white papers, and ebooks from industry thought leaders
- Critical converage of the employee benefits and financial advisory markets on our other ALM sites, BenefitsPRO and ThinkAdvisor
Already have an account? Sign In Now
© 2024 ALM Global, LLC, All Rights Reserved. Request academic re-use from www.copyright.com. All other uses, submit a request to [email protected]. For more information visit Asset & Logo Licensing.
