According to a fact sheet released by the Southwest Insurance Information Service (SIIS), Approximately 10% of all insurance claims are fraudulent, and nearly $80 billion in fraudulent claims are spent annually in the U.S., estimates the Coalition Against Insurance Fraud. Insurance fraud is certainly an issue that must be addressed given the benefits both the insurer and the insured will obtain from its prevention: the insurance buyer is able to receive coverage at a lower price, which gives the insurance company a competitive advantage.
Insurance fraud can be perpetrated by the seller or the buyer. Seller fraud occurs when the seller of a policy hijacks the usual process, in a way that maximizes his or her profit. Some examples are premium diversion, fee churning, ghost companies and worker’s compensation fraud. Buyer fraud occurs when the buyer deliberately invents or exaggerates a loss in order to obtain more coverage or receive payment for damages. Some examples are false medical history, murder for proceeds, post-dated life insurance and faking accidents.
Traditional methods to detect and prevent this form of fraud include duplicate testing, using date validation systems, calculating statistical parameters to identify outliers, using stratification or other types of analysis to identify unusual entries, and identifying gaps on sequential data. These methods are a great way to catch most of the casual, single fraudsters, but sophisticated fraud rings are usually well-organized and informed enough to avoid being spotted by the traditional means. They use layered “false” collusions in a similar way than money laundry rings.
In this scenario, where implementing alternative fraud detection methods is crucial, graph database management systems play a significant role. In the case of buyer fraud, the only way to catch the complex layered collusion performed by criminal rings is to analyze the relationships of the elements involved in the claim, which is a tedious task to perform on a relational database.