Fighting Fraud With Data Analytics

The FBI reports that the total cost of insurance fraud is estimated to be more than $40 billion per year, costing the average U.S. family – in the form of increased premiums – between $400 and $700. A long-established and growing problem, insurance fraud has its many guises – ranging from tiny, one-off opportunistic cases to multimillion-dollar syndicates of customers and suppliers working together to routinely defraud insurers. Luckily, digital enhancements within the insurance industry have been able to help companies lessen certain fraud risks – particularly when data analytics is brought into the mix. To remedy insurance fraud using data analytics, individuals and businesses must be analyzed as they exist in the real world – as holistic, connected entities. To make these kinds of connections accurately, detection strategies must process high volumes of data in real time, be able to generate and constantly update a view of entities and apply a scoring model to the full picture. This allows companies to track and catch fraud, even across insurance lines and when multiple people are involved. Fortunately, there are now technologies that are able to do just that – detect fraud and understand risk throughout a customer’s lifecycle. This will, in the long run, provide better claims processing and a healthier insurance system. See also: Leveraging Data Science for Impact   Quantexa, a data analytics firm that uses AI technology to piece together suspicious customer behavior, enables companies to make better decisions with their data. Their technology allows users to knit together vast and disparate data sets and derive actionable intelligence, a task that would normally take a human many months to complete. This technology can be focused on a single person and the many data points that are correlated to him or her, or larger entities such as corporations. Technology like that of Quantexa’s can gather both claims and policies and build a network that provides three levels to which one can apply analysis: The claim: This analyzes claim behavior over a long period. For instance, has a person filed for soft tissue damage multiple times? If so, how often and at what rate? This frequency could be a marker for fraud. There is also the ability to review if claims are filed close to when policies are taken out – another marker for fraud. The entity: The entity can be either a claimant or, say, a medical provider; the analysis lies within the relationship between the two entities. Believe it or not, there are instances where medical providers have intentionally and habitually provided the wrong injury code; for example, if a claimant is in the hospital for an injured leg, the medical provider bills the insurance company for a more expensive procedure, such as a hysterectomy. Technology can detect and assess injury code discrepancies. The network: This is based on the density of relationships and connections between claimants, witnesses, medical providers and beyond, and can stem from both claim information and transactional data. For instance, are multiple claims from “different” claimants all going to the same bank account? Factors can be pieced together to paint a larger picture on where fraud is originating. See also: How Connected Data Can Help Stop Fraud   Technology allows fraud to be detected much earlier on and across much larger schemes than humans ever could – a fact that should give thieves something to be concerned about, and all honest insurance policyholders something to rejoice about.