HANDBOOK
INSURANCE FRAUD
Deception Detection Technology should enhance your anti-fraud strategy, not replace it. Here’s why... BY DA N M C K E N Z I E , P r i n c i p a l S o l u t i o n A r c h i t e c t , Fi n a n c i a l C r i m e s , S A S
C
utting-edge developments in analytics, machine learning and artificial intelligence promise powerfully enhanced anti-fraud capabilities for insurers. Software can recognize and even learn patterns that detect and prevent fraud, while speeding customer service and reducing false positives. “Enhance” is the key word. The most sophisticated software tools are worthless if they aren’t built on a solid foundation of business processes. Steps 5 and 6 of an anti-fraud strategy aren’t helpful if Steps 1 and 2 are neglected. Fundamentally-sound business practices deliver results on their own. But when they are the core of a platform designed to feed those high-tech, analytical tools, the results can only improve.
Rules about rules The simplest tools are the most powerful. Years of underwriting and investigative experience teaches the patterns of customer and provider behaviour that should be red flags. For example, do personal injury and auto property damage correspond? Can four people suffer soft-tissue damage from a minor fender-bender? Was the accident reported to police? How recently was the policy taken out? What’s the value of the car? People stage accidents with 1990-era Toyota Tercels, not brand-new Maseratis. Similar rules aimed at third-party service providers can ferret out even more fraud. A customer can defraud a company out of a few hundred or thousands of dollars, but providers can cost compa-
nies millions. The “Impossible Day” is a standard: you can’t provide 27 hours of service if you’re open nine to five. Do all of a provider’s invoices bump up against the limit for reimbursement? Do the costs of services line up with the industry average? Is a supplier submitting a large number of invoices for customers who work for the same company? This doesn’t require a huge software solution. A SQL database is enough for a basic set of rules. A dozen rules is a good starting point. Too many rules return too many false positives. Keen-eyed adjusters will catch these red flags, but there is a limit to how much a human being can review. Applying an automated rule set allows an insurer to vet all claims.
canadianunderwriter.ca | December 2019
41