Certain triggers may be enough to raise suspicions of fraudulent activity for insurance companies. These could include inconsistent reports of an event, a history of similar incidents, or the absence of a contemporaneous report of accident or injury. Of course, these may be innocent in nature but it’s the job of the claims validation team (CVT) and/or fraud investigation team to investigate all information thoroughly. Intelligence, data mining and claims analysis all help to identify trends and links between claims and highlight anything which looks amiss.
Machine learning and Artificial Intelligence (AI) are increasingly being used to speed up manual, complex analysis of claims. Until recently, their use was more widespread in personal lines but we’re now seeing application in commercial lines. AI and machine learning make use of algorithms to cross-reference huge amounts of data points at speed, identifying anomalous patterns and trends. It’s hoped that digital detection will result in lower costs to insurers which in turn could be passed on to customers in the form of lower premiums.
Over time anti-fraud infrastructure has developed significantly with the setting up of various counter fraud initiatives. One such example is the Insurance Fraud Bureau (IFB), established in 2006 as an industry response to organised fraud. Today, over 98% of the general insurance market holds IFB membership.
