In 2014, a New York City court made headlines by convicting a doctor in an auto insurance fraud scheme that cost insurers millions of dollars over 5 years between 2007 and 2012. In general, statistics show that while the nature of fraud is constantly evolving, questionable claims are on the rise—an increase of 16% in the United States according to the National Insurance Crime Bureau (NICB).
Such insurance frauds comprise a significant percentage of all insurance premiums, so it is unsurprising that an increasing number of insurers is using antifraud technology, for instance as part of claims processing outsourcing operations to detect claims fraud.
Insurance Analytics to detect auto frauds
Auto insurance fraud, like other insurance frauds is on the rise, and it is becoming harder to track it. Buyer behavioral changes, limited and undertrained staff, and overworked insurance officers result in only a tiny fraction of total fraudulent cases being identified for what they are.
That's where analytics can help. Anomaly detection, predictive modeling, and social network analysis methods can be used in tandem to generate a system that can:
- Inform insurance companies of suspicious patterns that need scrutiny.
- Objectively analyze information and process larger volumes of data in a transparent manner.
It is worth noting is that most frauds takes place in the early stages of an insurance application, and commonly involve a misrepresentation of facts. It is estimated that in 2012, U.S. based auto insurers lost around $16.07 billion in such rating errors . To stop this, a proactive system is urgently needed that can flag early warning signs. Insurance analytics can work very effectively in identifying application stage frauds; as has already been demonstrated with considerable success by WNS when working with leading auto insurance companies.