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Digitization marked by a growing number of mobile devices and social media is changing the business landscape for all sectors — including insurance. The opportunities offered by this landscape for insurers are vast. Insurance firms also receive a plethora of inputs from digital information in the form of feedback, which also can be used to come up with customized products and competitive pricing.
In addition to these opportunities, insurance companies are harnessing digitization — using data analytics for fraud detection. Handling fraud manually has always been costly for insurance companies, even if one or two low incidences of high-value fraud went undetected. In addition to this, the big data trend, (the growth in unstructured data) always leaves lot of room for a fraud going undetected if data is not analyzed thoroughly.
Insurance fraud detection is a set of practices implemented by insurers to identify and prevent fraud from policyholders or third parties. Not so long ago, insurers were primarily relying on claims adjusters’ experience in fraud detection efforts. Later, rule-based systems appeared, allowing organizations to automate the identification of potentially fraudulent claims.
Risk analytics is the use of historical data, machine learning algorithms, and statistical modeling to foresee future events. Risk analytics in insurance fraud detection is the use of data and statistical techniques to automatically identify fraud patterns and reveal potentially fraudulent claims.
Posted by:Admin Posted on:10/03/21 07:08
