New whistleblower on insurance frauds – An Infosys Research Brief
Article Synopsis :
Even though insurers are sitting on huge piles of data, processes to detect and mitigate fraud tend to be highly manual and dependent on claims adjusters who base their decisions on traditional business rules and intuition. Big data analytics presents a great opportunity for insurers to automate the fight against fraud.
In the research paper “Big Data Analytics: New whistleblower on insurance fraud”, Sachin Pandhare of Infosys discusses traditional methods to counter new forms of fraud, the surge in data, and the role of analytics in detecting both instances and patterns of financial deceit. The paper stresses the importance of building an IT infrastructure supportive of analytics by providing data from a variety of key sources. It also presents four key data models that can help insurers strengthen their fraud detection capabilities:
- Social network analysis
- Predictive modelling
- Social customer relationship management
- Telematics driving data analysis
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Digital Insurer's CommentsReliable estimates suggest fraud touches one in four insurance claims resulting in as much as 10% overpayment (or false payment) industry-wide. Claims fraud costs the insurance industry billions of dollars per year.
Traditional fraud mitigation solutions are manual in nature and don’t harness the full potential of available data sources. Tools are emerging to crunch both internal and external data at scale and speed to detect often complex fraud schemes.
The high volume of claims data is key to the problem. Claims adjusters and analysts are so swamped with information that they lack the capacity to spot irregularities or anomalies that seem obvious when pointed out. Digitally-minded insurers are investing heavily to add fraud analytics to automate and scale detection efforts in pursuit of substantial savings for shareholders and mutual policyholders.
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