A few years ago, the buzz around big data analytics as an effective solution to deter insurance fraud was greeted with cautious optimism. Many insurance companies were reluctant to invest in a technology whose outcome was uncertain. So, how are insurers who jumped on the big data bandwagon faring? Has it helped them pare down their losses from fraud? A survey conducted by WNS on Property & Casualty insurers in the U.S. has revealed some interesting results.

For instance, underwriting fraud generally costs insurance companies 10 percent of their revenue. The survey results show that 75 percent of insurers who deployed analytics in the underwriting stage saw a drop in the claims volume. Predictive modeling techniques at the underwriting stage enabled them to detect fraudulent policies promptly.

Fifty percent of the survey respondents said that fraud analytics in the underwriting stage helped in higher detection of suspicious policies and reduced premium leakage, while 25 percent experienced a lower rate in policy cancellations.

With the proliferation of digital channels insurance companies are receiving massive amounts of unstructured data. In such a context, big data technology helps companies to capture abnormal behavior patterns and detect identity theft.

However, despite all this, the penetration of big data in the underwriting stage is low. Most insurers are still grappling with the implementation. This is mostly due to factors such as inadequate budgetary provisions, infrastructure constraints and fragmented IT teams. For such companies, tying up with a specialist provider in insurance and analytics is an ideal way out as it can also reduce investment costs.

While one survey is being quoted, there is enough research backing the holy grail of big data. The moot question therefore is, ‘Will the future of profitable growth for insurers definitely involve the power to harness and leverage big data analytics?’

To know more, visit us at WNS DecisionPointTM | Insurance Fraud Detection and Prevention in the Era of Big Data

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