Data Analytics Can Provide A Competitive Edge To Insurers

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An article by McKinsey’s Richard Clarke and Ari Libarikian describes a new trend in how insurers use data in their businesses.

Gone are the days when insurers managed underwriting risk solely through actuarial data valuations, though these are still of crucial importance. Instead, advanced data analytics that rely on high technology computers and innovative sources of data now help insurers gain new insights into their business. Insurers are using innovations in mobile, social and cloud technologies, combined with advances in analytics software, to bring about process changes across their organization, including sales, marketing and service operations.

Insurers using data analytics: Case studies

In auto insurance, one company correlated data from credit bureaus with their own analysis based on very logical evidence that persons who were punctual in paying their bills were also, generally, safe drivers.

The authors cite another example of insured automobile drivers who agreed to let the insurance company monitor their driving through real-time telematics. “There is already evidence that this is influencing drivers and changing their driving habits for the better,” say the authors. “One UK insurance company using telematics reported that better driving habits resulted in a 30% reduction in the number of claims; another UK insurer similarly used telematics to help a large client reduce accident-causing risky driving maneuvers by 53%.”

In yet another example, cited in an Infosys white paper, it is now possible for an auto insurer to more accurately set premiums on a given car model by crunching data from hitherto unconventional sources such as its user reviews, owner experience, ratings from sources like  blogs, auto forums, social media, etc. – all of which can be  leveraged to conduct a sentiment analysis as an input into the risk management process. The white paper had this interesting illustration of the process:

In another example, GE Consumer & Industrial (C&I) used a data analytics solution to red flag certain service providers committing fraud – the organization relies on thousands of service providers to handle more than 1 million service claims in its appliance groups each year – and is said to have saved $5.1 million during the first year it used analytical software. “GE claims data is uploaded to the fraud detection software, where 26 claim-level sets of analyses are automatically calculated for each claim. Claims are flagged for audit when multiple elements are out of the ordinary, compared to averages. Once flagged, auditors at GE receive reports of suspicious claims to investigate,” says the provider of the software.

How use of data analytics provide competitive advantage to insurers

“In the future, the creative sourcing of data and the distinctiveness of analytics methods will be much greater sources of competitive advantage in insurance,” say the McKinsey authors. “New sources of external data, new tools for underwriting risk, and behavior-influencing data monitoring are the key developments that are shaping up as game changers.”

Data sources are crucial, and fortunately, are becoming more accessible to insurers. Rich data bases, such as those of the public sector and so far closely held by governments in the US, UK and EU, are now available through “open data” websites. Such data provides valuable behavioral insight as well as a better understanding of risk to insurers.

The authors point out that one insurance vendor has combined this kind of government data with demographic trends, actuarial data, and medical science to create an innovative health-risk model.

Insurers managing change

Advanced analytics place substantial implementation challenges on the adopting companies, and according to the authors these are best overcome through a 5-step framework as shown below:

insurers data analytics framework

The process begins by identifying the business area, operation or function that can throw up value by using analytics. Identifying the data sources that can be used in the analysis (in the earlier example the credit bureau was an external source, while customer records were internal) comes next. Analytic professionals and the functional decision makers then need to collaborate to design and build the analytic model. Once completed the model has to be integrated into the work processes of the insurer, and thereafter embraced by the functionaries such as claims adjusters, underwriters and call-center representatives.

“Weaving analytics into the fabric of an organization is a journey,” comment authors Clarke and Libarikian. “Every organization will progress at its own pace, from fragmented beginnings to emerging influence to world-class corporate capability.”

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