Diving Into Big Data

Underwriting is another area of big data that insurance companies have started to embrace. McCrindell notes that Gore Mutual’s “first step” involved using Outside Intelligence Risk Discovery, a platform for big data analysis.

“We are able to have rapid access to large amounts of data from many sources that may not have been as readily accessible before,” McCrindell says. “This allows us to deliver decisions on submissions faster to our broker partners, as well as learn more about the risks we already write. The competitive advantage can be lost if too much time is taken to analyze a risk,” she says.

“The goal is to focus on giving underwriters better information to identify and price risks more accurately,” Adamson comments. “We know insurers want to prove this out in terms of return on investment. For example, if we can get ahead of certain kinds of risks by gathering information from new sources and integrate that into existing data, can we underwrite and price that risk more accurately?”

Guidewire’s Eugene Lee suggests that big data can also result in better underwriting decisions in real time. “It means better underwriting decisions for underwriters who can access real-time forecast and forensic weather information to determine whether they should inspect a property for prior damage or block a policy change if the property falls inside the path of an oncoming hurricane,” he notes as one example.

For RSA’s Mark Cairns, he expects that pricing will be influenced by new data sources and analytic tools. “In the insurance industry, we have seen some of our larger pioneers taking advantage of vehicle telemetry to aid in driver insight analysis,” he says. “This will lead to another metric or rating component when determining the optimal pricing (and) alter how traditional actuarial-based personal auto rating has been calculated,” he predicts.

Aid to Loss Prevention

Access to new forms of data can affect loss prevention measures for clients, says Kendrick. “Zurich has a wealth of data that we utilize for our partners; from implementing industry-specific loss reduction strategies to global risk transfer solutions that result in operational efficiencies,” he notes. “(We) take advantage of broad data sets to help identify evolving and international claims trends and make strategic underwriting decisions.”

FRAUD DETECTOR

When it comes to claims, one of the most frequently touted benefits of big data is in improved fraud detection. IBM is using big data analysis techniques for insurance fraud with a large Canadian p&c insurer – an initiative that Donahue reports is the first of its kind in the world.

“In terms of fraud, we are taking internal, external data and using sophisticated analytic tools to look at it through a different lens,” Donahue explains, adding that initial results of the project are expected this spring. “We are backing up a bit into the customer selection and underwriting process. We’re asking: ‘Can we use data, claims, customer information and location intelligence to screen out fraud earlier in the process?'”

The true benefits of big data fraud detection and prevention will be found in greater information sharing amongst p&c insurance companies, Donahue suggests. “The real opportunity, of course, is to do this across companies,” she says. “As many in the industry know, organized fraud rings will use multiple companies, so access to the widest possible data is crucial.”

“There is a strong need to have industry-wide data sharing to gain business insights (e.g. anti-fraud) that will enable the insurance industry to provide better service and lower cost to the consumers,” suggests Robert Merizzi of Aviva Canada.

Lee also notes that big data may have a wider application in the efficiency of claims management departments. “(Big data) can mean reduced claim expenses for managers who can more accurately allocate their internal and independent adjusting resources to those properties that require inspection and remote adjust those that do not,” he comments.

While there are rapidly emerging examples of big data in the p&c insurance sector, several sources say that true business adoption of unstructured information is still in the early stages.

“At RSA, we are in our infancy taking advantage of big data,” Cairns observes. “We are in the process of augmenting our infrastructure to include the requisite tools to be able to interpret big data in a more succinct and timely manner,” he says.

“I don’t think of big data as a thing,” Lee says. “It’s the water we swim in. So the practical application of big data in the insurance industry is to help our organizations do everything we do, but to do them more effectively.”

HURDLES TO CLEAR

Several challenges stand in the way of full-fledged implementation of big data analysis for real business outcomes.

“I think one of the key challenges is that there is no magic formula to bringing disparate data sets together to get a common basis from which to draw insights,” Betts notes. “You have to constantly work at it, and in some cases, it is a gradual, iterative approach,” he says.

“I go back to proving out the return on investment,” Adamson says. “You can’t just do a science experiment – the data, both external and internal, has to be extracted into meaningful business decisions. It is not just dumping more screens of data onto underwriters; they already have that. It means weeding out the noise and synthesizing the data to allow the underwriters to do their jobs better,” he says.

“There are many challenges when it comes to big data,” Merizzi observes. “A few come (to) mind: How to manage and store these massive amounts of data in a cost-effective way? How to deal with historic data that have different formats? How to combine structured and unstructured data seamlessly in order to enable analytics?”

For McCrindell, one of the “Vs” that applies to big data is a potential concern for insurers: veracity. “One of the main challenges when it comes to big data is being able to identify what information is credible,” she suggests. “Ensuring the accuracy of data is key. As an insurer, we need to vet what is credible, and balance it with traditional information and experience to make the best decisions,” she emphasizes.

In addition, big data’s arrival on the insurance scene has been preceded by pre-existing IT projects at companies. “In many cases, insurance companies already have several multi-year data warehousing projects on the go, so they need to look for solutions that help piece this information together,” Adamson says. “We often find we can help them with the external data part of the solution, and then we can deal with the integration problem.”

The p&c insurance industry has a frequently cited reluctance to be on the leading edge of technological innovation. Big data’s tendency to be seen as a “living exercise” that will continue to grow and “morph dynamically” may represent a “contrarian mindset to the traditional insurance space,” Cairns suggests.

Donahue holds that insurers have to roll up their sleeves and get started. “A lot of insurers spend a great deal of time getting ready for the party; they should just have the party. Define a key business issue, understand your desired outcomes and start a big data pilot project,” she says.

For Betts, there is an undercurrent of interest in big data among Aon senior managers that he has not traditionally seen in IT projects. “Our insurance executives in the risk business have shown a real enthusiasm with the possibilities found in GRIP and big data,” he says. “When they see what is possible through this data, they get excited,” he adds.

“With the changes in data analytics and actuarial capabilities, companies are now able to do analysis that was not previously possible,” McCrindell observes. “This changes how business is priced, the speed at which insurers can respond, and their ability to provide sound risk management sol utions for their broker partners,” she says.

“The applications for utilizing a big data structure are unlimited and present meaningful opportunities for insurers to offer new, optimized and tailored risk reduction strategies,” Kendrick notes.

“The possibilities are almost endless what can be gleaned from all this information,” Cairns adds. “Firms that can take advantage of the tens of millions of tweets per day or the in excess of 10 terabytes of Facebook information per day will be able to garner immense value from both aspects,” he argues.

“I think the biggest challenge for enterprises is analogous to the challenges we face as a society; namely, that we need to become a lot more ‘data-literate,'” Lee concludes. “What is data good for? How should it be read and interpreted? As our society becomes more data-literate, consumer expectations will change and businesses will have to change along with them.”