BLOG: How P&C insurance professionals can find the truth in big data

BLOG: How P&C insurance professionals can find the truth in big data | Insurance Business

BLOG: How P&C insurance professionals can find the truth in big data
If you’re not yet using big data and analytics to do your job, you likely will be soon. Surely you’ve heard of big data transforming industries as varied as medicine, advertising, and financial services, and it holds particular potency for the property and casualty insurance space. Big data can simplify your marketing to potential clients, aid underwriting and claims, and ultimately help you retain more clients over the long term.

But as more and more insurance carriers begin to use data to drive their decision-making, they have to be careful about where that data is originating. Too often it comes from error-prone surveys marred by selection bias, or from third-party sources that could be shadowed with misinformation or lack of context. For big data to truly be useful, it has to be relevant and accurate, and many streams of data in use today just don’t fit the bill.

How insurance companies collect and verify their data will determine whether they’re better informed about their clients and basing decisions on a bedrock of truth. Big data is poised to improve the decision-making of an entire insurance organization, from home office employees to customer-facing agents, but only those with relevant and accurate information will reap the rewards.

Realizing data’s potential
Pinpointing prospects no longer involves a phone book and uncanny stamina. Using big data tools, agents can spot pockets of businesses that might qualify for insurance. For example, if your company has great prices for florists and agents are interested in writing Commercial Auto, you can locate the florists that are likely to have multiple vehicles by using sales as a metric, with more sales being an indicator of more vehicles. Once you know where those florists are agents can start marketing. To write Workers Comp, they can look for florists with a high payroll (i.e., more employees).

Using the same example, underwriters can pull up the financial metrics of a particular florist and benchmark it against the floral peer group as a whole. Underwriters no longer have to apply a discretionary factor to the price of a policy based on a gut feeling about the owner, sense of how the business is run, or one of the few known factors about the account, such as whether there is a safety program in Workers Comp. Now they can make a decision about the discretionary factor using more foundational information. 

Realizing data’s pitfalls
All of these benefits assume, however, that the data that insurance agents use to drive sales has been vetted and is accurate. Sometimes that’s hard to judge, but often the source of the data tells you all you need to know about whether it’s a foundation for decision-making or a misleading fraction of the truth.

Many firms, inundated by the volume, variety, and velocity of incoming data, can overlook its veracity. True data veracity depends on a technological foundation, one that taps reliable sources, fills out a complete picture, and establishes benchmarks that put information in context. Surveys and third-party data often fall short on all three counts and can create more problems than they solve.

As more property and casualty insurers look to tap the power of big data, they should do so with an eye on veracity. Knowing where data comes from, what biases might pepper the information, and the full context behind that information, insurers can separate the questionable data from the truth. Knowing that difference, and tapping the right data, will be the key to big data success.
 
Author Bio
Kevin Sheetz, CEO and Co-Founder, Powerlytics is a former audit and advisory partner at KPMG. He has developed a global risk assessment tool used for bank audits, an information-based internet joint venture focused on financial services. Kevin was also a VP & Managing Partner of Global Business Operations at Unisys and ran his own consulting business.

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