The potential for insurers to tap the potential of big data with analytics has never been greater. However, insurers must adapt to the organizational and technology requirements that come with adding new data sources to their arsenals to minimize risk and bolster profits.

Perhaps more than any other industry, the insurance sector has a long history of analyzing data to understand the costs of risks. But big data offers a slew of new ways for companies to innovate.

For example, McKinsey & Co. notes that instead of merely relying on internal data such as loss history, auto insurers can now link behavior-based credit scores into their analyses to identify people as being safer drivers because they pay bills on time.

“In the future, the creative sourcing of data and the distinctiveness of analytics methods will be much greater sources of competitive advantage in insurance,” according to the McKinsey. “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.”

McKinsey notes insurers can take five steps to prepare for the innovation available from the growing volume and sources of data:

  1. Start analytics projects by identifying the business value that can drive revenue growth and profitability. Business unit and front-line managers who will be using the tools must jointly define the business problem and the desired value of the analytics.
  2. Include employees on analytics teams who can quickly assess data resources available inside and outside a company and also determine how those sources can be integrated. “For instance, risk pricing and selection often can be improved significantly by mapping the data from internal customer-management systems with traditional third-party data providers such as credit bureaus and data exhaust from new digital sources,” according to McKinsey.
  3. Analytics professionals who build models must work closely with the functional decision-makers who will use the insight.
  4. Design the work flow of analytics to be as simple as possible. For example, a large underwriting group that traditionally reviews thousands of policy applications may only have to be involved in reviewing a small percentage after an insurer puts a rules engine in place. However, automation will not replace the judgment of managers handling multi-million dollar commercial accounts.
  5. Manage adoption effectively to ensure that employees will accept and trust data analysis tools. If front-line workers do not use the tools, the business value can be impacted severely, even eliminated.

Next Steps:

  • Download the whitepaper: “Using Customer Analytics to Deliver Value Within Insurance.”
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