Create and Test a Comprehensive Insurance Big Data Strategy

Insurers should develop an enterprise-wide big data strategy as those insurers with the greatest and most enduring big data success will find major competitive advantages. With the advent of InsurTech, the tools to experiment with, test and implement this strategy are now realistically within reach.

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Life, disability, and long-term care insurers ponder how important big data really is.  As companies that achieve the greatest with big data will have a major competitive advantage, insurers need to know how to cost-effectively determine the benefits they will attain. Insurers should develop a comprehensive big data test and strategy for their entire operation, now that practical enterprise-wide solutions—both for implementation and experimentation—are now realistically within reach.

Life insurers have started to use big data, with good results, for specific critical purposes such as speed-to-market and customer reach.  What about measurement of inforce and lifetime customer value, analysis of policyholder behavior, and policyholder retention?  Can big data initiatives help pricing, product design, and the computation of reserves and capital under principle based approaches, e.g., PBR?  Generally speaking, insurers in the United States have not focused on big data for these roles, possibly because they do not yet see the value that big data can bring.

These initiatives will enable individual and group insurers to better measure inforce and lifetime policyholder value and improve pricing, product design, persistency and cross-sell opportunities. For example, the measurement and use of lifetime customer value could become practical and implementable across products and lines of business.  Companies would truly be able to target those customers with the greatest value.

In moving forward, companies need to consider how best to determine the utility of big data for their operations.  Should each function that could benefit from big data proceed separately as a distinct work stream or should the company strive for synergies across these initiatives, such as consistent technology and data management?  Do companies have the bandwidth and funding to support these initiatives?  What is the company’s mindset for transformations and how can the opportunity buildout be opened in a small yet effective manner?  Once a project’s value is proven, how easy will it be to ramp up operations?

To answer these questions, insurers must develop strategic/test plans and achieve these six objectives:

  • Rationalize those areas of the company that will benefit most from big data. Simply looking at the front office, top line sales operations, the competition, or those groups that express the greatest interest is not good enough.
  • Obtain the necessary infrastructure, expertise, and funding to make big data happen.
  • Identify changes needed in the operating environment.
  • Ensure that the company’s divisions work with common vision. What type of central governance is needed? How scalable will the work be?
  • Determine whether big data should be a core company competency. How does big data fit into the company’s strategic objectives?
  • Define critical milestones to reevaluate progress and the competitive landscape. How effectively are human capital, technology, and process transformations proceeding?  How is the regulatory landscape changing?

In the search for practical solutions, companies should consider the impact of recent big changes in insurance technology.  For example, new capabilities provided by InsurTech startup firms, can play a key role in the strategy buildout.  Suppose that a company views the sale, manufacture, and distribution of life insurance as core strengths, but not the infrastructure build and associated technological, risk and security issues. A solid InsurTech partner can manage the infrastructure, provide for scalability and consistency across the organization, and give the company the functionality it needs at a fraction of the cost. A company’s strategy can include initial testing with an InsurTech to do the pilot securely and cost effectively. Upon successful completion of the pilot, the company can choose to continue with the InsurTech or bring this functionality in house.

Blending a strategic testing approach with a practical and implementable solution may give an insurer the tools to make them land on top. Being the first to successfully test and then implement big data comprehensively, scalably, and practically across an organization can give the insurer the significant strategic edge that it needs.

Changing Your Technology Mindset: A Roadmap for Embracing Big Data

Martin Snow //

Martin Snow can be reached at and is available for conversations about the strategy, testing, and implementation of big data to improve operations.  Martin is a member of the Advisory Board of Atidot, an agile machine learning InsurTech firm, and has more than thirty years of experience and leadership at MetLife, TIAA, and Prudential.

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