Insurers must grab the opportunity of advanced analytics says McKinsey

Analytics sits at the core of the insurance industry. Advanced math and financial theory have always been essential to risk assessments of  client default. Over the past 15 years, new tools have emerged through software sophistication and the explosion of new data sources .

And these new tools are changing the way insurers do business, according to a new McKinsey report “Unleashing the value of advanced analytics in insurance” written by Richard Clarke and Ari Libarikian.

“The impetus to invest in analytics has never been greater for insurance companies”, say the authors.

The revolution of advanced analytics in insurance lies in three key points: 

  • The explosion of data sources:  Insurers are no longer restricted to internal data. Thanks to social media, multimedia, smartphones and computers, insurers now have access to a huge amount of third-party data sources. Recently, the US and UK governments as well as the Europe Union launched “open data” websites on which massive amounts of government statistics, including health, education, worker-safety, and energy data, were available. “With much better access to third-party data from a wide variety of sources, insurers can pose new questions and better understand many different types of risks, for example (…) which combination of corporate behaviours in health and safety management is predictive of lower worker-compensation claims?”
  • New tools:  The insurance industry is seeing an increasing number of investments in innovative analytics vendors. McKinsey cites the example of one vendor has developed a new health-risk model “by blending best-in-class actuarial data with medical science, demographic trends, and government data”. New tools will allow insurers to underwrite new risks such as cyber security risks and industry-wide business interruption stemming from natural disasters.
  • Real-time data monitoring that influences behaviour: Real time monitoring is about to drive significant changes for both the insurer and the insured. The insured will learn more about themselves and their behaviour. The insurer will be able to influence behaviour thanks to data. For instance, in auto insurance, the insurer monitors the driving habits of the customer and the insurer can leverage this data to change the driving habits for the better.  “One UK insurance company using telematics reported that better driving habits resulted in a 30 percent reduction in the number of claims”

 The management consultants also provide a framework with five components that are likely to lead to success in advanced analytics.

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“While more data, better tools, and new applications are creating opportunity in the insurance industry, to adapt and thrive in this emerging world of advanced analytics, insurers need to manage complex and large-scale organizational change.”

Define the business value: Insurers should start every analytic project by defining the business value of analytics: first define the business problem, and then define what value analytics could bring up.

  1. The data ecosystem : It’s crucial that insurers be able to scan the whole data ecosystem in search for additional data. “Unlocking the business potential of advanced analytics often requires the integration of numerous internal and external data assets”, says McKinsey
  2. Modelling insights: Collaboration is needed between analytics professionals and decision makers so that they combine a “black box” (pure statistical analyses of large amounts of data)and a “smart box” (empirical knowledge of experienced practitioners). “Experienced claims adjusters, for instance, have an intuitive sense about which injuries have the highest probability of escalating. Often, a hypothesis based on judgment still needs to be validated against external data”.
  3. Transformation: work-flow integration: Insurers should design as simply and easy as possible the integration of new decision-support tools. The key for that is to determine the right level of automation. “A centralized underwriting group, for example, which had manually reviewed thousands of insurance-policy applications, needed only to review 1 percent of them after they adopted a rules engine. At the other end of the spectrum, automation can never replace the expertise and judgment of managers handling multimillion-dollar commercial accounts”.
  4. Adoption: It’s vital that employees adopt the new tools, understand well how they work and how to use them consistently. It’s the role of decision makers to manage the adoption phase. “If frontline decision makers do not use the analytics the way they are intended to be used, the value to the business evaporates”, says McKinsey.
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