Predictive analytics is slowly making its way into the insurance sector but progress is different in every country.  Nevertheless, there is a global trend across the industry to acquire data analytics capability.

Insurers with an analytics team are already enjoying the benefits of superior underwriting, pricing, and fraud detection with only a few companies currently using analytics in more than one of these areas.

This is expected to change and extended use of analytics is on the horizon[1].  However, once all insurers have a data analytics capability, something extra will be required to keep a competitive advantage.  The way an insurer uses its analytical resources will determine whether the company has a lasting advantage.  Data analysts are in high demand[2] so insurance companies need to ensure their analysts focus on the most important tasks for the business.

In addition, an analytics team will perform better when there are specific objectives and a clear sense of purpose.

Data analytics – enabling the company’s strategy?

Instead of adopting a silo approach, an insurer should use data analysts to support its business plan.  Analytics should help the business achieve its strategic goals to:  increase market size, achieve a better product per customer ratio, reduce fraud, etc.

The company’s strategy contains the business’ most important goals, so the data analytics department should focus on achieving those goals.  This can only happen when an insurer fully embeds analytics in the organisation rather than leaving it to act as an isolated team with little contact with the rest of the company.

XL and AIG have already seen this as key to success and they have taken steps accordingly[3].  Deloitte’s John Lucker considers that an appropriate analytics strategy is the first component to successful execution.  Christian Moe, Senior Analytical Consultant at SAS, also sees the alignment of analytics and business strategy as the first step to succeed in the implementation of data analytics[4].

But not all insurers are there yet:  a Deloitte’s 2015 survey reported that fewer than 50% of US Health companies (which includes Health insurers) had a clear analytics strategy[5].  US healthcare is often quoted as undergoing a data analytics revolution[6], putting the strategic gap in perspective.

There is a clear opportunity in the US Health Insurance market for companies that can formulate a good analytics strategy.  This applies to other insurers too.

Predictive analytics – driving the strategy?

Some argue that rather than supporting the company’s strategy, predictive analytics should dictate the strategy.  In today’s digital, fast paced world, the ability to identify new or changing trends and anticipate developments could be the key to success[7].

Oracle’s white paper ‘Driving Strategic Planning with Predictive Modelling‘ already identified this in 2008[8].  The paper expects a shift in the focus of planning sessions after adopting predictive modelling:  ‘…from debating arbitrary point estimates toward reaching consensus on the key underlying assumptions with the greatest impact on the results’.

Focusing the analytics team on helping the business achieve desired outcomes for those key assumptions is a great way to contribute to the success of the strategy and its execution.

Oracle considers that the change in planning focus is possible because predictive analytics enables a business to ‘identify and evaluate risk and uncertainty in strategic decisions’.  Could insurers benefit from using predictive analytics to drive their strategy?

To an extent, insurance companies are already doing this.  Claims projections, stress and scenario testing, asset and liability matching and other forms of predictive modelling are already embedded in insurance, with actuaries making excellent use of these techniques.

This is good news for insurers but there is a wider range of methods in predictive analytics that would be a useful addition to the traditional actuarial skill set.  There is also scope for application in non-actuarial parts of the business such as Sales or Claims Management.

Analytics, an independent team with a supporting role

A company’s strategy will focus on different areas, sometimes on several at the same time.  Because of this, it is important that the analytics team is not part of a team with another specific focus such as Actuarial or Marketing.  Analysts need to be able to work with different business functions, understand how these operate and move on with the business strategy.

To be fully effective, an analytics team must be independent, ideally reporting to the CEO.  This is to ensure that it best serves the company’s interests rather than those of a particular department.  A recent survey by Towers Watson found that conflicting priorities is the third most significant challenge US P&C insurers face in the US, following only the lack of available talent and difficulty in data capture[9].

An insurance company with an autonomous analytics team, staffed with data analysts and business experts, will always be in a better position to help the company achieve its desired business outcomes than one that depends on a specific business unit and only counts with data analysts among its members.

A dynamic approach to analytics, aligning it with the company’s strategy, is a long-term winning combination that will provide the necessary competitive edge when data analytics is widespread across the industry.

Notes

Image source: Omer Yousief / Pixabay

[1] https://www.towerswatson.com/en/Insights/IC-Types/Survey-Research-Results/2016/02/infographic-us-pc-insurers-use-of-analytics-big-data?utm_source=SocialMedia&utm_medium=LinkedIn&utm_campaign=RFS-RCS&utm_content=US-InsInsightDigest

[2] http://www.bloomberg.com/news/articles/2015-06-04/help-wanted-black-belts-in-data

[3] http://www.insurancetech.com/data-and-analytics/learning-from-xl-and-aig-how-to-become-an-analytical-competitor/d/d-id/1314121?

[4] http://blogs.sas.com/content/hiddeninsights/2013/10/13/six-steps-to-a-business-driven-analytics-strategy/

[5] http://www2.deloitte.com/content/dam/Deloitte/us/Documents/life-sciences-health-care/us-dchs-provider-analytics-report.pdf

[6] https://hbr.org/2014/10/predictive-medicine-depends-on-analytics

[7] https://hbr.org/2014/10/create-a-strategy-that-anticipates-and-learns/

[8] http://www.oracle.com/us/solutions/ent-performance-bi/performance-management/064129.pdf

[9] https://www.towerswatson.com/en/Insights/IC-Types/Survey-Research-Results/2016/02/infographic-us-pc-insurers-use-of-analytics-big-data?utm_source=SocialMedia&utm_medium=LinkedIn&utm_campaign=RFS-RCS&utm_content=US-InsInsightDigest

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