Category: Strategy

The Shape of Things to Come


Modern technologies are increasingly used in the insurance industry.  Cloud computing is becoming more common, the use of machine learning and artificial intelligence is spreading at a steady rate and insurers’ demand for data scientists is on the rise.  Not all markets show the same levels of adoption but they are all moving in this direction at their own speed.

Open source machine learning frameworks and inexpensive technology at everyone’s disposal are fuelling the rapid adoption of machine learning in insurance.

Success stories and inexpensive technology are making this possible.  There is easy access to artificial intelligence and advanced machine learning techniques.  Popular data science languages such as R and Python have many relevant open source libraries.  

Deep learning frameworks such as Theano, Keras or TensorFlow are also open source and free for anyone to use.  Cloud computing is cheap and accessible to all thanks to Amazon Web Services, Google Cloud, etc.

In addition, the current lack of data science talent has already encouraged a myriad of Masters programmes and online learning courses that will add to the pool of data scientists.  All of this is great news.

However, this democratization of data analytics and technology has unintended consequences.  When everyone has access to the most sophisticated models and best cloud computing services, these are no longer a differentiating factor among competing organisations.  In short: you will not have an edge over your competition because you make extensive use of deep learning, as your competitors can close this gap much more quickly than before.

Relevant data, not technology, will provide the next competitive advantage.

It is important to remember that even the most complex machine learning methods are still models and the old adage holds true: garbage in, garbage out.  We have already seen cases where flawed training data has led to flawed results, sometimes embarrassing.  It is inevitable that we will see more cases as the use of artificial intelligence becomes more common.

So, is it true then that data is the new oil?  The Economist certainly thinks so.  Just like with oil, the importance of data lies in what you can do with it rather than in raw data itself.  It needs to be cleaned, processed, packaged and relevant to your needs to be useful.  Therefore, you are not sitting on a pile of gold simply because you have large amounts of data unless you are prepared to do something with it and it is relevant to your goals.  Home insurance data might not be of much help if you are planning to sell motor insurance.  Unstructured data from thousands of claim forms is pointless unless you make it machine readable and amenable to use with machine learning algorithms.

Access to relevant data will be the competitive advantage in the near future and organisations would do well by start preparing now.  There is much publicly available data whose potential is still untapped (The Irish Census, Eurostat, etc.)  But work is required before this data can be helpful.  This takes time and learning the potential and limitations of this data will take time too.  You may want to start now.

The computer games industry is a good example where this has already happened. Valve’s Steam still enjoys a dominant position as a seller of PC games.  Selling computer games is a largely digital business that generates significant amounts of valuable data for developers and distributors.  Steam collects this data and uses it to gain insights.

That industry has seen many attempts to replicate this model.  GOG has succeeded in finding its place but other initiatives such as Electronic Arts’ Origin are still far away from being a serious competitor.  However, Origin is a strategic development for Electronic Arts, as it enables the company to gather its own data.

Everyone knows that data is important. Customers and regulators too.

We have all heard how important data is and everyone is prepared to act on it. Customers are becoming more aware of the data they provide and regulators are concerned with the uses companies will make of data.  Financial services are not escaping this trend.

The insurance industry in particular enjoys a poor reputation among customers. Insurers will find that acquiring more data is not as easy as it is for other companies. Policyholders do not trust insurers and are reluctant to share data with them, as they believe insurance companies will use it against them.  However, they happily share much with companies they trust such as Google or Facebook.  Insurers will have to find a way to address this issue.

In addition, data protection legislation is evolving to face the new challenges that data analytics and artificial intelligence bring.  The GDPR is a very important development but we have already seen other initiatives to regulate access to data and the use insurers make of it.

The rise of the data broker?

Will the need for relevant data lead to the creation of data brokers?  It might happen. Alternatively, companies with large amounts of data might commercialise the insights gained from that data rather than sell the data itself.  Companies that have focused on gathering industry relevant data will be well positioned.  Google is probably the first company to come to mind but each market is likely to have its own dominant player and these may look like LexisNexis in insurance or Crème Global in the food and cosmetic industries.


Talent, technology and the adoption of machine learning will continue to be important but they will stop being a competitive advantage in the next few years.  Instead, the strategic use of machine learning and access to well maintained, relevant data will be the key to win.

Image source: Pixabay


The Role of Predictive Analytics in the Strategy of an Insurance Company


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.


Image source: Omer Yousief / Pixabay