March 29, 2017

Thanks to our guest blogger, Tony Boobier for his input and vision on bringing data to the point of need in insurance, contextual analytics and the latest ‘cogs’ in the insurance industry machine.

With over 30 years of leadership experience assisting companies who are developing analytical solutions, coupled with deep subject matter experience in data, analytics and management, Tony Boobier has a particular focus on the financial services sector. Specifically in the insurance sector, he has deep insight into the key business imperatives of operational efficiency (including claims management), customer analytics, and risk management (including location analytics).

Before operating on an independent basis, he held a senior worldwide role at IBM. Previously he had senior positions in the financial services sector, both for insurers and intermediaries. He holds professional qualifications in engineering, marketing, management, supply chain management and in insurance. He is an established international author and speaker.

Everyone likes to make comparisons, so I was delighted to recently discover the existence of a survey on happiness*, which measures overall happiness levels on a scale of one to ten (with ten being the happiest).

Norway has increased its position from fourth place in 2016 to first place this year, followed by Denmark, Iceland and Switzerland. All of the top four countries (which noticeably are all Northern European) rank highly on all the main factors which make us feel happy: ‘caring, freedom, generosity, honesty, health, income and good governance’. UK was in 19th place, if you’re interested, below the US which came in at 14th.

It’s a complex and detailed report, but in general terms it seems that about half the world are moderately happy. Only 5% view themselves as being very unhappy, which was about the same percentage of those who described themselves as extremely happy. It looks like most of us are somewhere down the middle.

It seems that comparisons are everywhere: the highest mountain, the cheapest air flight, insurance policy, salary comparisons and many, many others. The advent of customer-facing comparison websites has led to whole new benchmarking industries having been created. Information inevitably increases in value and importance when it is shown in context.

For insurers, data and analytics continue to be high on the agenda. Traditional business intelligence tools especially in the area of the finance department continue to help insurers understand internal costs and profitability by product and channel. Predictive tooling provides them with better insight as to the corrective action they should take especially around issues such as pricing and customer retention. Risk analytics helps insurers understand capital needs and allocations with more granularity.

In the regulatory area, we are in an age when the agenda of insurance leaders has been dominated by risk associated with capital and solvency: they have perhaps taken their eye off the ball in terms of other risks, such as underwriting risk or reputational risk.

Insurers are entering a new paradigm where greater disclosure under Solvency II rules will result in more information being publicly ‘on the table’ than has existed previously. Current disclosures are at a relatively high level but future disclosures will be both more granular and more sensitive in nature including full balance sheets, components of own funds, and greater breakdown of the SCR (Solvency Capital Ratio) number.

This additional risk management information may not mean a lot to the general public or even to many insurance professionals, but properly interpreted it has the potential to provide great insight into competitive positioning. Fund managers who make significant business decisions involving investment are also already looking on with interest.

The potential for more detailed insight is not only confined to the risk management area, but also extends to customer pricing and the customer experience, especially when coupled with qualitative insights gleaned from social media analytics, consumer sentiment and the continuous drumbeat of news.

News aggregators not only allow insurers to know what is being said about them, but in addition what is being said about everyone else. They are a vital cog in the information engine, and the importance of this function is set to increase.

If analytics is currently one of the biggest tickets in town for the moment for insurers, the ability of an insurer to understand their own performance ‘in context’, in other words in comparison to the marketplace must surely become a critical success factor. For want of a better name I have described this type of comparison as ‘contextual analytics’.

After all, what good is internal information to an organisation if at the end of the day it doesn’t know whether it is performing better or worse than its peers?

Even so, ‘contextual analytics’ and the desire for greater understanding of the market and competition is not in itself the destination. As with more traditional analytics, it is what is done with the information that matters. With contextual analytics, insurers will become increasingly good at knowing and optimizing their own performance.

Context-sensitive information also opens up some new scenarios for the insurance products themselves. Knowing that a car is parked securely in a garage and not on the street could give consumers of the future the option to turn their coverage off and on again. Knowing a driver with telematics is driving extremely slowly, but on an icy road at night, can be used to inform their risk rating.

Doesn’t this new contextual thinking have the potential to create even greater insights which power more informed decision making, improved tactics and best practices, and ultimately more relevant strategic decisions?

*World Happiness Report 2017. Sustainable Development Solutions Network, United Nations