Companies are embracing tech quicker, which means harnessing big data and analytics to better understand the risks in their organisations.
Big data is growing. According to a report by market research firm IDC in March, worldwide revenues from big data and business analytics will grow more than 12 per cent this year to reach $150.8 billion by the end of 2017.
As the company’s Group Vice President observes: “[Big] data and business analytics solutions have now finally hit mainstream.”
And it’s being used by organisations of all sizes. Another recent survey showed three quarters of SMEs are planning a big data or analytics project within the next 12 months.
Big data is increasingly seen as a leveller, helping small and mid-market businesses compete. That’s certainly the story in insurance, says David Flandro, Global Head of Analytics at JLT Re.
“It used to be that the big insurance companies enjoyed economies of scale when it came to areas requiring real underwriting skill or risk modelling. Now the right data or analysis can give small or mid-sized entities a comparative advantage in certain lines of business.”
A PwC survey in 2015 showed 93 per cent of insurance CEOs thought data mining and analysis was the most strategically important digital technology for their business.
Gregg Holtmeier, Executive Vice President at JLT Re, says this should be broadly positive for insurance buyers, as big data enables underwriters to improve risk assessments and forecast potential losses.
“One benefit is that, in some cases, there is now data to assess risks that were previously uninsurable; another is that a more granular and scientific evaluation should ultimately lead to lower premiums,” he says.
Managing data risk
But the same technology can also be used by businesses themselves – not only to transfer their risk but to manage it better.
It’s something Airmic has started looking at as part of its new ‘Roads to Revolution’ research project to be published later this year, says Airmic’s Deputy CEO and Technical Director, Julia Graham.
“The digital age is giving business great opportunities to capture and manipulate data around risk,”
For risk managers to use it, though, they need to have a sufficient understanding to be able to collaborate intelligently on big data and data analytics with technical staff in the organisation.
Big data possibilities
The potential applications are as varied as the data itself. In some areas of risk, big data analysis is already rapidly becoming the norm.
Another consultant, EY, last year published its Global Forensic Data Analytics Survey showing big data being heavily used in identifying fraud and corruption, with companies increasingly analysing not just traditional transactional data but also emails, social media and text messages, for example.
Yet another area where big data will play an increasing role is fleet risk management. Telematics now allows insurers and fleet risk managers to collect detailed information on vehicles’ position, speed and the time of any incidents for analysis.
The power of big data
Combine this with other data, such as information on weather and temperature conditions, and analytics can start to identify the factors contributing to accidents. “We could get a much more detailed picture of how incidents occur,” says Emma Craddock, a JLT Re Partner responsible for providing analytical services to JLT Specialty and its clients.
Weather data could help in analysing construction claims, she adds, while telematics-type technology is not just applicable to fleet risks, but also large- scale shipping.
More widely, increased analytical power can help draw new lessons from existing data to enable businesses to look at interrelationships between their risks.
“With big corporate clients, we’re already trying to create holistic models of their entire risk world so we can see how risks interact and what a really bad year could look like – even if it came from multiple correlated and uncorrelated risks,” says Craddock.
“It’s not the data or computing power that’s the limiting factor now; it’s only the ideas,” she concludes.
One potential exciting area will be to redefine the period(s) for risk transfer products, as faster accurate risk data will open up possibilities for a more dynamic approach to risk management, providing opportunities and enabling new data-driven products and closer alignment to specific ‘point in time’ exposures.
Analysing data responsibly
There are a few notes of caution, though. The first is that collecting and processing data, particularly personal information, brings obligations to ensure you have the right permissions to process it and that you protect it.
“The more data you collect, the greater your opportunities to violate data protection regulations,” warns Sarah Stephens, Head of Cyber, Content and New Technology Risks at JLT Specialty.
Another related danger is that businesses – both insurers and their clients – gather data unnecessarily.
“One of our concerns is that, when you’ve got the ability to gather data, there’s a temptation to capture everything and then try to work out what you’re going to do with it,” says Graham.
This can actually reduce the ability to extract meaningful information. “Big data can easily become bad data,” she adds.
Finally, it needs to be understood that the better understanding of risk that big data brings won’t necessarily be good news for everyone. As Holtmeier points out, there will be both winners and losers.
“Companies that have the right risk management mechanisms in place – are well managed and take measures to ensure a better outcome for their insurers – should see premiums decrease because we’re going to have data to more accurately assess that risk.
“But there is a flip side; businesses that don’t have those things in place are going to be seen as a worse risk.
“Big data means we’re no longer going to have the good risks subsidising the bad.”
For more information please contact Emma Craddock, Partner at JLT Re on +44 (0)20 7528 4629 or email firstname.lastname@example.org