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The business case for brokers to invest in analytics

The business case for brokers to invest in analytics | Insurance Business

The business case for brokers to invest in analytics

Data, analytics, AI: they can often feel like buzzwords that are just thrown around for the sake of it. For brokers and MGAs, who often lack the scale of an insurer, it can be difficult to decode what it all means – and why you should be concerned with any of it.

London is the data hub of Europe “by a huge margin,” according to new research by Equinix published this week. The report found that, not slowed down by Brexit, London is still the most important market in Europe with regards to data, even while Frankfurt surges as the fastest-growing data market in the world. Within insurance and banking alone, it is expected that the sector will transact 1,046 Terabytes per second globally, and 247 Terabytes in Europe by 2021, the research claims – that’s a lot of data.

Read more: Willis Towers Watson reveals insurers’ insights on future of advanced analytics

Technology is increasingly changing the game for brokers, whether it’s through the rise in e-trading or the push from consumers to communicate via new channels such as chatbots or Facebook Messenger. Today, businesses across all sectors are being told that by more effectively using their data and developing analytics tools, they could transform the way that they work.

Brokers are no exception: an Accenture report declared in July that with the arrival of sophisticated data and analytics, the new “Intelligent Broker” has arrived.

But for smaller businesses with less resources and tech prowess, deciding whether data and analytics are worth the time and investment can be difficult. However, there can be some pretty big benefits.

“Harnessing data and analytics allows you to take better decisions at the best time, based on a more accurate understanding of the risks and likely consequences,” Andrew Dunkley, head of analytics at insurance law firm BLM, told Insurance Business.

When it comes to using data and analytics, those who don’t collect enormous amounts of data in line with that of a major insurer – for example, a smaller regional broker – needn’t be put off.

“Sometimes this can take the form of big data tools like machine learning and artificial intelligence, but you don’t have to be constrained by the availability of data or resources to buy complex and expensive systems,” Dunkley said.

“Thinking hard about how you collect and manage data, making intelligent use of statistical approaches like decision trees and adopting an analytics approach to decision-making and forecasting can also yield real benefits,” he went on to say.

An MGA, for example, could benefit from using an analytics tool when it comes to claims, to ensure that its pricing model accurately reflects claims history and reserves, Dunkley said.

Artificial intelligence (AI) however, is probably more suitable for those with a bigger data pool.

“AI is only really applicable where you have a large amount of data, because it relies on using the past to predict the future,” Dunkley said during a Managing General Agents’ Association briefing in London this week.

AI technology requires a lot of data input in order to make it smart enough to make decisions, and it can be a difficult sell to whoever is in charge of the budget.

“It’s complex, its expensive. If you don’t have enough samples, or data, to teach the intelligence, it’s probably not worth doing,” Dunkley said.

For brokers considering upping the ante when it comes to how they use their data, it’s imperative to do the research first and choose the right tool for the job.

“You’ve got to think quite hard about it,” Dunkley said. “There are an awful lot of people at the moment just sticking AI on a product and trying to sell it on that basis.”