What can insurers do with big data?

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We’ve often spoken about the advantages of good information architecture in the insurance industry. Most firms still use a mix of legacy IT systems that don’t share very much data, which present a number of drawbacks compared with more modern, integrated solutions: they introduce scope for inaccuracy, are a drain on underwriters’ and adjusters’ time, increase the time-to-market for new products, and so on.

But what about the advantages of data integration vis a vis business intelligence, or so-called ‘big data’?

This was a topic broached by ratings agency Fitch last week (March 11th) with the release of a report entitled Insurers Begin to Unlock the Power of Big Data. The paper states that advanced analytics are becoming critical to firms’ “profitability, competitiveness and – in the long term – credit ratings”, and that those who are slow to adapt “may lose earnings or market position”.

All in all, a sober warning. So what are the actual applications of big data in the insurance industry? And what can integrated, end-to-end policy and claims systems contribute to this?

Produce quick and accurate quotes

In terms of policy administration, big data is already widely used in one particular area of insurance. For a few years now, motorists have had the opportunity to have their premiums predicated on their actual driving performance via a telematics device. According to research from Ptolemus Consulting Group cited in the Fitch report, insurers sold a total of 4.6 million telematics policies globally in 2014 – a massive increase of 240 per cent on 2012.

This type of policy is pleasing to customers because they’re promised an accurate reflection of their own personal habits in their insurance rates. But it’s also good for insurers, as it allows them to ensure that they’re neither jeopardising their profitability nor customer satisfaction by under or overcharging.

Similar principles could be applied to other insurance products, using a wide range of data types to produce quicker, more accurate quotes than has previously been possible.

Combat fraudulent insurance claims

Big data also generates opportunities for insurers to combat fraudulent claims via innovative techniques like predictive analytics. An solution could, for example, collate historic claims data to build up detailed profiles of genuine versus bogus submissions, potentially detecting common fraud characteristics that would otherwise have been much harder to discover.

Fraudulent claims have dogged insurers for hundreds of years, resulting in significant annual losses and, in turn, higher premiums for honest customers. As such, any opportunity to reduce their damage mustn’t be ignored.

Improving claims process efficiency

The applications of big data in the claims process extend beyond fraud detection, too. Analytics can be used to minimise claim lifecycle times in dozens of different ways, from the initial assignment of a claim right through to the final settlement.

Think about what you’d gain if you were able always to put senior adjusters on complex claims from day one: every member of staff working at their own level, no unforeseen reassignment costs. With big data, you can better understand what constitutes a difficult or potentially long-winded claim and therefore ensure that it’s assigned to the right person.

Analytics could also be used to implement a fast-track or instant settlement process, using data from a wide range of sources to establish profitable payout limits. These tactics are often most applicable when customer satisfaction matters most, such as when a spate of claims are filed after a natural disaster, so it’s important that a firm can handle those submissions in a way that’s agreeable to every party.

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