by Ian Smith

A clutch of companies are hoping to break the stranglehold of near 40-year-old technology on specialist underwriting

The bright minds of the insurance industry are used to sizing up big global risks, from systemic cyber attacks to natural disasters.

But some are now turning their focus to a problem that seems almost as intractable: breaking the stranglehold of Excel on the business of specialist underwriting.

Although people have been creating new methods to “analyse and crunch numbers for decades”, said Amrit Santhirasenan, chief executive and co-founder of insurance start-up hyperexponential, “Excel has been the tool of choice.”

Hyperexponential is just one of several new businesses hoping to automate the data input and analysis that is the mainstay of the job of underwriters — the insurance industry’s core decision makers.

To assess big risks such as terrorism and oil spills and to calculate the price of insuring against them, underwriters receive information from brokers, usually in spreadsheet form. They then add other information, including their own claims data.

This is combined with pricing information from actuaries that weights various aspects of the risks, which is also generally created in Excel. All this data combined is used to decide whether to offer insurance cover and at what price. There is a lot of manual work: according to hyperexponential, underwriters spend three hours a day on data entry.

One underwriter said Excel had the advantage of being flexible but it struggled with large data sets, adding “frequent crashes and sluggish performance can lead to considerable time wastage”.

“I think the underwriter’s job has got harder and harder over the last decade or so, because all we have done . . . is give you more information to consume, understand, process, but not given you any new tools to do it with,” said Nigel Walsh, head of insurance at Google Cloud, which provides analytics and product development tools.

Data has been “stuck into spreadsheets and those spreadsheets got more complicated [with] more versions,” he added. “As those got bigger, and bigger, those things took a lot longer to run . . . and you never really knew if you were working on the latest set of data, or the latest version.”

Spreadsheets can also struggle to cope with the vast reams of real-time data on insured assets such as oil tankers and airlines that is now available.

The Excel model “made sense 20 years ago” said Santhirasenan, “because the vast amount of data was just facts and figures that fit really nicely with the spreadsheet. [But] the risks that the world is insuring don’t look like that anymore.”

Enter a series of start-ups and insurer-tech partnerships that are developing specialist insurance pricing software and other analytic tools.

The goal is to automate the collating of data from brokers, actuaries and other internal and external sources and use analytics to help the underwriter decide whether to offer insurance and at what price.

The promise is that it will save time — cutting the process of issuing some quotes from days to hours, and in some cases from hours to minutes — and reduce risks compared with pricing models and analysis that require manual updates.

Several industry veterans told the Financial Times that Ki, a digital insurer in the Lloyd’s of London market that provides insurance cover to businesses, had increased awareness of the benefits of automation.

Ki has automated the quote issuing process, but only where it is providing insurance on a “follow” basis, taking a share of risk behind a “lead” underwriter who will cover claims up to a certain level.

For lead underwriting, the general market view is that underwriters and actuaries need to be deeply involved. But automated risk analysis and pricing tools should mean their involvement can be reduced to focus on the most specialist work.

A recently announced partnership between Google Cloud and Lloyd’s insurer Hiscox trialled automated underwriting on a property sabotage and terrorism policy. The AI-powered model analysed the newly presented risk, checked if it was appropriate for the insurer and even drafted an email to the broker offering cover.

AI can push automation further, Walsh said, allowing underwriters to analyse the data, for example, by asking whether the addition of a certain risk would make their portfolio too concentrated in one area. “You’re making your data conversational.”

Another start-up, Cytora, offers technology that extracts information from insurance broker submissions, builds in data and classifies the risks needing coverage, directing them to the relevant underwriter for review. “Typically [underwriters] have to click three buttons to [issue a] quote,” said Richard Hartley, co-founder and chief executive.