Commercial carriers should be prepared: sophisticated data analytics is coming to commercial insurance and generating a host of advantages, as Dave Ovenden explains
The use of advanced data analytics has become normal among personal lines insurers, but this "arms race" is now spilling over into commercial insurers.
The theory is well proven: better analytics leads to better-informed decisions; it raises the speed at which insurers operate; and previously unconnected business units can be integrated to mutual benefit.
Though the commercial insurance market remains a people business, underwriters' personal experiences is no longer enough for them to make the best decisions. They need hard figures and thoughtful analysis, too.
Aviva, for example, by drawing on the expertise and experience of its personal lines business, has introduced analytics into its commercial underwriting, spanning a great range of customers. Its underwriters use models to bring what it calls "the best of analytics science and underwriting art" to each case, with the aim of improving consistency and their understanding of the data.
Insurers are already considering how predictive analytics can inform more than just their pricing.
Sophisticated analytics about a portfolio and a broker, for example, can forewarn the underwriter to expect a particular renewal could be difficult.
At the company level, deeper understanding gained about a portfolio of commercial business can lead to smarter and more targeted decisions about placing reinsurance programmes or securitising and selling blocks of the business, giving an overall competitive edge.
And at senior management level, advanced analytics can aid those facing pivotal decisions about automating operations. This has arguably become an industry mantra, and decision makers may be tempted to automate interactions with all smaller commercial insurance clients.
But analytics that elegantly dissect the customer base, products, segments of commercial business and trades can give executives a better idea of what is safest and most logical to mechanise, and what they should leave untouched.
Extracting the greatest business value from data can hinge as much on an insurer's culture as on its processes.
At some less integrated firms, the actuaries, underwriters and claims departments interact only rarely. The actuaries know little about what the underwriters want from them, and the underwriters are ignorant of what benefit the actuaries can be for them. Part of the task of Willis Towers Watson is to show a client's underwriters of commercial cover what is possible, and help actuaries understand that even relatively simple predictive models can help.
In addition, while statisticians may be the first employees to encounter commercially valuable, raw data, they may not by their nature be best at promoting its importance or usefulness within the insurer.
Once they convince their colleagues, however, we witness organisations becoming 'hooked' on extracting the maximum value from their statistics.
Pick your battle
Different commercial underwriters will choose to fight on different fronts. Those whose edge is in distribution exploit their data on this activity, whereas others with deeply technical pricing may leverage very different statistics.
Regardless the volume and type of data that is available, our goal for each client is to extract as many economically valuable insights, as much predictive value and support for decisions as possible.
We commonly begin with simple transactional data. It may not be explicitly designed for pricing or underwriting, but we derive richer information via sector or segmented analysis. We can unearth where cross-subsidies exist, or analyse pricing against peers, for example.
Commercial carriers can analyse which of their underwriters or geographies are doing the best job with an individual risk within a portfolio. And they can examine what pricing they would require to hit a profit target in a given segment.
Clients can use our software solutions to conduct analysis and act on it immediately. They can view data on pricing or performance management of specific products, generate scenarios, analyse the outcomes and take and implement decisions about pricing all on the same day.
In the analytics arms race, commercial carriers that continue to believe data and analysis is only tangential to their business will lose out to more data-savvy competitors.