With advances in technology and data-crunching, business insurance providers got used to having at their fingertips an array of precious information about perils, property characteristics, location, enterprises and their owners.
But, as we know, not all data sets are equal, and insurance providers make a clear distinction between the data that is useful today, and what they say is a ‘must have’ for their workflows for today or for the future. This is what we found out in a recent LexisNexis Risk Solution survey, conducted with 101 respondents from leading UK insurance providers*.
The sample was roughly divided in two halves, between commercial property insurance providers on the one side, and business/small and medium size enterprise (SME) insurance providers on the other. Although some of the preferences on the types of data used and importance coincided for the two groups, others were significantly different.
Our LexisNexis Risk Solutions survey mapped the use of specific data attributes in pricing/underwriting, as well as what insurers ‘must have’ to perform their calculations, in four categories: building characteristics, perils and location, property/business information, and business and business owner information. The findings were then combined, generating some unique insights.
Commercial property insurers said the most important data attribute both to use and to have, related to building characteristics, were: ‘year built’ of the property, ‘square footage’ and ‘property type’. SME insurance providers said that in terms of importance to pricing/underwriting, data on ‘construction frame’of the building is way ahead of anything else.
In terms of perils and location characteristics, the two groups of insurers acknowledged the same top three attributes that they use: ‘fire risk score’, ‘flood risk’ and ‘subsidence risk’.
On property and business information, the two groups also have similar preferences. ‘Building conditions’ top the category, way above all other data attributes, for commercial property insurers, while business/SME insurers place high importance on using and having data for ‘building conditions’ and ‘value at time of survey’.
If we look separately at the results for use of data attributes and importance, additional differences stand out.
For pricing/underwriting, 81% of commercial property insurers said the data attributes they ‘mostly use’ are ‘fire risk scores’ (perils data) and ‘year built’ (building characteristics). Among business/SME insurers, the most used attribute is ‘subsidence risk’ (also in the perils data category), with 78% of the respondents putting this at the top of their list.
When it comes to the data attributes insurers said they ‘must have’, the top ranked amongst commercial property insurers is ‘subsidence risk’, with 74% of respondents saying so, while among business/SME insurers the top ranked need is ‘claims in the last three years’ made by the business owner (72%).
As the survey suggests, it is impossible to overstate the importance of data in the insurance industry. Processes are increasingly digitised, making the use of enriched data attributes easier.
A digitised ecosystem benefits both insurers – they become more efficient at the same time they cut costs – and the customer who enjoys a smoother, faster experience. In addition, a digital process can speed up the purchase by timely flagging inconsistencies or gaps in applications, alerting providers to solve it and ensuring a quicker, steady flow.
Our survey results for each category further inform the way business insurers operate and what data they really value.
In building characteristics, as I mentioned above, ‘year built’ attribute commands the highest usage rate, 81%, in pricing/underwriting by commercial property insurers.
But when we asked what they ‘must have’, ‘property type’ came first, with 57% of the respondents choosing it. While among business/SME insurers ‘year built’ and ‘construction frame’are the most used, both with the preference of 72%, the ‘must have’ attribute is ‘construction frame’, the highest preference, with 63% choosing it.
Of the 18 risk attributes for building characteristics, data related to roughly half of them are currently used by more than 50% of all our respondents. When it comes to the ‘must have’ characteristics, only four were identified by the majority as essential. Among commercial property insurers, besides ‘property type’, those cited as most important were: ‘square footage’ and ‘roof type’ (both chosen by 53%), and ‘year built’ (51%).
For business/SME insurers, besides ‘construction frame’, the top must-haves are related to ‘property type’ (56%), and ‘number of floors’ and ‘roof type’ (both chosen by 54%).
The good news is that, whenever market dynamics change, risk profiles and requirements, systems and preferences change, the way we at LexisNexis Risk Solutions deliver data through a single point of entry makes it an easier prospect for an insurance provider to ingest new data sets into its workflow.
Following the market’s evolution and the move to automation, at LexisNexis Risk Solutions we have invested in integrating our LexisNexis® Map View platform to our Data Hub platform, enabling perils scores and exposure data to be available at point-of-quote alongside all the other data enrichment used for automated business insurance and SME quotations.
Our survey also looked at the view of insurance providers towards data enrichment, its usage and value.
Indeed, when it comes to the business value added from data enrichment, perils data is considered of utmost importance, with 92% of commercial property insurers and 87% of business/SME insurers saying it is ‘extremely valuable’ or ‘very valuable’.
But in terms of current usage of data, while commercial property insurers make heavier use of data enrichment for property characteristics, with 70% reporting it, the highest data usage among business/SME insurers is for business and business owner information, with 71% reporting it.
With the market evolving to offer some real-time short term insurance products, data enrichment, especially for perils attributes, is going to be essential for pricing/underwriting in a fast and accurate manner. A higher degree of data enrichment and data granularity is going to allow insurers to individually price individual risks.
For some business and SME customers after all, a single quoting or underwriting opportunity may be their only chance to buy insurance. If high risks or inaccurately priced risks make them too dangerous for everybody’s book of business, that becomes a lost opportunity for the insurer, for the industry as a whole and for the customer.
Looking back to our survey, there are some more interesting results and big opportunities for ingesting business owner data and business activity data into the insurance workflow.
The data attributes of the business considered of greatest importance to underwriting/pricing in the business/SME sector are ‘claims in the past three years’ and ‘number of employees’, both deemed ‘very important’ by 70% of the respondents. Other risk attributes considered of secondary importance, but still important in the SME space, are data for ‘years trading’ (65% of respondents) and ‘information on CCJs’ (61%).
For commercial property insurers, the top three risk attributes are slightly different: ‘incorporation data’ of the company is the first ranked equally with ‘claims in the past three years’ and ‘years trading’. These are all considered ‘very important’ risk attributes by 53% of the respondents.
When it comes to information on the business owner, insurers of all types place the highest importance on ‘claims in the last three years’ (72% of business/SME insurers and 64% of commercial property insurers).
For business/SME insurers, the second most important attribute was deemed to be ‘information on CCJs’ (67% of respondents). Commercial property insurers, however, placed ‘information on CCJs’ at the bottom of their list of needs, together with ‘number of directorships’, with only 38% saying that these attributes are ‘very important’.
LexisNexis Risk Solutions is currently working with our customers to assess requirements for a market-wide claims solution that will help with assessment of perils risks at point-of-quote. It will be based on the same principles of the contributory data platforms and claims data platforms that we operate around the world. The underlying principle is that commercial insurance providers have been telling us for some years there’s an opportunity to make underwriting improvements with claims data, rather than relying on self-declared information from the insured, or the data held by a single insurance provider.
There’s an opportunity to better share historical claims data on any specific property, in an efficient manner, so we have in one place its claims history, and avoiding duplication of effort (and cost) across the industry.
Insurance providers can then gain visibility on a specific property and its history across multiple occupants or businesses related to a particular type of claim. For instance it could be historical claims for ‘trips and slips’, crime-related or other perils-related claims. This type of precision and additional insight is going to add substantially to the predictive model of risk and it’s information that is obviously very valuable for pricing and staying competitive.
* The Business/SME & Commercial Property Insurers Study (January 2019) surveyed 101 UK commercial insurance providers. LexisNexis Risk Solutions was not identified as the sponsor of the survey.
For more insights from these research results download the LexisNexis Risk Solutions commercial insurance white paper ‘A Digital Divide in Commercial Insurance’
Follow the link to the LexisNexis Risk Solutions website to find out more about how we support insurers.