Claire Williams, Commercial Account Manager and Louise Johnson, Director of Product Management at LexisNexis Risk Solutions UK and Ireland, discuss how MGAs can use data and technology to stay on top of emerging product trends and react quickly to customer demand
Although the amount of time it takes for an underwriter to make an initial assessment of a commercial risk can be incredibly short, in many instances quote delivery can be slowed down by the manual underwriting processes that are commonplace in more complex commercial insurance risks. The commercial insurance market may have evolved from the days of underwriting slips and face-to-face meetings with e-trading of some more straightforward small and medium-sized enterprise (SME) risks, but automated commercial risk assessment based on a clearer view of the customer at the point of quote is still lagging quite far behind the personal lines market.
Using the commercial property market as an example, a LexisNexis Risk Solutions study found that close to half of insurance providers could lose market share if they fail to adopt a greater level of automation across their businesses.[i] Just 23% of providers confirmed their underwriting and pricing processes are all or mostly digitised whilst 49% said they are all or mostly manual, leaving 28% using a mix between the two.
Some of the pain points still prevalent in the market today are referrals, ambiguity around eligibility and fraud. With utmost good faith at the heart of the commercial industry there has been a huge reliance on the duty of disclosure. Also, the way that commercial risks are validated varies hugely across the board and can make it a long and complex process. What if there was a simpler way?
Looking to personal lines for data inspiration
Over the last decade personal lines insurance has been on a big data enrichment journey, particularly at point of quote.Initially, underwriters were interested in public credit and identity data combined with claims history data, especially in the motor market. As data in the market matured, these insights moved to point of quote, and credit and claims history datasets are now seen as ‘basic hygiene checks’ amongst the variety of other data available. This includes fraud and identity datasets, No Claims Discount (NCD), vehicle/motor policy history and residential property data, all of which give a more holistic view of risk.
On average, personal lines insurance providers use around five datasets in the automated data enrichment process for quoting and underwriting, but there are some that can use up to 13 during the assessment process. Contributory datasets such as policy history tend to be top of the list as they offer a market-wide view of the market’s experience with the customer.
Due diligence checks prior to quote
Commercial risks often come with more complex data requirements that need different underwriting expertise. Added to this, utmost good faith just doesn’t cut it anymore in the commercial market, and capacity protection in the MGA landscape is paramount. This means that performing due diligence check upfront and having the correct tools to conduct searches in crucial. This is where data enrichment can play a valuable role, helping MGAs perform the necessary due diligence checks prior to the quote. This can help with risk management and can also allow MGAs to perform correct anti-fraud and fraud identification measures as well as manage their portfolios.
Portfolio performance is also key as just a handful or poorly assessed policies can lead to underwriting restrictions, more regular audits and even a reduction or loss in capacity.
Back to basics
Gaining a full picture of the business MGAs are looking to insure starts by selecting the correct business name and location and establishing some basic facts about the company.
This has been easier said than done in the past.
One of the biggest challenges for the market to date has been identifying the commercial entity. Businesses can range from sole traders, partnerships, limited companies and non-profit organisations which can be difficult to pinpoint. The commercial location can be as much of a struggle to identify; is it just a small workshop or a business on a development site with multiple commercial entities inside? There are hundreds of commercial products and historically the data capture in the market has not been to a high enough quality to complete data enrichment.
The solution lies in matching routines that home in on the precise business and its location. Business data can confirm how big a business is, how long it’s been trading, what its financial position is but also importantly who is involved in its management.
With these basic facts established, MGAs can then apply this data across the entire insurance continuum. Bespoke risk management per risk is possible when the checks and decisions are automated upfront.
Challenging underwriting practices to stay ahead of the curve
Not only can basic data enrichment help MGAs improve their on-boarding services, but perils and geospatial datasets allow them to go even further to mitigate risk. Climate change is already shifting the UK’s high-impact weather[ii] and challenging underwriting practices to ensure risk management stays ahead of the curve. The whole insurance industry has an obligation to understand extreme weather events in order to help plan for severe damage and protect customers.
In the past, an insurer might have declined cover for properties within an entire postcode because they fell within a flood zone, but recent extreme weather events now require the industry to look beyond simple postcode assessment. Real-time environmental and geospatial data intelligence are already highly valued by the commercial property market[iii]. This data can be accessed in relation to the precise location of a property, even the footprint of the building to define its associated peril risk. This helps MGAs and insurers to refine the underwriting process and acceptance criteria in real time.
As the commercial insurance market shifts more towards automated underwriting, the technology used to create these insights can bring all the data in house and convert it into scores that can be used for rating and underwriting. This supports MGAs in managing risk and maximising the capacity to write business without incurring additional reinsurance costs. Combining data attributes from multiple datasets also allows MGAs to validate and present an accurate view of risk to the market.
Breeding confidence and success
Given the complexity of some commercial policies, underwriters need time to fully understand the risk. Data enrichment can deliver this valuable time, allowing for key assessment up front and reducing referrals in e-traded business. This can allow reward and discounts for well-run policies and the ability to automatically decline business that MGAs don’t want to write.
Insurance providers are looking for more technology-enabled MGAs that stay on top of emerging trends and able to react to evolving customer demand in a post-COVID landscape.
As more MGAs look at data enrichment solutions in the market, they will gain an increased awareness of their appetite and risk accumulations which can lead to heightened portfolio management and a better running book with a natural reduction in fraud. This breeds confidence which can in turn breed success and allow them to make more informed decision on risks.
[i] LexisNexis Risk Solutions has published a white paper, ‘A digital divide?’, sharing the results of the latest study, which involved more than 100 insurance professionals working in relevant lines of insurance.
[iii] LexisNexis Risk Solutions has published a white paper, ‘A digital divide?’, sharing the results of the latest study, which involved more than 100 insurance professionals working in relevant lines of insurance.