3 Keys to Adapting to Capture the Small and Medium Business Market

The challenges of underwriting the small and medium business (SMB) market are well documented: Challenging life expectancy of SMBs and thin margins make pricing efficiency vital and difficult. Traditional underwriting methods are costly and slow in a market where volume is key and waves of changes impact risk.

SMBs typically behave like consumers in wanting fast and convenient coverage at a low cost. To meet these demands, most underwriters are forced to assess risk based on broad statistics such as sector and turnover gathered on an annual basis, resulting in very little differentiation between risk profiles.

This is especially ineffective when insuring microbusinesses and gig workers, where risk profiles are even more complex, fast moving, and opaque. These challenges have been compounded by the global pandemic: pricing pressure has increased, small businesses are going bankrupt at a faster rate, and business profiles have changed to such a degree that any risk assessment conducted 12 months ago borders on meaningless.  Traditional underwriting methods before the pandemic are now reaching their limits.

Given these challenges, commercial insurers may question if this segment is still worth prioritizing. One option is to avoid it altogether and focus on insuring the needs of large enterprises. However, not only does such a defensive strategy exclude a vast market; it also prolongs confronting inevitable challenges as big businesses are undergoing similar changes, albeit at a less spectacular pace. 

The alternative option is for insurers is to rethink how they serve this market and pre-empt the permanent changes in insurance needs. Like all industries, insurance is going through its own evolutionary process. Those that adapt will succeed; those that do not will be left behind. 

Proactive leaders should therefore ask themselves key strategic questions: Are traditional methods of underwriting still relevant for the new wave of SMBs? Do we offer products that are agile enough to flex with fast-moving businesses? Will tech-savvy executives have different expectations for the type of insurance they buy?


Advanced Analytics: Underwriting Fit for the New World


For those insurers that take the second option and position themselves for the SMB recovery, we believe that—more than ever before—success in this space requires a relentless focus on three core areas high-quality, on-demand data; risk-differentiating insights; and customer convenience and competitive pricing.

The conflict inherent in traditional underwriting is that improvements in the first two areas immediately compromise the third. But what if there was a method that could achieve all three simultaneously?

The emergence of advanced data-driven underwriting, based on predictive analytics and artificial intelligence, is enabling insurers to do just that. Data-driven underwriting works by gathering a vast amount of external and internal data to build sophisticated and real-time risk profiles in a matter of minutes.

This in turn enables underwriters to make up-to-date and competitive decisions that accurately reflect the risk rather than relying on a blunt set of risk classifications. This approach enhances performance in each of the three core areas:

High-quality, on-demand data: According to research by Accenture, 15%‒20% of key data from agents or customers is wrong. Advanced analytics bypasses the risk of human error by instantly accessing data from a wide range of sources, including public, proprietary, non-obvious, and historical data. These include key risk indicators such as demographics, crime rates, employee sentiment, social media sentiment, and hundreds of other characteristics that can be highly predictive of loss. Data can be accessed almost instantly, enabling underwriters to track the changing risk profiles of individual policies and entire portfolios throughout the policy lifecycle.

Risk-differentiating insights: Advanced behavioral analytics and machine-learning algorithms are applied to the data to provide a holistic, predictive risk profile in real-time for any given business. This process reveals that the risks inherent in two seemingly similar businesses suddenly look very different.

Customer convenience and competitive pricing: Properly conducted and structured, the review process can be undertaken with only two pieces of information: business name and address. This low-touch, automated underwriting monitors and adapts changing risk profiles with very little input required from the customer. Meanwhile, pricing remains competitive because of accurate risk assessment rather than artificial pricing pressures. The results of this approach are tangible and significant. For example, on-demand risk assessment engines for Small Business Workers’ Compensation has been proven to have a 2%‒3% impact on loss ratios when combined with in-house models.


From Risk Underwriter to Risk Partners

Over the past few years, advanced analytics has been used by a growing number of insurers to gain a competitive edge. However, the monumental changes triggered by the global pandemic have moved this trend to an inflection point: analytics is no longer an option but a necessity.

In our new world of fluid business models and fluctuating risk variables, inaccurate data will produce results that are increasingly detached from reality. Furthermore, as SMBs digitalize and modernize, we anticipate that their demands and expectations as insurance customers will change too.

The preference for a low-touch digital experience will grow, and savvier policyholders will seek greater product personalization and flexibility. A survey by McKinsey of SMBs in the UK showed that 47% of respondents prioritize flexibility in product coverage, a rise of four percentage points in under a year.

Analytics will be instrumental in facilitating change, resulting in an entirely new style of insurance product that automatically aligns and moves with the business it covers.


Paul Mang

Paul Mang is Chief Innovation Officer at Guidewire, a provider of predictive analytics, risk insights and business intelligence solutions for the P&C insurance industry.  He is the former Global CEO of Analytics at Aon plc.