For tech-forward insurers, predictive risk analytics has become key to profitable underwriting in the enormous market for small-business workers’ compensation insurance. But in the age of COVID-19, the competitive importance of these technologies may very well become a make-or-break proposition.
It could be years before we fully understand the damage done to small businesses by the novel coronavirus known clinically as SARS-CoV-2.
Thanks to shelter-in-place orders, untold legions of small professional services firms and startups are participating in what Time Magazine has famously called “the world’s largest work-from-home experiment”—ready or not. On the other end of the spectrum, an army of “essential workers” faces longer hours, brutal working conditions, and unnerving levels of risk to their personal health.
Some insurers may decide that this segment is just too risky, considering the economic uncertainty that has already seen large numbers of small businesses in hospitality, food services, construction, and other fields close temporarily or shutter their doors completely. But that could be a costly mistake—and a goldmine for more tenacious competitors armed with the right technologies.
Predictive Risk Analytics: Keys to an Economic Powerhouse
Even back in that robust economy of long ago—also known as mid-March 2020—small businesses represented a Darwinian challenge to many insurers. Eighty-eight percent of all businesses in the US have fewer than 20 employees, and those same businesses drive 44% of the US economy. They also make up the bulk of all employment in key industries for workers’ compensation.
In other words, it’s a lucrative market. But it’s also a challenging one. Lower premiums, the high costs associated with underwriting itself, prospecting, and a large percentage of business failures each year collectively mean that success in this segment requires high throughput. Yet accurate risk assessment remains a serious weakness for many, if not most, underwriters.
Without access to high-quality granular data on small businesses, most underwriters are forced to assess risk based on broad statistics. For instance, if it’s a construction company in Utah, you price a policy for a construction company in Utah. For far too many, “deep research” means a Google search and at most a quick look at Yelp and Glassdoor. However, this is all very time intensive, especially when customers want answers in minutes instead of days. It also depends on assessing small pieces of incomplete information.
The good news: A new generation of predictive risk analytics solutions has begun to change all of this. Guidewire’s own solution, for instance, uses modern machine learning to deliver an accurate, real-time risk assessment on any business of any size—quickly, easily, and on demand.
But how can such technologies help insurers successfully serve a market that faces so much uncertainty?
Navigating Underwriting’s “New Normal”
To understand how predictive risk modeling can help insurers make sense of a post-pandemic world, it’s instructive to look at how today’s most advanced solutions work.
Our own solution, Guidewire Cyence™ for Small Business for Workers’ Compensation, instantly accesses the world's largest collection of public, private, and proprietary data sources—including demographics, crime rates, hiring, pay disparity, employee sentiment, customer satisfaction, social media sentiment, proximity to public services (such as fire departments and hospitals), online activities, and more than 700 other non-obvious characteristics.
It then applies advanced behavioral analytics and machine-learning algorithms to assemble a holistic, predictive risk profile on that business within just two minutes. For insurers who use it, Guidewire Cyence for Small Business has proven central to reducing loss ratios by up to 3%.
In the face of today’s new realities, ask yourself the following questions:
If small-business employees in some industries increasingly work from home after the outbreak, how liable is the employer to claims arising from home-office ergonomics that result in repetitive stress injuries?
What factors must be considered if some small businesses give up their physical locations and operate out of their owners’ houses?
What if lockdown-driven customer behavior becomes so ingrained that some retailers, restaurants, and other businesses find sustained demand for curbside pick-up or home delivery?
If COVID-19 proves to be seasonal, or a vaccine remains elusive, what impact could possible future lockdowns have on essential workers? What if the definition of “essential” also continues to evolve?
With these and countless other possible variables in play, one thing is clear. Solutions that provide comprehensive risk intelligence drawn from algorithms that are continuously trained, tested, and refined by top experts will be critically important as the data that’s required for fast, accurate risk decisioning rapidly evolves.
In Crisis Lies Opportunity
Of course, there’s a pertinent question in all of this. If small businesses continue to be hobbled by the precipitous economic decline seen since the outbreak, is this segment really worth pursuing?
Yes, it is—even more so. Although recovery will come in fits, starts, and setbacks, the entrepreneurial spirit is alive, kicking, and looking for opportunities. And it will be fueled by pent-up demand, the $2 trillion CARES Act that includes $345 billion in loans for small businesses to help keep the “workers” in “workers’ comp” gainfully employed, and any additional stimulus yet to be announced.
But here’s a word to the wise: If Google searches weren’t much help to risk assessment before the pandemic, their usefulness has since been largely nullified.
Instead, predictive risk analytics is set to become a game changer for underwriters seeking to profitably serve the market for small-business workers’ compensation during a period of tremendous change—and unprecedented opportunity ahead.
For more information, please watch the May 14th InsurTech webinar "COVID-19's Impact on the Future of Work and Workers' Compensation."