January 29, 2013
Predictive Analytics And Underwriting In Workers' Compensation
by Dax Craig
Evidence-based decision-making provides consistency and improved accuracy in selecting and pricing risk in workers' compensation.
Insurance executives are grappling with increasing competition, declining return on equity, average combined ratios sitting at 115 percent and rising claims costs. According to a recent report from Moody’s, achieving profitability in workers’ compensation insurance will continue to be a challenge due to low interest rates and the decline in manufacturing and construction employment, which makes up 40% of workers’ comp premium.
Insurers are also facing significant changes to how they run underwriting. The industry is affected more than most by the aging baby boomer population. In the last 10 years, the number of insurance workers 55 or older has increased by 74 percent, compared to the 45 percent increase for the overall workforce. With 20 percent of the underwriter workforce nearing retirement, McKinsey noted in a May 2010 Report that we will need 25,000 new underwriters by 2014. Where will the new underwriters come from? And more importantly, what will be the impact on underwriting accuracy?
Furthermore, there’s no question that technology has fundamentally changed the pace of business. Consider the example of FirstComp reported by The Motley Fool in May 2011. FirstComp created an online interface for agents to request workers’ compensation quotes. What they found was remarkable. When they provided a quote within one minute of the agent’s request, they booked that policy 52% of the time. However, their success percentage declined with each passing hour that they waited. In fact, if FirstComp waited a full 24 hours to respond, their close rate plummeted to 30 percent. In October 2012, Zurich North America was nominated for the Novarica Research Council Impact Award for reducing the time it takes to quote policies. In one example, Zurich cut the time it took to quote a 110-vehicle fleet from 8 hours to 15 minutes.
In order to improve their companies’ performance and meet response time expectations from agents, underwriters need advanced tools and methodologies that provide access to information in real-time. More data is available to underwriters, but they need a way to synthesize “big data” to make accurate decisions more quickly. When you combine the impending workforce turnover with the need to produce quotes within minutes, workers’ comp carriers are increasingly turning toward the use of advanced data and predictive analytics.
Added to these new industry dynamics is the reality that both workers’ compensation and homeowners are highly unprofitable for carriers. According to Insurance Information Institute’s 2012 Workers’ Compensation Critical Issues and Outlook Report, profitable underwriting was the norm prior to the 1980s. Workers’ comp has not consistently made an underwriting profit for the last few decades for several reasons including increasing medical costs, high unemployment and soft market pressures.
What Is Predictive Analytics?
Predictive analytics uses statistical and analytical techniques to develop predictive models that enable accurate predictions about future outcomes. Predictive models can take various forms, with most models generating a score that indicates the likelihood a given future scenario will occur. For instance, a predictive model can identify the probability that a policy will have a claim. Predictive analytics enables powerful, and sometimes counterintuitive, relationships among data variables to emerge that otherwise may not be readily apparent, thus improving a carrier’s ability to predict the future outcome of a policy.
Predictive modeling has also led to the advent of robust workers’ compensation “industry risk models” — models built on contributory databases of carrier data that perform very well across multiple carrier book profiles.
There are several best practices that enable carriers to benefit from predictive analytics. Large datasets are required to build accurate predictive models and to avoid selection bias, and most carriers need to leverage third party data and analytical resources. Predictive models allow carriers to make data-driven decisions consistently across their underwriting staff, and use evidenced-based decision making rather than relying solely on heuristics or human judgment to assess risk.
Finally, incorporating predictive analytics requires an evolution in terms of people, process, and technology, and thus executive level support is important to facilitate adoption internally. Carriers who fully adopt predictive analytics are more competitive in gaining profitable market share and avoiding adverse selection.
Is Your Organization Ready For Predictive Analytics?
As with any new initiative, how predictive analytics is implemented will determine its success. Evidence-based decision-making provides consistency and improved accuracy in selecting and pricing risk in workers’ compensation. Recently, Dowling & Partners Securities, LLC, released a special report on predictive analytics and said that the “use of predictive modeling is still in many cases a competitive advantage for insurers that use it, but it is beginning to be a disadvantage for those that don’t.” The question for many insurance executives remains: Is this right for my organization and what do we need to do use analytics successfully?
There are a few important criteria and best practices to consider when implementing predictive analytics to help drive underwriting profitability.
- Define your organization’s distinct capability as it relates to implementing predictive analytics within underwriting.
- Secure senior management commitment and passion for becoming an analytic competitor, and keep that level of commitment for the long term. It will be a trial and error process, especially in the beginning.
- Dream big. Organizations that find the greatest success with analytics have big, important goals tied to core metrics for the performance of their business.