Workers’ compensation is widely recognized as one of the most challenging lines of business, suffering years of poor results. Insurance companies are under increasing pressure to achieve profitability by focusing on their operations, such as underwriting and claims.
Insurers can no longer count on cycles, where a soft market follows a hard one. The traditional length of a hard or soft market is evolving in a global economy where capital moves faster than ever and competitors are using increasingly sophisticated growth, segmentation and pricing strategies.
Insurance executives also cite regulatory and legislative pressures, such as healthcare and tax reform, as inhibitors of growth. Furthermore, medical costs continue to rise, making it particularly difficult to price for risk exposure. The long tail of a workers’ compensation claim means that the cost to treat someone continues to increase as time elapses and becomes a compounding problem.
Despite recent improvements in combined ratios, there are still many challenges within workers’ compensation that have to be reconciled. The savviest insurers are evaluating the availability of technologies, advanced data and analytics to more accurately price risk – and ultimately ensure profitability.
The ‘Unknown’ in Workers’ Compensation
Information asymmetry has made it difficult for insurers to accurately determine who is a high-risk customer and who is low-risk. At the point of new business, a workers’ compensation insurer is likely to have the least amount of information about those they are insuring, and it’s easy to understand why. The insured knows exactly who is on the payroll and what types of duties employees have. Some of that information is relayed to an agent, and then finally to the carrier, but, as in any game of telephone, the final message becomes distorted from the original.
This imbalance of information is one reason why fraud is rampant, and why insurers ultimately pay the price. Payroll misclassification – or “premium fraud” – occurs when businesses pay salaries off the books, misrepresent the type of work an employee does or purposely misclassify employees as independent contractors. Some misclassifications are not nefarious. But whatever the cause, they create significant revenue and expense challenges for carriers that rely on self-reporting.
The ‘Silent’ Killer
Without the right insight or analytical tools, insurance companies have a hard time discerning between their policyholders and making consistent and fair decisions on how much premium to charge on each policy. When an insurer begins to use predictive analytics, competitors that are still catching up run the risk of falling victim to adverse selection. When we hear executives say things like, “The competition has crazy pricing,” it raises a red flag. We immediately begin looking for warning signs of adverse selection, such as losing profitable business and an increasing loss ratio.
The problem is that it takes time to recognize that a more sophisticated competitor is stealing your good business by lowering prices while also sending you the worst-performing business. By the time you recognize adverse selection is occurring, you’re falling behind and have to respond quickly.
The Power of Actionable Data
Fortunately, there are technologies available for insurers of all sizes to make more informed, evidence-based decisions. But when it comes to data, there is still some confusion: Is more data always better? And how can carriers turn data into actionable results?
It’s not always about the volume of data that an insurer has; it’s about the business value you can derive from it.
If an insurer is just beginning to store, govern and structure its data, it is likely not receiving actionable insights from historic data assets. Accessing a more holistic data set with multiple variables (from states/geography, premium size, hazard groups, class codes, etc.) through a third party or partner can help to avoid selection bias, while encouraging rigorous testing and cataloging of data variables. Having access to a variety of information is key when it comes to making data-driven decisions.
The conundrum insurers face when delivering actionable intelligence that underwriters can use is that they only know the business they write. They know very little about business they quote and nothing about business they don’t even see. What complicates this picture is that an insurer’s data is skewed by its specific risk appetite and growth strategies. It’s up to the insurer to fill in the blind spots in its own data set to ensure accurate pricing and risk assessment. As we know, what an insurance company doesn’t know can hurt it.
As insurers increasingly turn to advanced data and analytics, the next question that keeps insurers up at night is, “When everything looks
good, how do I know what isn’t really good?” One way that Valen Analytics is helping insurers answer that question is by providing companies with a “Risk Score
,” a standard measure of risk quality. By tapping into Valen’s contributory database, workers’ compensation underwriters can have better insight into all the policies they write – even historically loss-free policies. In fact, the Risk Score accurately identifies a 30% loss ratio difference between the best- and worst-performing loss-free policies. This is one example of how the power of data can push the industry forward.
Despite its many challenges, the workers’ compensation industry is becoming more analytically driven and improving its profitability. A comprehensive data strategy drives pricing accuracy and business growth while allowing insurers to achieve efficiencies in underwriting decision-making. By keeping up with technological advances, insurers can use data and analytics to grow into new markets and areas of business, while also protecting their profitable market share.
While NCCI’s annual “State of the Line” report labeled workers’ compensation as “balanced” this year, we may soon see the integration of data and analytics push the industry to be recognized as “innovative.”