The insurance industry has a reputation for being slow to change, but the “big data” revolution is driving significant changes in workers’ compensation underwriting. The emerging use of “big data” analytics in underwriting is diminishing the purpose and value of the experience modification factor and beginning to affect middle-market agents and their clients.
Big data has already redefined industries like retail (Amazon), entertainment (Netflix) and content publishing (Facebook). Stock and mortgage brokers are well ahead of insurance with their own predictive models. Big data in insurance is still under the radar for many, but it’s beginning to affect pricing and how agents work with their middle-market clients.
The National Council of Compensation Insurance’s (NCCI) experience rating plan was created to adjust premium costs to reflect “the unique claims experience of each eligible individual employer relative to other employers within the same industry group.” The experience rating plan helps insurers charge the appropriate premium for an individual employer’s work comp policy. Or, as one actuary stated, “The experience mod is a predictive indicator of future losses.” Traditionally, a higher experience mod predicts that the employer will have greater than expected losses in the coming policy period, so the insurer needs additional premium for the risk.
Many experts would agree that the experience rating plan, created in the 1930s, has historically served the insurance industry well. However, we are entering a new era where individual insurers are building their own predictive analytics models because of:
- Recent and swift explosion of huge databases;
- Inexpensive computing power and data storage; and
- Advances in data acquisition and aggregation from multiple sources.
Computer hardware and software advancements, along with smart people, now allow insurers to quickly process millions of calculations, analyze the data they produce and promptly validate their emerging predictive models. Prior to these technological advances, insurers relied on the rating bureaus, such as NCCI, to collect and manage the data.
In addition, there are significant inefficiencies in the rating system that data-savvy insurers can leverage to gain a competitive advantage. For example, they can analyze their own data instead of relying on the rating bureau’s broader, aggregate view to create a competitive advantage.
Let’s assume the rating bureau’s data indicates that claim costs are rising for plumbers in a given state. The rating bureau will likely increase advisory and expected loss rates for plumbers in the entire state. However, an individual insurer may analyze its own book of business and see a decrease in claims costs for that state’s plumbers. The carrier could set a lower premium for plumbers and capture greater market share from competitors that only use aggregated rating bureau data.
It’s no surprise that large global actuarial and consulting firms are working with insurers to develop and enhance predictive models. Insurers already possess a treasure trove of data just waiting for those, affectionately known as “data nerds,” to spin it into gold. As one actuary from a well-known consulting firm said at a recent industry conference, “Underwriters have been using about six to eight data points to determine acceptability and pricing of a risk. We can build them a model with 400 to 600 data points.”
Big data brings big opportunities to insurers and agents; however, as with any collision of old-world and new-world methodologies, there will be some challenges and casualties. For example, let’s assume an underwriter receives an application for a workers’ compensation renewal, and the experience modification factor is renewing lower than the prior year. And the governing class code advisory rate is lower, as well.
However, the insurer’s predictive model indicates an increase in pricing is needed. As a result, the underwriter removes the scheduled credit and adds a scheduled debit to the pricing. Now, the agent has to explain an unexpected higher premium to the client.
Or, worse, the underwriter cannot even make an offer because the maximum allowed scheduled debit will not provide the pricing needed, according to the predictive model. In this case, an applicant’s reduced experience modification factor actually prevented the employer from getting a renewal offer from its current or preferred insurer. This may seem crazy, but when you add more and new data to a pricing model, you often get a different indicator.
Enhanced data analytics can turn traditional rating and pricing upside down. The purpose of the rating bureau’s experience rating plans is to assist the insurers appropriately set a price for the risk. However, with advanced analytics and regulations mandating the use of the experience mod, employers may find themselves in the residual market because the insurer was unable to make an offer at their price.
Workers’ compensation experience rating and experience modification factors are not going away any time soon; they are enmeshed into each state’s regulatory and statutory framework. And not all insurers will create and use their own predictive models, so some will continue to rely on the rating bureaus. However, you’re probably beginning to see anomalies between the old world of “predictive indicators of future losses” and the new world of insurance-specific predictive analytics.
Agents must not only be aware of these underwriting changes but must educate their clients and prospects. The brightest future belongs to employers that can move the loss data in the right direction over the long term. The agent’s role is to help them establish processes to make that happen.
As with most leadership challenges, agents need to start with a new conversation and dialog. Questions might include:
- Are you aware of how the “big data” revolution is affecting your insurance program and pricing?
- Has anyone shared with you how the insurance company’s underwriting process is going through its most dramatic change in more than 50 years?
- Have you taken steps to adapt and align your business objectives and risk management practices to leverage this new approach?
Agents often say they want a way to differentiate in a crowded and noisy marketplace. This underwriting revolution presents a sustainable competitive advantage to those willing to invest in gaining knowledge and expertise.
