By Dax Craig, Co-founder and CEO, Valen Analytics
It wasn’t long ago that workers’ compensation was recognized for being an unprofitable line of insurance. However, the wide adoption of underwriting analytics helped turn the line around, and the industry has seen profitability for six consecutive years. But underwriting is only one component of the insurance equation, and the gains in profitability are at risk due to the looming threat of claims severity. Research from the Insurance Information Institute found a +7.6% increase in incurred losses and loss-adjustment expenses in the first nine months of 2016. While claims frequency has declined over the past 20 years, severity has escalated with a 226.7% rise in medical cost, according to NCCI.
California recently shared that litigated claims contribute only 20% of the total claims with indemnity payments, but more than half (~54%) of total incurred losses. Litigated claims tend to have higher severity, meaning California isn’t an outlier. Rather, it’s a trend we can expect to be repeated in other states.
Work comp insurers have, by and large, been slow to adapt to these shifting dynamics. The longer a claim goes unpaid, the more expensive and complicated it will be. Therefore, it is in the best interest of insurers to obtain executive level support for predictive analytics in claims handling.
Why is identifying severe claims difficult early on?
Traditional claims processing has struggled to reduce severity because claims data is often too limited to accurately identify problematic claims. As a result, business rules for flagging a claim are too broad, creating a logjam of false-positives that drive inefficiencies for the claims team. Seasoned adjusters are being forced to review too many claims, instead of being able to focus attention on the claims more likely to spiral out of control.
This is where predictive analytics can be helpful. Similar to using analytics in underwriting that minimize the number of policies an underwriter vets (but maximizes their time on the most difficult policies), claims analytics can be used to significantly reduce the volume of claims an adjuster must spend time on.
How predictive analytics simplifies and reinforces traditional claims processes
Claims triage, or the process of handling a claim, has a variety of steps. Deploying “scores” (likelihood of severity) early within the claims lifecycle can help identify problem areas sooner and with more diligence. LexisNexis found significant ROI using predictive analytics on claims, specifically 15-25% lower severity payments, 25-29% lower attorney involvement and 5-15% shorter cycle times.
Once a claim has been filed, the first step is to assign an adjuster to the case. Traditionally, insurers must look at the incident report to identify the complexity of the claim and decide whether an experienced or novice adjuster is necessary. Predictive models can alert insurers immediately whether a claim has a high risk of becoming a long tail claim or a jumper claim. Models can also recommend a more experienced adjuster be assigned to the case. Conversely, a low-touch claim can be assigned to a less experienced adjuster who can manage it more quickly through the final payment and expedite the close date.
In addition to improving decisions on which adjuster is assigned to a case, analytics can also help insurers make decisions on whether added measures are needed to get someone back to work sooner. For example, the deployment of scores throughout the lifecycle of the claim makes it easier to more rapidly pinpoint crucial pieces of information. This might mean assigning a nurse case manager to oversee a claimant with warning signs of decaying health, or one who demonstrates characteristics of stretching their injury to avoid returning to work.
If a claim requires supervisor review, analytics can provide a number of ways to reduce the scrutiny and amount of time spent with the claim. Data can streamline the evaluation strategy by notifying which claims have potential for subrogation, project the ultimate payments of a claim, and estimate a closing date.
Insurers need to better understand how to adjudicate claims decisions across their book, especially with a rise in severity. With predictive analytics, insurers replicate the success that they had when first incorporating data analytics into underwriting and focus their most experienced adjusters on the most complex claims. The negative effect of remaining stagnant in claims handling isn’t immediately obvious, meaning that few executives likely feel their claims department is “on fire”. But to balk at innovating on such a crucial part of the insurance process will silently hurt an insurer’s business.
About Dax Craig
Dax Craig is the CEO and president of Valen Analytics®, an Insurity company, and provider of proprietary data, analytics and predictive modeling for P/C insurers. Valen leverages its large contributory data assets to help carriers price insurance policies more accurately and achieve lower loss ratios. Prior to founding Valen in 2004, Dax was founder and CEO of Xertex Technologies, which was acquired by a global leader in the wireless antenna industry, Centurion Wireless. Dax proceeded to serve as vice president of global business development at Centurion, where he was directly responsible for global business development including sales, market definition, market segmentation, market research, strategic planning, and market development. Dax graduated from the University of Tulsa with a bachelor’s degree in business administration and marketing. He earned his MBA in finance from the University of Colorado at Boulder.
About Valen Analytics
Valen Analytics, an Insurity company, provides proprietary data, analytics and predictive modeling for property and casualty insurers. We work with insurers who are actively looking to utilize modern approaches to pricing, risk selection, claims triage, and premium fraud. Our customers are focused on increasing competitive pressures, fighting adverse selection with innovative solutions, and raising awareness for the impending “experience gap” with initiatives such as Insurance Careers Movement. Our customers span many lines of business including Homeowners, Personal Auto, Workers’ Compensation, Commercial Auto, Commercial Package, Commercial Property, and BOP. Learn more about Valen at www.valen.com.