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When claims spiral out of control

Even the most seasoned adjusters occasionally find that claims can spiral out of control. This can happen for a number of reasons: heavy caseloads, frequent file transfers, lack of information and inexperience in spotting the severity indicators of a particular claim. Assigning the right claim to the right adjuster is only half the battle. The adjuster must have the experience and skill necessary to identify severity indicators early on in the life of the claim.

The adjuster must also devise an appropriate plan to bring the claim to resolution and get the injured claimant to maximum medical recovery and then back to work, if possible. No matter how deep their experience, adjusters will likely encounter claims with unfamiliar characteristics. For a newer adjuster, this can be daunting. Controlling costs becomes even more difficult when claims are reassigned and each successive adjuster must make a seamless transition in identifying the proper steps to take in handling the claim. With each reassignment, the likelihood of potentially severe claims falling through the cracks — resulting in skyrocketing costs — increases exponentially.

Using data and analytics to improve outcomes

Spotting high-risk claims early in their lifecycle can be key to mitigating costs and improving outcomes for all parties involved. With the increasing availability of big data, it has never been easier to incorporate predictive modeling into the claims process. This changes the process from relying on the experience of a single adjuster to drawing on the claims experience at the company level, which may mean hundreds of thousands of claims — or even to the industry level, which may involve millions of claims.

The challenge of correctly identifying many potentially severe claims early-on can confound even the most seasoned adjusters. Data supplied by a solid predictive model can accelerate experiential learning and provide a safety net for adjusters by providing data-driven indicators to flag claims that may spiral out of control without proper attention. Pairing severity identification with business process can yield even greater results.

Given a predictive model providing a range of severity scores, for example, it makes sense to assign the least severe claims to newer adjusters, providing them with low-risk claims so they can learn the fundamentals of claims handling. As experience levels rise, so should the complexity of claims assigned. This allows adjusters to deepen their experience level while optimizing claim outcomes for claimants, employers and insurers.

Beyond triaging claim assignments, severity thresholds can be built into the claims handling process. For example, auto adjudication may be incorporated for the lowest-severity claims, while actions such as mandating nurse case management and increased managerial oversight may be considered for the highest-potential claims. This allows a claims department to focus its time, energy and expenses where they can have the greatest impact on high-severity claims.

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Why predictive analytics?

With the unprecedented availability of data, it has never been easier to reap the benefits of establishing a claims process informed by predictive analytics. Incorporating a robust model can yield a valid statistical basis for decision making, which strongly augments the decision-making ability of claims staff. When deciding if this is the right time for employing analytics, it is important to consider not only potential return on investment, but also the very real cost of inaction. Claims severity continues to rise, driven by medical and indemnity costs, for which the National Council on Compensation Insurance reports a cumulative increase of 226.7% and 135.2%over the past 20 years, respectively.

Adjuster experience, while invaluable, is not infallible. A good predictive model informs, but does not replace claims experience in decision making. It helps adjusters and management focus attention where it is most needed, minimizing the risk of exploding claims and reigning in unnecessary expenses with potential for directly improving an insurer’s financial results.

The human element will continue to impact the claims process — just as it should, given the insurer’s goal of helping claimants deal with unexpected losses that may represent life-changing catastrophes. But the development of finely tuned analytical models is yielding insights into those claims to bring a sharper understanding of their potential severity and helping inform those involved in the claims process from assignment to settlement. Although not yet a crystal ball, better analytics are opening new windows and providing a clearer view into the world of claims.

Adam Wesson is the director of claims solutions for ISO Claims Partners, a Verisk Analytics (Nasdaq:VRSK) business.