Six inhibitors to analytics adoption in claims

By Mark Gorman, CEO and founder of Gorman Grouyp and Stephen Swenson, Regional Insurance Manager, SAS

Insurance companies widely use predictive analytics in pricing, marketing and underwriting. Now the technology is making its way into claims, an area where it promises big changes – and advancements – in the industry. However, getting your claims department to embrace the technology may require overcoming some ‘inhibitors’ or obstacles to the change.

We found that there are six top inhibitors to adoption, and here are our suggestions for how to address them:

Inhibitor #1: Predictive analytics confirms what we already know.

In some instances, predictive analytics does confirm what adjusters already know – but it goes beyond that. Predictive analysis sorts through vast amounts of claims data to offer new insights, faster. To convince your internal team predictive analytics guarantees results, clarify your business objectives. Next, focus on gleaning clear and well-defined metrics to support the initiative. You also want to demonstrate you have the proper predictive modeling expertise in place to move ahead.

Inhibitor #2: Our customers will think we’re using it to reduce payouts.

Some claims managers and adjusters worry that their customers will think they’re using some type of “black box” approach to reducing payouts, and their customers will pursue litigation. Stress that predictive modeling is for decision support and not a decision automation tool. Make sure your model development approach is fully transparent and well documented, so you can manage any perceived risk. And, as with any technology adoption, be sure to balance risk with the business benefits.."

Make sure the people you choose to manage your predictive analytics implementation understand your organization, the claims handling process and how the technology helps that process.

Inhibitor #3: We need to address our data warehousing issues first.

If you’re planning to adopt predictive analysis in claims, a big part of the project will be evaluating and standardizing how you capture and store your data. Predictive analysis is the tool that brings value to the data. Instead of positioning warehousing and analysis as two projects, bring them together. Align your predictive analytic data requirements with your IT initiatives, so the two work together in the best possible way.

Inhibitor #4: But it won’t help with both indemnity and LAE.

Predictive analysis has an impact on all aspects of claims handling. Steer your focus toward a more holistic view of claims processing – one that includes client retention, managing product changes, retention of policy holder surplus and more. When you pay a claim without predictive analysis you may miss the connections and relationships.

Inhibitor #5: Experienced adjusters make better, more intuitive decisions.

Skepticism is expected. Will analysis outperform the skills of an experienced adjuster? The answer depends on the data. The validity of any predictive model is only as good as the quality and quantity of data available. If you want to build a team of believers, start with data that supports an effective model building effort. Also, remind your adjusters they don’t have to base their decisions solely on the analysis. Instead, consider model scoring as additional information to aid decision making.

Inhibitor #6: Our current processes work, so why should we change them?

Getting your team to embrace any new technology requires an effective change management strategy. Make sure the people you choose to manage your predictive analytics implementation understand your organization, the claims handling process and how the technology helps that process. Choose key individuals to manage the development and deployment aspects of model claims scoring. And align people, processes and technology to create a comprehensive project-based approach.

While our findings do not reflect the current situation at any specific organization, they were consistently and repeatedly identified by those we interviewed. The final outcome is clear: Predictive analytics use by claims departments lags its use in pricing, underwriting and marketing. Organizations that are looking to better address the needs, issues and concerns of their claims department and increase the use of data-driven decisions must focus on removing the inhibitors to expanded use of predictive analytics.


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For more on our views and suggestions, read Building believers: How to expand the use of predictive analytics in claims.

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