As a statistical science, predictive modeling and artificial intelligence places a lot of importance on what events are being predicted and the data used in making those predictions. However, the overwhelming majority of individuals that are responsible for the actionable insight that comes from these models does not fall into the category of a statistician, mathematician, data scientist or actuary. Claims adjusters, managers, clients and C-Suite individuals are typically the consumers of the output of claims predictive models. Professions, regardless of the importance of their skills, are not ones typically versed in data analysis. This means that data visualization (one way of transforming data into information) is incredibly important in closing the gap between predictions and those that must act on them.

Although your claims operation may differ, the most straightforward actionable insights you can gain from predictive models have to do with prioritization of work and distribution of workload. Take a look at this interactive adjuster workload dashboard for an example of how this kind of visualization can work.

The majority of the data that is woven into a visualization isn’t predictive in nature. However, as is the case with this example, knowing whether claims are High or Low risk makes a big difference with respect to the story you draw from a visualization on standard claim data.

Imagine how you might visualize your workload data once you start predicting claim risks.