The concept of ‘invisible analytics’ is about understanding the claim management process and providing direction in a simple and concise way, so it’s transparent to the claim manager.
Rather than use the predictive model to provide a claim score, that a claim manager is supposed to interpret, we should drive the action.
For example, if the model scores a claim as low-risk, we should be implementing a process that passes the claim into a ‘pay and close’ workflow queue, instead of providing a score of an ‘8’ or a ‘7’, “What’s the difference?” would be the response from the claim manager!
So the objective is to use the output from the models seamlessly in the claim process, after all, this is what we mean by a “data-driven claims management”.
Attention data scientists, we need to do more that just churn out numbers, we need to provide more direction. We have seen a trend in the more adept claim analytics teams, where they are recruiting experienced claim managers to help them get closer to the claims process. Not only will this provide extra expertise for the analytics team, but it will also help break down the ‘barriers of understanding’, which we spoke of in November’s newsletter.
So as well as having smart data scientists, advanced analysis techniques, and great data…. We need to spend just as much time and effort on how to introduce the information into the claim management process, with minimal disruption to that process.
We are regularly asked to advise clients in how to improve the effectiveness of data analytics in their claims operation, and we would be pleased to discuss this with you further.
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