Invisible Analytics

The concept of invisible analytics is about understanding the claim management process and providing direction for claim managers, in a simple and concise way.

For example, rather than just use the output from a predictive model to score a low-risk claim, we should consider implementing a process that segments claims into a ‘pay and close’ workflow queue.

Another example might be, instead of highlighting that a claim has a 50/50 chance of RTW in 6 months, we should suggest that a psychosocial questionnaire is sent out to get more information.

So the objective is to provide some extra information to a claim manager that is actionable, which requires no interpretation.

This means that data analysts, need to do more that just churn out numbers. Also this is why the more adept claim analytics teams are recruiting experienced claim managers to help them get closer to the claims process. This will help to break down the barriers of understanding.

The Claim Lab believes this is just as important as having smart data scientists, advanced analysis techniques, and great data! We need to spend as much time and effort on how to introduce the information into the claim management process, with minimal disruption and minimal overhead in understanding.

We are regularly asked to advice clients in how to improve the effectiveness of data analytics in claim operations, and we would be pleased to discuss this with you further.

Email us at  info@claimlab.org