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Claims Automation and Flexibility in an Emergency 

by | Oct 12, 2022 | Business Rules, Decision Automation, Decision Management, Decision Modeling, DecisionsFirst Modeler

When it comes to insurance claims, there’s always a difference between normal operations and a disaster, like the recent hurricane in Florida. When disaster strikes, there is a need for normal, automated claims handling be turned off and replaced with more flexible, human decision-makers. Many additional claims professionals are flown in, normal operations are suspended, and the focus is on rapid response to the emergency. 

Of course, it is completely appropriate to have an emergency response protocol – one that is enacted temporarily and overrides normal operating rules. The number of claims, the cost of repairs, the sense of urgency, etc. all reflect the unique situation.  

However, it is absolutely possible for your automated claims system to also respond to emergency scenarios.  

When we build claims handling solutions (and we build them a lot), we ensure the solution empowers the business to make rapid change, enables improved customer experiences and breaks down each decision to its fundamental elements so that real-time adaptations are easy.  

  • We give those who understand the business policies and regulatory environment a visual blueprint of the automated decision-making (a decision model).  
  • We make sure that this model is used to structure the data we capture about how decisions were made, enabling continuous improvement of those decisions.  
  • And we give these business users a low code and transparent way to change the behavior of their system – including the ability to simulate the impact of changes and guiderails to prevent changes from creating unexpected side effects. 

When there’s an emergency, you could just turn one of these systems off and handle everything manually. But you could also use these same tools to make certain kinds of claims from inside the disaster area easier to auto-approve. You could make temporary changes to thresholds and other logic, confirming the likely impact before you make them. You could replace machine learning models built with data collected during normal operations with ones specifically build to work in disasters – or even with heuristics based on collected experience from previous similar disasters. You could update and refine these changes every day as the situation evolved and changed, making sure your system is as responsive to your customer’s needs as your people are.  

Your system could be a valued partner in emergencies, just as it is during normal operations. But only if you build it right. 

Click here to learn more about our Claims Handling Solution.

Drop us a line if you’d like to make sure your claims system is ready for the next emergency. 

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