How to Use Data to Influence Claim Decisions
Check out the key insights from our exclusive leader to leader webinar with Sentry Insurance experts. Watch the webinar, to discover how data is influencing claim decisions and grab valuable takeaways for your team.
What do you need to do to make analytics matter?
The key to making analytics matter is to make it actionable. Make sure the subject matter experts are involved in the beginning and throughout to make sure the analytics are accomplishing a business purpose. Especially when considering large language models (Artificial Intelligence) be sure to think through use cases as well as ethical and legal parameters.
What are strategies for making sure your data is actionable?
- Ensure you have the right customized tools readily available such as: loss reports, vendor partner reports and partnership reviews
- Customize the tools based on your organizational structure to make it most meaningful for your business
- Ask your vendor partners for what you want not what they want
- Discontinue reports that you don’t look at or need
- Make sure you can access your data independently. Such as self-service reporting tools and interactive dashboards
- Make time to use the tools
What are three things you should be getting out of your visual analytics tool?
- Claims frequency and severity
- Outlier claims and what to do about them
- Ways to Control costs
Once you deploy a model what should be included in a post production checklist?
- Explain to the end user why you built the model and how its going to benefit them
- Reinforce training on new workflows and processes while the model is running in production
- Analyze results early to validate its being used as intended and as effective as anticipated
What are considerations to evaluate when determining whether to buy vs. build a model?
When determining whether to buy or build a model consider the volume, variety and velocity of data available to you. You might also consider the technology that will be needed to deploy, integrate and monitor the success of the model. Do you have the technical and subject matter expertise internally? Finally, is the problem you are trying to solve unique to your business or something other industry experts have deployed.