There is a lot of buzz around the use of Big Data in Insurance, and particularly, the workers’ compensation claims industry. Although there is no doubt that this is a complicated field, let’s take a moment to cover some of the basics of what predictive modeling is, what it isn’t, and what it means for workers’ comp claims.
What Predictive Modeling Is
Whether you call it machine learning , analytics, or data modeling, you’re referring to the process of using historical data to make future predictions. A significant part of building a predictive model includes data mining. The art and science of data mining, practiced by data scientists, involves finding patterns and stories in data that explain real world phenomenon. Sometimes the data used to support a story or pattern is obtained from an outside source and it is the relationship between that data and your own that really powers your predictive model.
What Predictive Modeling Isn’t
Predictive modeling isn’t a crystal ball: past performance doesn’t always guarantee future outcomes. Although, if you’ve got a good enough model using the right data, it usually does. Predictive modeling (especially when referred to as artificial intelligence) is also not an adequate replacement for human intuition and know-how.
Applying Predictive Modeling to Workers’ Comp Claims
By implementing predictive modeling in your operation through either your claims or risk management system you can begin to effectively:
- Inform adjusters about possible claim issues or outcomes
- Allow for management to better triage claims and manage workloads
- Focus C-Suite and client attention on the claims that currently do, and likely will, matter
Assuming these are the goals you’re looking to achieve, your next step is to look at your data and find a platform that can execute your model.