So far, I’ve discussed how predictive analytics can help insurers achieve the next generation of claims processing. The next level of claims analytics extends one step further, to machine intelligence. Machine intelligence executes tasks and learns on its own, and can take new actions based on what it has learned—as opposed to a predictive model, which executes the algorithm with which it’s been programmed.

Claims like these

Machine learning can identify “claims like these.” Similar to how Amazon can make recommendations based on your previous purchases (and purchases of customers similar to you) insurers can segment previous claims according to certain characteristics. For example, an insurer might identify claims that happened with a certain make and model of car, in a certain geographic area, with a certain extent of damage. Machine learning could scour these claims to identify next-best actions and recommend them to the adjuster.

“Claims like these” is the just the beginning. Machine intelligence has the ability to manage far more data than a person or single predictive model can reasonably handle, and consequently, can find patterns in data that people would miss or not be able to see.

In addition, machine intelligence thrives on having more data, which makes the Internet of Things an asset rather than a challenge. It can:

  • Refine “claims like these” recommendations based on an insurer’s growing body of claims data.
  • Identify claim types where a predictive model could be applied but isn’t currently used by the insurer.
  • Identify claim types that are good candidates for straight-through processing.
  • Identify segmentation factors for earlier and better identification of claims that explode.

To unlock the true value of predictive analytics and machine intelligence, insurers need a culture and environment for claims analytics. Join me next week as I examine how to achieve that.

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