The Challenge.

  • Pricing models for car insurance are based on many factors related to customer demographics, location, and car information.​
  • However, there is little customization for driving patterns and behaviours.​
  • With the emergence of IoT, it is possible to get detailed driving data from consumers and use this to personalize car insurance packages.

The Solution.

  • Designed and built a Machine Learning model to predict a customer’s risk of incident based on driving patterns.
  • Used the results from this model to feed a pricing model personalized to consumers driving habits. ​
  • Built a segmentation model to group customers based on driving patterns and identify safer drivers.

The Results.

  • Improved customer experience by rewarding good driving behaviour.​
  • Segmentation of driving habits lead to profiling and targeting of potentially safer drivers, reducing future claims costs.​
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