Behavioural Policy Pricing
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
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.
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.