Generating Explanations for Algorithmic Decisions of Usage-Based Insurances using Natural Language Generation

Abstract

Usage-based insurances are becoming more and more popular, especially for cars. These so called telematics insurances use different sensors installed in a car to track the individual driving style of the driver. Instead of calculating insurance premiums based on statistical risk groups, insurance companies can use these data to create individual risk profiles and calculate insurance premiums accordingly. We present an approach to use Natural Language Generation (NLG) in order to explain customers which aspects of their behaviour influenced the assessment of the algorithm. In this way, we can not only increase the acceptance of customers regarding such systems, but also positively influence their future behaviour.