Innovations in the insurance industry are strongly driven by technological advancements. Due to the digitalization of information and thus the increase in data (Big Data), there is an unprecedented flood of raw data, which, however, can often only be made accessible and usable for business purposes with adequate methods. Recent developments in the field of cloud computing and machine learning (especially Deep Learning) make it possible to extract and classify complex patterns and thus extract information from raw sensory input data such as satellite images automatically. Such a machine learning pipline allows insurers to save costs and increase customer satisfaction.
The project goal was to develop a data service that provides highly specialized and structured information on insurance objects, thus enabling rapid processing of orders and claims settlements. As data sources, we relied on remote sensing, weather, and social media data, which we processed with the help of self-adaptive deep learning and machine learning algorithms.
Remote sensing, weather and social media data were combined to automatically detect changes in diverse objects such as buildings, infrastructure or agricultural land. These changes can be triggered by man-made disasters and natural catastrophes, or they can result from planned restructuring. This includes, for example, a spoiled grape harvest or the demolition or expansion of a residential building. Until now, those affected have had to approach the insurance company themselves in order to fill a claim and provide the information necessary to calculate the appropriate settlement. The new process saves time and money leading to higher customer satisfaction.
- Press Release Universität Konstanz
- Project Duration: 10/2019 – 09/2018
- Partner: Universität Konstanz