Automatic damage detection after natural disasters

Settlement within 10 days instead of 9 months due to automatic damage detection

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Duration of claims settlement reduced from 9 months to 10 days

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Reduction of appraiser costs by 75%

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Cost savings in the millions with simultaneous increase in customer satisfaction

Challenge

After natural disasters, an insurance company wants to quickly and cost-effectively assess the damage to its insured buildings. This requires an infrastructure that can process a large amount of image material in a timely manner. In addition, a model must be developed that allows the prediction for different building types and regions.

Solution

Various machine learning models are developed, evaluated and made available. These are the basis for automatic damage detection. A pipeline is built in the Amazon AWS Cloud that seamlessly integrates both the model training and the productive use of the results. Due to the complete integration of all processes in the AWS infrastructure, the product is scalable and can be automatically adapted to the current demand in a cost-efficient manner during idle and load times.

Result

The models can be trained and adapted in a scalable pipeline. Via an API, the results can be used for a wide range of applications for automatic damage detection. Damage classification makes it easier to estimate payout amounts and initiate payout processes automatically.

Are you interested in your own use cases?

Challenge

An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.

Solution

There will be a interactive and Flexible application, including of different maps with two different views implemented.

Result

Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.

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