Eurapco is an alliance of large independent European mutual insurance companies, consisting of eight partners operating in 16 countries across Europe. Over 43 million EU citizens trust the Eurapco Partners. Cost evaluation for car claims is a very demanding and expensive process.
Highly skilled experts process the images from car claims with visual analysis and prepare a summary report. It takes a long time to evaluate the claim and often the estimated costs are not accurate enough.
The goal was to build a system using Deep Learning that will estimate repair costs for car claims accurately. This will eliminate errors in manual evaluations by the experts who have no access to the actual data with repair costs or whose knowledge is outdated.
Normalization of each image pixel values to be in range [0, 1]
Stratified KFold basing on damage area
To solve semantic segmentation tasks the UNet based architecture was used with an InceptionResNetV2 encoder.
UNet model architecture
Takes into account the closeness of a prediction, strongly penalizes the classifier for being 100% sure, but does not capture the characteristics of the topology
Suitable for unbalanced data (harmonic mean of precision and recall)
Applying the Deep Learning approach helps to save time in the cost evaluation of car claims and making more accurate cost estimates.
GPU acceleration helps to scale this process by greatly increasing the performance of deep learning models.