The automotive damage appraisal process is one of the areas in property and casualty insurance that can benefit from applying deep learning technology and computer vision. It is commercially beneficial to introduce a fast and efficient claim process that can shorten the entire process. Technologies adopted include advanced neural network algorithm and Mask R-CNN to solve tasks such as image classification, object detection, and segmentation in combination with statistical analysis and model construction of the appraisal metadata to approximate final claim cost. With a database of over 3 million records as the data source, a workflow is constructed via a combination of image modeling and Estimate Management Standard (EMS) modeling which can automate the appraisal process with a one-stop approach. It will increase both the efficiency and the accuracy of the process by reducing the labor cost by 50%, as well as reducing the appraisal timeline from an industry standard five days to just hours on average.
Poon, Fred; Zhang, Yang; Roach, Jonathon; Josephs, David; and Santerre, John
"Modeling and Application of Neural Networks for Automotive Damage Appraisals,"
SMU Data Science Review: Vol. 5:
1, Article 3.
Available at: https://scholar.smu.edu/datasciencereview/vol5/iss1/3
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