Image recognition in auto damage claim process
Nguyen, Bao (2020)
2020
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2020112524383
https://urn.fi/URN:NBN:fi:amk-2020112524383
Tiivistelmä
Through interest in applicable technology in business and desire to close the gap between business and technical view, the author attempted to take image recognition and insurance as a combination. The primary goal of this thesis is to examine how the image recognition revolutionize the damage claim process in auto insurance.
Due to the nature of introductory stage of the technology, there are a few public comprehensive documents and service providers. Theoretical studies are conducted through the reliable market research agencies, and technical articles regarding the topic. The data analysis is based on conducted interviews with auto inspection service providers with semi-structured questionnaire and also published speeches and article from the 4 target companies.
As the result, the image recognition is the new competitive edge to insurance carrier in auto insurance. The concept of extracting and making sense of data from images provided has brought main impacts: timely and accurate information, streamlined process, high processing capacity. As a result, the technology enables shorter processing time, higher accuracy in detection and estimation, thus less human intervention, and image fraud detection.
Due to the nature of introductory stage of the technology, there are a few public comprehensive documents and service providers. Theoretical studies are conducted through the reliable market research agencies, and technical articles regarding the topic. The data analysis is based on conducted interviews with auto inspection service providers with semi-structured questionnaire and also published speeches and article from the 4 target companies.
As the result, the image recognition is the new competitive edge to insurance carrier in auto insurance. The concept of extracting and making sense of data from images provided has brought main impacts: timely and accurate information, streamlined process, high processing capacity. As a result, the technology enables shorter processing time, higher accuracy in detection and estimation, thus less human intervention, and image fraud detection.