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Case Studies in Big Data Analysis: A Novel Computer Vision Application to Detect Insurance Fraud
Abstract
Computer vision technology can be used for instant car damage recognition by analyzing images of damaged vehicles to detect and identify the location and severity of any damages. Technology can accurately classify damage into categories such as small, medium, or high severity. This can help insurance companies and other relevant stakeholders quickly process claims, reduce fraudulent claims, and improve the overall claims process efficiency. The conventional car damage assessment process is time-consuming, labor-intensive, and prone to errors. Computer vision models offer a new solution to detect car insurance fraud by identifying the damage severity and streamlining the claims process. AI can automate the process by analyzing images of damaged cars and generating a breakdown of the damage. The authors propose a unique computer vision process that can help identify small, medium, and high severity of damages and validate investigators' recommendations to detect anomalies in real-time.
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