报告详情
AI-Driven Automated Insurance Claim Damage Assessment System
编号：34
访问权限：仅限参会人
更新：2026-07-22 16:09:16 浏览：0次
Online
报告开始：2026年07月30日 17:35（Asia/Kolkata）
报告时间：15min
所在会场：[S6] Artificial Intelligence Use Cases [S6-2] Artificial Intelligence Use Cases
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摘要
Manual inspection is still the primary method used to assess vehicle insurance claims; therefore, there are longer timeframes and higher operational costs associated with manual assessments. There are inconsistencies with evaluations and vulnerability to fraud within the existing assessment method. This study presents a framework called VisionClaimNet, which provides an AI-based automated vehicle damage assessment procedure by employing deep learning and computer vision for the intelligent processing of vehicle insurance claims. The proposed VisionClaimNet model is structured as a multi-stage architecture composed of the following four components: image preprocessing, YOLOv8 for damage identification/detection, CNN for damage severity classification, and regression analysis for estimating repair costs. Through the use of an interactive web interface that uploads images of vehicles, VisionClaimNet processes these images and will identify/mark/pinpoint areas of damage on the vehicle, classify the damage based on severity, estimate the cost to repair, and generate a preliminary claims report. In order to enhance the robustness and generalizability of the VisionClaimNet model, various image normalization and augmentation techniques were used. In addition, in order to detect suspicious claims, an anomaly-aware analysis technique has been developed to identify unusual patterns based on the potential for fraud associated with each claim.Experimental results indicated that VisionClaimNet produced high detection rates, low latency of inference, and consistent performance with regards to assessing damage across multiple categories of damage. VisionClaimNet automates much of the claims process, thus reducing manual effort, accelerating settlement timeframes, and providing efficiencies that will positively impact current operations of modern insurance companies.
关键词
Damage Detection and Assessment, Automated Insurance Claims, Deep Learning; YOLOv8, Computer Vision, CNN, Damage Severity Classification, Cost Estimation, Fraud Detection, and Intelligent Insurance Analytics.
报告人
Shaik Imran
Student Santhiram Engineering College ；Department of Data Science N.Venkatesh Naik
Professor Santhiram Engineering College稿件作者
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重要日期
-
会议日期
07月30日
2026
至08月01日
2026
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06月30日 2026
初稿截稿日期
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07月30日 2026
注册截止日期
主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
IEEE Madras Section
联系方式
- ICNCCom 2026
- ic******@usssociety.org
历届会议
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2026年07月30日 印度 Trichy
2026 International Conference on Networks Computers and Communications
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