Elbhrawy, A., Belal, M., Hassanein, M. (2024). CES: Cost Estimation System for Enhancing the Processing of Car Insurance Claims. Journal of Computing and Communication, 3(1), 55-69. doi: 10.21608/jocc.2024.339922
Ahmed Shawky Elbhrawy; Mohamed AbdelFattah Belal; mohamed Sameh Hassanein. "CES: Cost Estimation System for Enhancing the Processing of Car Insurance Claims". Journal of Computing and Communication, 3, 1, 2024, 55-69. doi: 10.21608/jocc.2024.339922
Elbhrawy, A., Belal, M., Hassanein, M. (2024). 'CES: Cost Estimation System for Enhancing the Processing of Car Insurance Claims', Journal of Computing and Communication, 3(1), pp. 55-69. doi: 10.21608/jocc.2024.339922
Elbhrawy, A., Belal, M., Hassanein, M. CES: Cost Estimation System for Enhancing the Processing of Car Insurance Claims. Journal of Computing and Communication, 2024; 3(1): 55-69. doi: 10.21608/jocc.2024.339922
CES: Cost Estimation System for Enhancing the Processing of Car Insurance Claims
1Business Information System Department, Faculty of Commerce and Business Administration, Helwan University, Cairo, Egypt
2Professor, Computer Science Department Faculty of Computers and Artificial Intelligence Helwan University Cairo, Egypt
3Integrated Thebes Institutes for Computing & Management Science Cairo, Egypt
Abstract
Damage assessment is crucial in determining insurance reimbursements in the car insurance industry. However, manual inspection is time-consuming and financially costly. Artificial Intelligence (AI) offers a promising automatic damage assessment solution; we propose a Cost Estimation System (CES) for car damage volume level recognition and cost estimation. CES extracts damage estimates from mobile imagery data and combines them with structured customer data to generate accurate cost estimates for insurance purposes. This paper adopts the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology to develop a robust and systematic model. Leveraging AI technology such as the (You Only Look Once) YOLO model and Transformers in image classification while expediting the claims process and mitigating fraud risk. Evaluating CES performance indicates the ability to accurately identify and locate damaged regions in car images, with an average precision of 78.50%, an average recall of 70.24%, and a mean Average Precision (mAP) of 0.66. Resulting in satisfactory performance from the curated dataset of 2508 car photos, which is classified by car body parts, and their inspected damage parts for enhancing cost estimation, productivity, accuracy, and time savings.
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