CrashSnap: AI-Powered Vehicle Damage Detection and Intelligent Insurance Claim Processing System.

Road accidents are increasing rapidly and generate a large number of vehicle insurance claims every year. Conventional claim processing depends on manual inspection, paperwork verification, and delayed communication, resulting in slow settlement and inconsistent decisions. This paper presents CrashSnap, an Artificial Intelligence based vehicle damage assessment and insurance claim automation platform. The proposed system uses the YOLOv8 deep learning model to detect vehicle damages, classify damage categories, localize affected regions, estimate severity, and accelerate insurance decisions. CrashSnap integrates three major modules: User Module, Insurance Company Module, and Administrative Module. Users can upload damaged vehicle images and receive instant AI-generated reports. Insurance companies can review claims, approve or reject requests, and generate settlement reports. Experimental implementation shows that the system reduces manual effort, increases transparency, improves customer experience, and achieves approximately 82% to 83% damage detection accuracy. The platform provides a scalable solution for future smart insurance ecosystems.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-04-27
DOI
https://doi.org/10.5281/zenodo.19806941
Primary Topic
IoT and GPS-based Vehicle Safety Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

CrashSnap: AI-Powered Vehicle Damage Detection and Intelligent Insurance Claim Processing System.

Rinu BINU, Sheena K M, Shamas P M
Zenodo (CERN European Organization for Nuclear Research)
IoT and GPS-based Vehicle Safety Systems
article

CrashSnap: AI-Powered Vehicle Damage Detection and Intelligent Insurance Claim Processing System.

Rinu BINU, Sheena K M, Shamas P M
article en

Abstract

Road accidents are increasing rapidly and generate a large number of vehicle insurance claims every year. Conventional claim processing depends on manual inspection, paperwork verification, and delayed communication, resulting in slow settlement and inconsistent decisions. This paper presents CrashSnap, an Artificial Intelligence based vehicle damage assessment and insurance claim automation platform. The proposed system uses the YOLOv8 deep learning model to detect vehicle damages, classify damage categories, localize affected regions, estimate severity, and accelerate insurance decisions. CrashSnap integrates three major modules: User Module, Insurance Company Module, and Administrative Module. Users can upload damaged vehicle images and receive instant AI-generated reports. Insurance companies can review claims, approve or reject requests, and generate settlement reports. Experimental implementation shows that the system reduces manual effort, increases transparency, improves customer experience, and achieves approximately 82% to 83% damage detection accuracy. The platform provides a scalable solution for future smart insurance ecosystems.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 7%
IoT and GPS-based Vehicle Safety Systems
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