Abstract:
"The inefficiencies of manual automobile damage evaluations have long plagued the insurance sector, resulting in disagreements, delays, and erroneous cost estimates. In addition to straining the bonds between policyholders and insurers, these antiquated practices raise costs and insurance premiums for everyone. Acknowledging these difficulties, there has been an increasing demand for creative ways to improve accuracy and expedite the procedure.
Our strategy to address these issues concentrated on utilizing cutting-edge technology, specifically Convolutional Neural Networks (CNN), to transform the assessment of vehicle damage. After extensive investigation and testing, we created a CNN architecture designed especially for the identification of vehicle damage. Our goal was to improve the network's capacity to precisely identify and evaluate different kinds of damage in real time by fine-tuning its layers and parameters. Furthermore, we used deep learning methodologies to facilitate the system's potential to adjust and acquire knowledge from fresh data, guaranteeing ongoing enhancement and expandability.
We tested and assessed our solution thoroughly using a range of data science criteria in order to determine its effectiveness. With an accuracy rate of 88.64%, the CNN model showed remarkable performance metrics, indicating that it could accurately classify almost 89% of the data. High precision and recall rates for various damage classes were found through additional study, demonstrating the model's resilience and dependability. These test findings highlight how our method has the potential to completely change the insurance industry's approach to evaluating vehicle damage and open the door to more effective, precise, and economical procedures."
