Abstract: Accurate prediction of insurance payouts for car damage is essential for fair and efficient claim settlements in the insurance industry. This project introduces an innovative approach that leverages Generative Adversarial Networks (GANs) and deep learning techniques to estimate insurance amounts based on the severity of car damage. The system employs a GAN framework, where the generator creates synthetic images of damaged cars with varying severity levels, and the discriminator enhances the model's ability to recognize intricate damage patterns. These synthetic images are used to augment the training dataset, improving the model's performance. Features extracted from the images, combined with structured data such as car make, model, and accident details, are used to predict the insurance payout. This AI-driven method enhances prediction accuracy, reduces reliance on large labeled datasets, and improves generalization to new and complex damage scenarios. Automating the assessment process increases efficiency, reduces fraud, and ensures faster and more consistent claim processing.
Keywords: Insurance Amount Prediction, Car Damage Assessment, Generative Adversarial Networks (GANs),Deep Learning, Damage Severity Classification.
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DOI:
10.17148/IJARCCE.2025.14485
