INSURANCE AMOUNT PREDICTION BASED ON ACCIDENTAL CAR DAMAGE LEVEL USING CNN

INSURANCE AMOUNT PREDICTION BASED ON ACCIDENTAL CAR DAMAGE LEVEL USING CNN

 Beecha Aditya1, Mohammad Moizuddin2, Mekala Naveen3, A. Amulya4

1Dept. of information technology, Mahatma Gandhi institute of technology

2Dept. of information technology, Mahatma Gandhi institute of technology

3Dept. of information technology, Mahatma Gandhi institute of technology

 4Dept. of information technology, Mahatma Gandhi institute of technology

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Abstract: There is a paradigm shift in the automotive industry with the advancement of technology, especially in the area of ​​insurance claims handling. In this study, we propose a new method for predicting insurance claims based on car damage levels, utilizing advanced artificial intelligence algorithms for image analysis, particularly employing Convolutional Neural Networks (CNN) for image processing and classification.

Our strategy is to provide car damage estimates through picture analysis, making insurance claims in the automobile sector simpler. In order to facilitate user interaction with Flask and increase the predictability of our models, we have also implemented enhancements to the user interface.

By combining image analysis with a user-friendly interface, our method offers a practical solution for insurance companies to expedite claim processing, reduce fraudulent activities, and ensure fair settlements for policyholders. This research contributes to the ongoing transformation of insurance claim processing within the automotive industry, highlighting the potential of merging image analysis methodologies with user interfaces for enhanced efficiency and usability.

Keywords: Insurance claim prediction, Convolutional Neural Networks (CNNs), Image analysis, User interface, Flask, Automotive industry.