Deep Learning Approach For Vehicle Damage Detection And Fraud Prevention In Insurance Claims

Abstract

The Increasing Number Of Vehicle Insurance Claims Has Led To Challenges In Verifying Damages And Detecting Fraudulent Activities. Traditional Claim Verification Methods Are Time-consuming And Prone To Human Error. This Paper Proposes A Deep Learning-based System For Automated Vehicle Damage Detection And Fraud Prevention. The System Utilizes Convolutional Neural Networks (CNN) And Object Detection Models Such As YOLO To Identify And Classify Vehicle Damages From Images. Additionally, Machine Learning Techniques Are Used To Analyze Claim Patterns And Detect Fraudulent Behavior. The Integration Of Artificial Intelligence Improves Accuracy And Reduces Manual Effort. The Proposed System Enhances Transparency And Speeds Up Claim Processing. It Also Minimizes Financial Losses Caused By Fraudulent Claims. Experimental Results Show Improved Performance Compared To Traditional Methods. Overall, The System Provides An Efficient And Reliable Solution For Modern Insurance Industries.

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