Automated Fraud Detection System Using Real-Time Video Analysis

The patent replaces the mechanical human analysis system with an automated computer-based vision system that uses machine learning algorithms to detect fraudulent behavior. The system captures images from surveillance cameras, processes them through trained neural networks, and automatically identifies suspicious activities, eliminating the need for manual review by security personnel while significantly improving detection speed and efficiency.

The patent applies preliminary action by pre-training the machine learning model with extensive video data containing various fraudulent and non-fraudulent behaviors before deployment. This offline training phase prepares the system in advance, allowing it to perform complex automated analysis during operation without requiring real-time computational complexity. The pre-trained model can be updated periodically, separating the complexity of learning from the simplicity of inference.

The patent segments the video analysis process into distinct stages: frame capture from surveillance cameras, pre-processing to extract relevant features, classification through the trained machine learning model, and post-processing to generate alerts. This segmentation allows computational resources to be distributed efficiently across different processing stages, enabling real-time analysis by processing only essential features rather than analyzing entire video streams continuously.