New Fraudulent Behavior Detection System Launched to Enhance Security and Productivity

Signzy, a leading provider of innovative identity verification and fraud detection solutions, is proud to announce the granting of a new patent for its revolutionary method and system for identifying and preventing fraud. This new system performs automated analysis of human behavior to determine fraudulent behavior.

Financial institutions can now benefit from an automated system for fraud detection that overcomes the disadvantages of conventional security measures. Traditional measures, such as surveillance cameras and security guards, can be time-consuming and inefficient, relying heavily on manual analysis. Automated systems, such as this new fraud detection system, provide a more effective way to enhance security and productivity while reducing monetary losses.

The computer-implemented method collects technical data and video data, processes the live video stream data, analyzes the data using machine learning and deep learning algorithms, trains the system in real-time and identifies the likelihood of future fraudulent behavior. The system continuously receives and analyzes live video stream data, which includes voice communication, facial expressions and body movements and maps moving patterns of humans to those of fraudsters.

The system is designed to help identify and reduce fraud in facilities such as financial institutions, depository institutions, non-depository institutions, malls, supermarkets and departmental stores. The system can be trained on data sources including online databases, offline databases, data warehouses, websites, web pages and live video feeds. These video feeds can be collected from multiple sources, such as infrared, surveillance and CCTV cameras.

The fraudulent behavior detection system is designed to scan live video stream data, divide it into frames of images and properly analyze these frames to predict the likelihood of fraudulent behavior. It maps moving patterns of humans from the live video data to those of fraudsters to identify potential fraudsters.

The system is efficient and effective, using machine learning and deep learning algorithms to train the fraudulent behavior detection system in real-time. The technical data collected includes employee data, real-time transaction data, design engineer input, movement calculation of humans and architecture of the facility.

This new fraud detection system provides an automated solution to enhance security and productivity for financial institutions and retail companies. It is a reliable, cost-effective and efficient system that can be used in a range of facilities to reduce monetary losses and improve security.