Bed Bath & Beyond has patented a system using machine learning to detect credit card fraud on e-commerce websites. The system analyzes user data and factors to prevent fraudulent purchase attempts, enhancing security during online transactions. GlobalData’s report on Bed Bath & Beyond gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Bed Bath & Beyond, was a key innovation area identified from patents. Bed Bath & Beyond's grant share as of February 2024 was 59%. Grant share is based on the ratio of number of grants to total number of patents.
Fraud detection system for e-commerce websites
A recently granted patent (Publication Number: US11928685B1) discloses a system and method for detecting and preventing distributed verification attacks on e-commerce websites. The system involves an e-commerce computer connected to a purchaser computer through the Internet, a server executing fraud-detection instructions, and a machine learning algorithm for training the fraud detection system. The fraud detection webservice compares user data against various factors to determine the validity of purchase attempts, including factors such as previous attempts, customer account ID, IP address, transaction amount, shipping address, and more. Additionally, a Checkout Action Aggregator is included to reference historical data for checkout requests.
The method outlined in the patent involves storing historical customer data in a database, comparing this data against relevant factors to determine fraudulent purchase attempts, and using machine learning algorithms to train the fraud filtering program. Factors considered for fraud detection include previous attempts, customer account ID, IP address, transaction amount, shipping address, browser user agent, and more. By recording and analyzing these factors, the system aims to prevent fraudulent attempts, particularly in the case of distributed verification attacks, by adjusting the weights of factors used for detection. This innovative approach to fraud prevention in e-commerce transactions showcases a comprehensive system that leverages historical data and machine learning to enhance security measures and protect against potential threats.
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