Stripe had four patents in regtech during Q1 2024. Stripe Inc’s patents filed in Q1 2024 focus on machine learning-based fraud detection for transactions. One patent describes using separate machine learning models to determine fraud likelihood and merchant thresholds, while another patent introduces a single model for detecting fraud across multiple payment types using payment scoring events. These innovations aim to enhance security and efficiency in processing transactions. GlobalData’s report on Stripe gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Stripe grant share with regtech as a theme is 25% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Machine learning for fraud tolerance (Patent ID: US20240095742A1)
The patent filed by Stripe Inc. describes a method and system for fraud detection in transactions using machine learning models. The first model predicts the likelihood of fraud in a transaction, while the second model determines a suitable threshold for a merchant based on historical transaction data. This threshold can be automatically applied or recommended to the merchant for acceptance or rejection. The second model evaluates potential transactions based on features extracted from historical data and the fraud tolerance threshold assigned to the merchant, allowing for the blocking of fraudulent transactions.
The system includes processors and memory storing instructions for accessing and processing the machine learning models to determine whether to allow or block potential transactions. The models can be retrained based on the results of their evaluations, including measures of disputed transactions. Additionally, the second model is designed to optimize revenue by assigning merchants to segments with common fraud tolerance levels. The non-transitory machine-readable medium contains instructions for accessing the machine learning models, transforming features, evaluating transactions, and retraining the models based on results, ultimately aiming to improve fraud detection and revenue optimization for merchants.
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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.
GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
