Stripe had 22 patents in regtech during Q2 2024. Stripe Inc filed patents related to detecting fraudulent merchant activities using machine learning models, analyzing data from payment card scans to verify transactions without rescanning, fraud detection during transactions using identity graphs, and generating machine learning models for classifying network transactions based on selected features. These patents aim to enhance security and efficiency in online 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 had no grants in regtech as a theme in Q2 2024.
Recent Patents
Application: Systems and methods for merchant level fraud detection based in part on event timing (Patent ID: US20240211951A1)
The patent filed by Stripe Inc. describes a method and apparatus for detecting fraudulent merchant activities at a commerce platform system. The method involves accessing merchant system data, encoding it into structured and unstructured data input signals, inputting these signals into machine learning models to generate fraud scores, combining these scores to generate a single fraud score, and initiating remedial actions if the fraud score meets a threshold. The system utilizes different machine learning models, such as XGBoost and Neural Network models, to detect fraud from structured and unstructured data input signals, including tabular numeric data and time-based data associated with sequential operations.
Furthermore, the method includes accessing additional sets of merchant system data, such as unstructured text data, encoding it into input signals, inputting it into a third machine learning model, and combining the scores from all models to generate a single fraud score. Remedial actions initiated based on the fraud score can include declining transactions, deactivating accounts, sending fraud detection messages, and storing annotated transaction data for training purposes. The system allows for real-time fraud detection, periodic retraining of machine learning models based on annotated data, and the use of a server computer system to perform the described operations efficiently.
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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.
