Socure. has filed a patent for a system that uses historical transaction data to create feature sets for decision-making solutions. The system includes modules for data ingestion, feature extraction, and fraud score generation based on duplicated features. Machine learning models are used to update and adjust the feature sets. GlobalData’s report on Socure 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 Socure, Content access control was a key innovation area identified from patents. Socure's grant share as of January 2024 was 52%. Grant share is based on the ratio of number of grants to total number of patents.
Fraud detection system using historical transaction data
The patent application (Publication Number: US20230376962A1) describes a system and method for generating fraud scores using computer program modules and data extraction techniques. The system includes processors, data ingestion modules, feature extraction modules, and databases to process incoming transactions and extract features for fraud detection. These extracted features are used to generate fraud scores in real-time by comparing them with current transaction data. The system also incorporates machine learning models that update and adjust the extracted features based on feedback about fraudulent transactions, enhancing the accuracy of fraud detection.
The method outlined in the patent application involves receiving previous transaction data, extracting features from this data, and applying them to incoming transactions to detect fraud. The extracted features can be single key or two-key features based on data elements like email addresses or IP addresses. These features are generated using predetermined operators and time frames to identify patterns indicative of fraudulent activities. Additionally, the method includes the application of an additional fraud model and merging its results with the fraud score to create a composite fraud score. The system and method leverage machine learning models to continuously improve fraud detection capabilities by updating the extracted features based on feedback from identified fraudulent transactions, ensuring a dynamic and effective fraud detection system.
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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.
