Nice had eight patents in digitalization during Q1 2024. Nice Ltd filed patents for computerized methods related to predicting fraudulent financial-account access, authorizing time-off based on agent skills, and generating high-quality synthetic fraud data from financial transaction tabular data. These methods involve machine learning models, skill-based time-off calculations, deep learning for data generation, and fraud detection model training. The goal is to improve fraud prediction, agent availability, and fraud data quality in financial transactions. GlobalData’s report on Nice gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Nice grant share with digitalization as a theme is 62% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Computerized-method and system for predicting a probability of fraudulent financial-account access (Patent ID: US20240070673A1)
The patent filed by Nice Ltd. describes a computerized method for predicting the probability of fraudulent financial account access using a Machine Learning (ML) sequence model. The method involves building the ML model by retrieving and labeling chronical sequences of non-financial activities associated with financial accounts, training the model, and implementing a forward propagation routine to generate fraud probability scores. These scores are then used by a Fraud Management System to predict fraudulent access in real-time. The system also includes features such as profiling customers, early fraud detection, and adjusting the ML model for improved accuracy.
The computerized system outlined in the patent includes memory for storing customer data and a persistent storage, along with processors configured to build the ML sequence model, implement the forward propagation routine, and integrate the executable with a Fraud Management System. By following the steps outlined in the patent claims, the system can effectively predict the likelihood of fraudulent financial account access by analyzing the chronical sequences of non-financial activities associated with each account. This method allows for real-time monitoring and decision-making to prevent fraudulent activities and protect financial accounts.
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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.
