Broadridge Financial Solutions. has been granted a patent for a machine-learning method that predicts user-specific engagement scores based on activity performance and personal data. The method utilizes neural networks to analyze data from multiple digital resources, ultimately guiding users with tailored activity-related action instructions. GlobalData’s report on Broadridge Financial Solutions 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 Broadridge Financial Solutions, was a key innovation area identified from patents. Broadridge Financial Solutions's grant share as of June 2024 was 36%. Grant share is based on the ratio of number of grants to total number of patents.
Machine-learning method for predicting user engagement scores
The granted patent US12014254B2 outlines a method and system for predicting user-specific engagement in activities, particularly in the context of financial services such as loans and lines of credit. The process begins with a processor obtaining user-specific, activity-specific data from various digital resources. This data includes performance metrics related to user activities, object data that enables these activities, and personal information about the user. A neural network machine learning model is then trained using this data to predict engagement scores, which indicate the likelihood of a user utilizing a line of credit or potentially churning from it. The model's training involves generating multiple datasets, inputting them into the neural network, and applying these datasets to produce actionable outputs.
The patent further details that the engagement predicting scores can be utilized to instruct computing devices to present tailored activity-related action instructions to users. This includes displaying prediction utilization scores that reflect the likelihood of a user engaging with financial products. The system can also rank users based on their predicted scores and provide recommendations aimed at encouraging them to apply for loans or lines of credit. The methodology emphasizes the use of time-series and feature data pipelines to enhance the accuracy of predictions, thereby enabling more personalized financial services for users based on their specific behaviors and characteristics.
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