Citizens Financial Group. has filed a patent for a system and method that uses historical account activity to forecast future inflow and outflow. The system includes processors, memory, and a prediction model that optimizes accuracy by adjusting weight parameters. It also generates future balance forecasts based on current activity. GlobalData’s report on Citizens Financial Group 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 Citizens Financial Group, Virtual banking assistant was a key innovation area identified from patents. Citizens Financial Group's grant share as of January 2024 was 89%. Grant share is based on the ratio of number of grants to total number of patents.
Predicting future account activity based on historical data
The patent application (Publication Number: US20240037370A1) describes a system that utilizes historical account activity data to forecast future inflow and outflow activity for multiple accounts. The system includes processors and memory storing programming instructions that retrieve historical data, construct a training data set, and generate a combined prediction model for forecasting future activities. The model is trained based on historical data and adjusted to optimize accuracy. It receives current activity data from third-party systems, generates future forecasts for target accounts, and determines future balance forecasts. The system also includes processes for retraining the model based on performance metrics and adjusting weight parameters to improve accuracy.
Furthermore, the system is designed to preprocess historical data by imputing missing values, filtering accounts with insufficient data, performing feature engineering, and scaling data values. It splits historical data into training, validation, and test sets for model evaluation and adjusts weight parameters based on validation data. The system also preprocesses current activity data and balance information for target accounts, models the multi-variable data set, and forecasts inflow, outflow, and balance for a multi-step time horizon. Additionally, it can store forecasts in tables for future access, generate a graphical user interface (GUI) for displaying forecasts, and transmit the GUI to user devices for visualization. The system aims to provide accurate and reliable forecasts for financial activities based on historical and current data analysis.
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