Toronto-Dominion Bank had 35 patents in regtech during Q4 2023. The Toronto-Dominion Bank filed patents in Q4 2023 related to optimized anomaly prediction using machine learning, automated teller machine session data storage, and processing credit application statuses. These patents involve methods, systems, and computing platforms for efficient anomaly detection, checksum generation and storage, as well as automated status updates for credit applications without the need for manual contact. GlobalData’s report on Toronto-Dominion Bank gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Toronto-Dominion Bank grant share with regtech as a theme is 48% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Systems and methods of optimizing machine learning models for automated anomaly detection (Patent ID: US20230419402A1)
The patent filed by The Toronto-Dominion Bank describes a method, system, and technique for optimized anomaly prediction using machine learning. The system includes an unsupervised clustering module to cluster account data sets, an anomaly detection module to label anomalies in the data sets, and a tree classification model to detect anomalies based on the labeled data. The system generates rules executable for anomaly spotting by defining combinations of feature characteristics resulting in outlier data, which can be applied to new data for anomaly detection.
The method involves clustering training data into clusters based on input features, determining distribution patterns for each cluster, identifying outlier data points within each cluster, and labeling them as anomalies. A tree classification model is then trained on the labeled data to detect anomalies, and rules are extracted from the model for anomaly spotting. The method also includes applying the rules to new customer data to classify them as outliers or normal based on the defined feature characteristics. The system and method aim to improve anomaly detection in account data using machine learning models and unsupervised clustering techniques.
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