Abstract: System and methods disclosed herein are for generating labels for dynamically received textual data based on similarity with previously-labeled datasets. The system may receive first textual data. The system may determine a first timestamp at which the first textual data was received. The system may determine a first receipt range for the first textual data based on the first timestamp. The system may retrieve a plurality of datasets. The system may select a first dataset from the plurality of datasets. The system may retrieve second textual data from the first dataset. The system may determine a first similarity metric between the first textual data and the first dataset. The system may compare the first similarity metric to a threshold similarity metric. The system may determine to assign a label for the second textual data to the first textual data.

Abstract: System and methods for extracting data from and authenticating digital images of documents. The system may receive first digital image data. The system may determine a first image type of the first digital image data. The system may determine a first version identifier based on the first digital image data. The system may determine a first ruleset. The system may determine a first set of pixel coordinate ranges for authenticating digital images. The system may determine a first authentication status for the first digital image. The system may determine a second portion of the first digital image at a second set of pixel coordinate ranges. The system may compare a first portion to the second portion. The system may determine a second ruleset. The system may update the image ruleset database to include the second ruleset.

Abstract: Systems and methods for selecting labels based on dataset similarity for machine learning model training data are disclosed herein. The system may receive a label modification request for a training datum. The system may retrieve label records corresponding to the datum identifier. The system may compare a pre-existing label with the new label. Based on determining that the pre-existing label differs from the new label, the system may generate a first dataset and a second dataset. The system may calculate a first average similarity metric and a second average similarity metric. The system may compare the first average similarity metric with the second average similarity metric. The system may generate a recommendation for a modified label.

Abstract: Systems and methods for generating recommendations for unlabelled data for machine learning model training using natural language processing are disclosed herein. The system may receive first text data for a first unlabelled training datum. The system may retrieve textual datasets from a label record database. The system may determine a first dataset that corresponds to a first label record. The system may determine a first plurality of textual data corresponding to the first dataset. The system may compare the first text data and the first plurality of textual data to determine similarity metrics. Based on the similarity metrics, the system may determine the first label record for the first dataset. The system may generate a first recommendation for a first label for the first text data.

Abstract: Systems and methods for documenting label versions for machine learning model input datasets are disclosed herein. The system may receive a label modification request for a dataset. The system may determine a dataset identifier and model error indicator. The system may determine a modification timestamp. The system may generate a label record and generate the label record in a label record database.

Abstract: In some implementations, a transaction card includes a card body having a first surface and a second surface opposite the first surface, and the card body may be in a configuration that defines a storage area for a physical key. The transaction card may include at least one of an integrated circuit chip or a magnetic stripe accessible from at least one of the first surface or the second surface of the card body. The transaction card may include a connecting element to connect the physical key to the card body. The connecting element may be configured to enable movement of the physical key between a storage position, in which at least a portion of the physical key occupies the storage area, and a use position, in which at least a portion of the physical key is outside of the storage area.

Abstract: Disclosed embodiments pertain to identifying sensitive data using redacted data. Data entry into electronic form fields can be monitored and analyzed to detect improperly entered sensitive data. The type of sensitive data can be determined, and the sensitive data can be removed or redacted from the electronic form field. Surrounding context data, including text associated with the sensitive data, can be identified and captured. The context data and type of sensitive data can be utilized to train or update a machine learning model configured to identify sensitive data. In one instance, the machine learning model can be employed to detect improperly entered sensitive data, and context and type can be utilized to improve the performance and predictive power of the machine learning model.

Abstract: In some implementations, a transaction card includes a first card body portion, a second card body portion, at least one of an integrated circuit chip or a magnetic stripe accessible from at least one of the first card body portion or the second card body portion, and a third card body portion that includes a series of accordion pleats. The series of accordion pleats may be between the first card body portion and the second card body portion. The series of accordion pleats may be configured to contract the third card body portion into a contracted configuration, and to expand the third card body portion into an expanded configuration. The first card body portion may be nearer to the second card body portion with the third card body portion in the contracted configuration than with the third card body portion in the expanded configuration.