Abstract: Methods and systems are described herein for identifying malicious activity using deep-linked items related to stochastic images. The system may receive event data associated with an event performed in connection with a token. The system may generate a token embedding based on the event data and may obtain, via a stochastic machine learning model based on the token embedding, an image related to the event. The system may generate, for display, the image and the event data. In some embodiments, the image may be deep-linked to functionality for submitting feedback relating to the event. The system may receive feedback related to the image indicating an invalid event. Based on the feedback related to the image, the system may perform a remedial action related to the token or to the event.

Abstract: Methods and systems are described herein for facilitating token use authentication for access tokens using stochastic images. The system may detect an authentication request to authenticate use of an access token. The access token may be associated with a first image previously displayed to an authenticated user of the access token. The system may retrieve the first image previously displayed to the authenticated user and input parameters previously used to generate the first image. The system may obtain, from a stochastic machine learning model based on the input parameters, a second image different from the first image. The system may generate, for display, an image set including the first image and the second image and may receive a selection of the first image from the image set. The system may then grant the authentication request based on the selection of the first image.

Abstract: Methods and systems are described herein for generating deep-linked stochastic image representations of access tokens that embed token access deep links on a mobile application interface. The system may obtain, in connection with a request to register an access token with an account, token data associated with the access token and event data associated with one or more events performed with the access token. The system may generate, for input to a stochastic machine learning model, input vectors using the token data and the event data. The system may obtain, via the stochastic machine learning model based on the input vectors, an image for the access token and may generate, for display on a user interface associated with the account, an image representation of the access token including the image and a deep link to functionality associated with the access token.

Abstract: Disclosed embodiments can include a system for generating experiences. A system receives user data related to interactions by the user. The system identifies a set of trends for the user based on the interactions. The system receives a locational indicator, the locational indicator providing a first destination. The system searches locational data associated with the first destination to identify one or more experiences for the user. The system compares the set of trends with the locational data. The system calculates a first experience for the user. The system outputs, for display on a user device, the first experience.

Abstract: Disclosed embodiments can include a system for generating experiences. A system receives user data related to interactions by the user. The system identifies a set of trends for the user based on the interactions. The system receives a locational indicator, the locational indicator providing a first destination. The system searches locational data associated with the first destination to identify one or more experiences for the user. The system compares the set of trends with the locational data. The system calculates a first experience for the user. The system outputs, for display on a user device, the first experience.

Abstract: In some implementations, a system may receive interaction data associated with interactions between a user and subsets of a plurality of interaction parties. The system may store the interaction data and the as historical interaction data associated with historical interactions of the user. The system may provide the historical interaction data as input to a machine learning model, which may be trained using supervised learning and the historical interactions of the user or historical interactions of one or more other users with one or more of the plurality of interaction parties. The system may receive an output, based on applying the machine learning model to the historical interaction data, that may indicate one or more recommended interaction parties based at least in part on one or more factors, wherein the one or more recommended parties may be local entities local to a geographic location associated with the user.

Abstract: In some implementations, a system may receive interaction data associated with interactions between a user and subsets of a plurality of interaction parties. The system may store the interaction data and the as historical interaction data associated with historical interactions of the user. The system may provide the historical interaction data as input to a machine learning model, which may be trained using supervised learning and the historical interactions of the user or historical interactions of one or more other users with one or more of the plurality of interaction parties. The system may receive an output, based on applying the machine learning model to the historical interaction data, that may indicate one or more recommended interaction parties based at least in part on one or more factors, wherein the one or more recommended parties may be local entities local to a geographic location associated with the user.

Abstract: Disclosed embodiments pertain to protecting sensitive information. A browser extension associated with a web browser can detect a user entering information associated with the user into an electronic form. The browser extension can monitor the user entering sensitive information into the electronic form and detect that the user has entered sensitive information incorrectly. In response, the browser extension can provide a warning to the user that sensitive information has been incorrectly entered. Instructions can be displayed to a user on how incorrectly entered sensitive information is to be corrected. The incorrectly entered sensitive information is corrected based on a response from the user before the sensitive information propagates beyond the electronic form.

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 system may receive interaction data associated with interactions between a user and subsets of a plurality of interaction parties. The system may store the interaction data and the as historical interaction data associated with historical interactions of the user. The system may provide the historical interaction data as input to a machine learning model, which may be trained using supervised learning and the historical interactions of the user or historical interactions of one or more other users with one or more of the plurality of interaction parties. The system may receive an output, based on applying the machine learning model to the historical interaction data, that may indicate one or more recommended interaction parties based at least in part on one or more factors, wherein the one or more recommended parties may be local entities local to a geographic location associated with the user.

Abstract: A method includes segmenting updates associated with a record into a set of update subsets and generating first and second vectors based on first and second update subsets using a first neural network. The first update subset is associated with a first session and a timestamp, and the second update subset is associated with a second session. The method includes determining a first output using a second neural network based on the first and second vectors and a time difference between the first and second sessions. The method includes selecting a segment of a periodic time interval based on the timestamp, determining a second output using a third neural network based on a ratio based on the segment and the periodic time interval, and generating a characterizing vector using a fourth neural network based on the first and second outputs.