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: 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: A method and related system operations include obtaining a time-ordered set of action types and generating a first dataset by determining, for each respective stored sequence of a plurality of stored sequences, a respective dataset element indicating that the respective stored sequence is present in the time-ordered set of action types. The method may also include generating a reduced dataset based on the first dataset by detecting that the first dataset indicates that a first sequence and a second sequence are present in the time-ordered set of action types, determining a reduced dataset element based on the detection of a presence of the first sequence and the second sequence in the time-ordered set of action types and a score between the first sequence and the second sequence indicated by a table, and detecting malicious activity using a decision model based on the reduced dataset.

Abstract: Methods and systems are described herein for generating group definition sequences for accounts (e.g., user accounts) using action sequence processing and then classifying accounts using those group definition sequences. A plurality of user account actions and corresponding time that each action was taken may be received and based on that information, a sequence of action types sometimes referred to as a time-ordered dataset of action types (e.g., based on a chronological order of the actions) may be generated. The time-ordered dataset of action types may be compared with known time-ordered sequences for a particular user group or user classification. If the time-ordered dataset of action types matches the time-ordered sequences of the particular user group, the user may be classified into that user group.

Abstract: Methods and systems are described herein for generating group definition sequences for accounts (e.g., user accounts) using action sequence processing and then classifying accounts using those group definition sequences. A plurality of user account actions and corresponding time that each action was taken may be received and based on that information, a sequence of action types sometimes referred to as a time-ordered dataset of action types (e.g., based on a chronological order of the actions) may be generated. The time-ordered dataset of action types may be compared with known time-ordered sequences for a particular user group or user classification. If the time-ordered dataset of action types matches the time-ordered sequences of the particular user group, the user may be classified into that user group.

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: A method and related system operations include obtaining a time-ordered set of action types and generating a first dataset by determining, for each respective stored sequence of a plurality of stored sequences, a respective dataset element indicating that the respective stored sequence is present in the time-ordered set of action types. The method may also include generating a reduced dataset based on the first dataset by detecting that the first dataset indicates that a first sequence and a second sequence are present in the time-ordered set of action types, determining a reduced dataset element based on the detection of a presence of the first sequence and the second sequence in the time-ordered set of action types and a score between the first sequence and the second sequence indicated by a table, and detecting malicious activity using a decision model based on the reduced dataset.

Abstract: Methods and systems are described herein for generating group definition sequences for accounts (e.g., user accounts) using action sequence processing and then classifying accounts using those group definition sequences. A plurality of user account actions and corresponding time that each action was taken may be received and based on that information, a sequence of action types sometimes referred to as a time-ordered dataset of action types (e.g., based on a chronological order of the actions) may be generated. The time-ordered dataset of action types may be compared with known time-ordered sequences for a particular user group or user classification. If the time-ordered dataset of action types matches the time-ordered sequences of the particular user group, the user may be classified into that user group.