Abstract: Methods and systems for predicting titles for contents segments of media items at a platform using machine-learning are provided herein. A media item is provided to users of a platform, the media item having a plurality of content segments comprising a first content segment and a second content segment preceding the first content segment in the media item. The first content segment and a title of the second content segment are provided as input to a machine-learning model trained to predict a title for the first content segment that is consistent with the title of the second content segment. One or more outputs of the machine-learning model are obtained which indicate the title for the first content segment. An indication of each content segment and a respective title of each content segment are provided for presentation to at least one user of the one or more users.

Abstract: Systems and methods include: receiving digital event type data that define attributes of a digital event type; receiving digital fraud policy that defines a plurality of digital processing protocols; transmitting via a network the digital event data and the digital fraud policy to a remote digital fraud mitigation platform; using the digital event data to configure a first computing node comprising an events data application program interface or an events data computing server to detect digital events that classify as the digital event type; using digital fraud policy to configure a second computing node comprising a decisioning API or a decisioning computing server to automatically evaluate and automatically select one digital event processing outcome of a plurality of digital event processing outcomes that indicates a disposal of the digital events classified as the digital event type; and implementing a digital threat mitigation application process flow that evaluates digital event data.

Abstract: Systems and methods include: collecting digital event data for the digital account; using a trained machine learning model to extract account takeover (ATO) risk features from the collected digital event data; evaluating the extracted ATO risk features of the collected digital event data of the digital account against a plurality of ATO risk heuristics; identifying one or more of the plurality of ATO risk heuristics that is triggered by the extracted ATO risk features, wherein one or more of the plurality of ATO risk heuristics may be triggered if at least a subset of the extracted ATO risk features matches requirements of the one or more ATO risk heuristics; and generating an ATO risk assessment for the digital account using the one or more triggered ATO risk heuristics.

Abstract: Systems and methods include implementing a remote machine learning service that collects digital event data; collecting incumbent digital threat scores generated by an incumbent machine learning model and successor digital threat scores generated by a successor digital threat machine learning (ML) model; implementing anomalous-shift-detection that detects whether the successor digital threat scores of the successor digital threat ML model produces an anomalous shift; if the anomalous shift is detected by the machine learning model validation system, blocking a deployment of the successor digital threat model to a live ensemble of digital threat scoring models; or if the anomalous shift is not detected by the machine learning model validation system, deploying the successor digital threat ML model by replacing the incumbent digital threat ML model in a live ensemble of digital threat scoring models with the successor digital threat ML model.

Abstract: Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.

Abstract: Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.

Abstract: Systems and methods include: receiving digital event type data that define attributes of a digital event type; receiving digital fraud policy that defines a plurality of digital processing protocols; transmitting via a network the digital event data and the digital fraud policy to a remote digital fraud mitigation platform; using the digital event data to configure a first computing node comprising an events data application program interface or an events data computing server to detect digital events that classify as the digital event type; using digital fraud policy to configure a second computing node comprising a decisioning API or a decisioning computing server to automatically evaluate and automatically select one digital event processing outcome of a plurality of digital event processing outcomes that indicates a disposal of the digital events classified as the digital event type; and implementing a digital threat mitigation application process flow that evaluates digital event data.

Abstract: Systems and methods include implementing a remote machine learning service that collects digital event data; collecting incumbent digital threat scores generated by an incumbent machine learning model and successor digital threat scores generated by a successor digital threat machine learning (ML) model; implementing anomalous-shift-detection that detects whether the successor digital threat scores of the successor digital threat ML model produces an anomalous shift; if the anomalous shift is detected by the machine learning model validation system, blocking a deployment of the successor digital threat model to a live ensemble of digital threat scoring models; or if the anomalous shift is not detected by the machine learning model validation system, deploying the successor digital threat ML model by replacing the incumbent digital threat ML model in a live ensemble of digital threat scoring models with the successor digital threat ML model.

Abstract: Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.

Abstract: Systems and methods include: receiving digital event type data that define attributes of a digital event type; receiving digital fraud policy that defines a plurality of digital processing protocols; transmitting via a network the digital event data and the digital fraud policy to a remote digital fraud mitigation platform; using the digital event data to configure a first computing node comprising an events data application program interface or an events data computing server to detect digital events that classify as the digital event type; using digital fraud policy to configure a second computing node comprising a decisioning API or a decisioning computing server to automatically evaluate and automatically select one digital event processing outcome of a plurality of digital event processing outcomes that indicates a disposal of the digital events classified as the digital event type; and implementing a digital threat mitigation application process flow that evaluates digital event data.

Abstract: Systems and methods include: collecting digital event data for the digital account; using a trained machine learning model to extract account takeover (ATO) risk features from the collected digital event data; evaluating the extracted ATO risk features of the collected digital event data of the digital account against a plurality of ATO risk heuristics; identifying one or more of the plurality of ATO risk heuristics that is triggered by the extracted ATO risk features, wherein one or more of the plurality of ATO risk heuristics may be triggered if at least a subset of the extracted ATO risk features matches requirements of the one or more ATO risk heuristics; and generating an ATO risk assessment for the digital account using the one or more triggered ATO risk heuristics.

Abstract: Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.

Abstract: Systems and methods include: collecting digital event data for the digital account; using a trained machine learning model to extract account takeover (ATO) risk features from the collected digital event data; evaluating the extracted ATO risk features of the collected digital event data of the digital account against a plurality of ATO risk heuristics; identifying one or more of the plurality of ATO risk heuristics that is triggered by the extracted ATO risk features, wherein one or more of the plurality of ATO risk heuristics may be triggered if at least a subset of the extracted ATO risk features matches requirements of the one or more ATO risk heuristics; and generating an ATO risk assessment for the digital account using the one or more triggered ATO risk heuristics.

Abstract: Systems and methods include: receiving digital event type data that define attributes of a digital event type; receiving digital fraud policy that defines a plurality of digital processing protocols; transmitting via a network the digital event data and the digital fraud policy to a remote digital fraud mitigation platform; using the digital event data to configure a first computing node comprising an events data application program interface or an events data computing server to detect digital events that classify as the digital event type; using digital fraud policy to configure a second computing node comprising a decisioning API or a decisioning computing server to automatically evaluate and automatically select one digital event processing outcome of a plurality of digital event processing outcomes that indicates a disposal of the digital events classified as the digital event type; and implementing a digital threat mitigation application process flow that evaluates digital event data.

Abstract: Systems and methods include: collecting digital event data for the digital account; using a trained machine learning model to extract account takeover (ATO) risk features from the collected digital event data; evaluating the extracted ATO risk features of the collected digital event data of the digital account against a plurality of ATO risk heuristics; identifying one or more of the plurality of ATO risk heuristics that is triggered by the extracted ATO risk features, wherein one or more of the plurality of ATO risk heuristics may be triggered if at least a subset of the extracted ATO risk features matches requirements of the one or more ATO risk heuristics; and generating an ATO risk assessment for the digital account using the one or more triggered ATO risk heuristics.

Abstract: Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.

Abstract: Systems and methods include: collecting digital event data from at least one remote source of digital event data; using the collected digital event data as input into primary machine learning ensemble that predicts the likelihood of digital fraud and/or digital abuse; generating by the machine learning system the global digital threat score; identifying a sub-request for a specific digital threat score for a digital abuse type; in response to identifying the sub-request, providing the input of the collected digital event data to a secondary machine learning model ensemble of the machine learning system that predicts a likelihood of the identified digital abuse type; generating by the secondary machine learning ensemble the specific digital threat score for the digital abuse type based on the input of the collected digital event data; and transmitting the global digital threat score and the specific digital threat score for the identified digital abuse type.

Abstract: Systems and methods include: receiving digital event type data that define attributes of a digital event type; receiving digital fraud policy that defines a plurality of digital processing protocols; transmitting via a network the digital event data and the digital fraud policy to a remote digital fraud mitigation platform; using the digital event data to configure a first computing node comprising an events data application program interface or an events data computing server to detect digital events that classify as the digital event type; using digital fraud policy to configure a second computing node comprising a decisioning API or a decisioning computing server to automatically evaluate and automatically select one digital event processing outcome of a plurality of digital event processing outcomes that indicates a disposal of the digital events classified as the digital event type; and implementing a digital threat mitigation application process flow that evaluates digital event data.

Abstract: An apparatus and method for constructing the two-dimensional five-number puzzle is provided wherein the twenty-five cubes, each with numbered indicia from “1” to “5”, are positionable in a five-times-five relationship grid that forms twenty-five square recesses arranged in five horizontal and five vertical rows intersecting each other, and a plurality of pattern cards can be chosen and positioned as the puzzle's background pattern that divides the five-times-five grid into five sections with three color indicia. The object of the game is to arrange the cubes so that the numerical indicia whereon are unique from “1” to “5” horizontally, vertically, as well as in each section, and the color indicia match those indicia of each section on the pattern card.

Abstract: A stock market board game comprising a game board having a central stock market pricing display for all the stocks, a plurality of dividend spaces indicating dividend amounts, a plurality of spaces holding quarterly earning cards for each stock along the perimeter of the board, a plurality of tables for the estimated quarterly earnings and for the annual target prices for each stock aligning along the perimeter, a plurality of price marker pegs and share-splitting marker pegs for the pricing display, a yearly marker peg indicating years played, a plurality of quarterly earning cards for each stock, and a plurality of dice utilized to generate random numbers for earning card disclosure and price fluctuations. Play money of various denominations and the stock certificates are used for financial transactions between stock market and players. There are peg holes on the board for the easy and stable placement of marker pegs.