Abstract: Methods and server systems for computing fraud risk scores for various merchants associated with an acquirer described herein. The method performed by a server system includes accessing merchant-related transaction data including merchant-related transaction indicators associated with a merchant from a transaction database. Method includes generating a merchant-related transaction features based on the merchant-related indicators. Method includes generating via risk prediction models, for a payment transaction with the merchant, merchant health and compliance risk scores, merchant terminal risk scores, merchant chargeback risk scores, and merchant activity risk scores based on the merchant-related transaction features. Method includes facilitating transmission of a notification message to an acquirer server associated with the merchant.

Abstract: A device receives image data that depicts a target that is subject to a security check, and receives transactional data identifying a characteristic of a transaction associated with the target. The device identifies the target within the image data and identifies a first set of target attributes of the target. The device determines a risk level that represents a likelihood of the characteristic of the transaction having a correct value by analyzing the set of target attributes using one or more attribute recognition techniques and/or using a data model that has been trained using machine learning. The device determines whether the risk level satisfies one or more threshold risk levels. The device provides an alert to another device cause the other device to perform actions based on whether at least one of the one or more threshold risk levels are satisfied.

Abstract: A device receives image data that depicts an individual, identifies the individual by using a target recognition technique to analyze the image data, and identifies human attributes of the individual. The device determines a risk level that represents a likelihood of the individual being or becoming involved in a harmful act. The risk level may be determined by using the human attributes as part of a machine-learning-driven emotional analysis of the individual. The device provides a message, selected based on whether the risk level satisfies at least one of the one or more threshold risk levels, to another device, to cause the other device to perform actions that are indicative of the risk level being associated with a low amount of risk, or other actions indicative of the risk level being associated with a high amount of risk and that are associated with assisting in prevention of the harmful act.

Abstract: A device receives image data that depicts a target that is subject to a security check, and receives transactional data identifying a characteristic of a transaction associated with the target. The device identifies the target within the image data and identifies a first set of target attributes of the target. The device determines a risk level that represents a likelihood of the characteristic of the transaction having a correct value by analyzing the set of target attributes using one or more attribute recognition techniques and/or using a data model that has been trained using machine learning. The device determines whether the risk level satisfies one or more threshold risk levels. The device provides an alert to another device cause the other device to perform actions based on whether at least one of the one or more threshold risk levels are satisfied.

Abstract: A device receives image data that depicts an individual, identifies the individual by using a target recognition technique to analyze the image data, and identifies human attributes of the individual. The device determines a risk level that represents a likelihood of the individual being or becoming involved in a harmful act. The risk level may be determined by using the human attributes as part of a machine-learning-driven emotional analysis of the individual. The device provides a message, selected based on whether the risk level satisfies at least one of the one or more threshold risk levels, to another device, to cause the other device to perform actions that are indicative of the risk level being associated with a low amount of risk, or other actions indicative of the risk level being associated with a high amount of risk and that are associated with assisting in prevention of the harmful act.

Abstract: A mobile application and computer-implemented method for facilitating in-person social introductions among other users that include logging into the application, accessing a map showing the location of the user and member locations, entering a member location to determine which other users are present at the member location, viewing other member information on a handheld device, communicating with at least one other user to make an in-person face-to-face meeting with the other user, and meeting the other user face-to-face. Invoices are generated and sent to member locations based on the amount of time a user is present at the member location.