MASTERCARD TECHNOLOGIES CANADA ULC

A system for communicating cardholder data. The system comprises a remuneration vehicle device, a third-party entity device, and a server. The remuneration vehicle device includes a first communication interface, a first electronic processor, and a first memory. The third-party entity device includes a second communication interface, a second electronic processor, and a second memory. The server includes a third communication interface, a third electronic processor, and a third memory. The third electronic processor is configured to receive, with the third communication interface, a first data packet indicating cardholder data from the first communication interface, store the cardholder data in the third memory, receive a second data packet indicating a transaction request from the second communication interface, and provide a third data packet indicating a transaction response to the second communication interface. The third data packet includes the cardholder data from the third memory.

A system is provided for electronically communicating multi-factor authentication (MFA) data. The system includes a remuneration vehicle device including a first communication interface, a first electronic processor, and a first memory, a third-party device including a second communication interface, a second electronic processor, and a second memory, and a server including a third communication interface, a third electronic processor, and a third memory. The third electronic processor is configured to receive, with the third communication interface, a first data packet indicating MFA preferences from the first communication interface, store the MFA preferences in the third memory, receive, with the third communication interface, a second data packet indicating an interaction request from the second communication interface, and provide, with the third communication interface, a third data packet indicating an interaction response to the second communication interface. The third data packet includes the MFA preferences from the third memory.

Serverless containerization of a collaboration application improves deployment and security of the collaboration application. A system may generate a container within a private cloud. The container may execute the collaboration application. The container is assigned an Internet Protocol (IP) address and is associated with a security group that controls network traffic to and from the container. The system may include a network load balancer configured with the IP address assigned to the container that allocates computational resources for executing the collaboration application. The system may include serverless resources such as a database and filesystem that is accessible to the container within the private cloud. The system may include a private cloud (PC) endpoint configured to establish a private connection with an end user interface, wherein the private connection provides secure network traffic between the PC endpoint the end user interface within a private network of the PC endpoint.

A fraud prevention system that includes a fraud prevention server including an electronic processor and a memory. The memory includes an online application origination (OAO) service. When executing the OAO service, the electronic processor is configured to determine whether the OAO service is enabled and whether a website configuration includes a list of multi-page placements for an online application, determine that input data needs to be stored in the memory and combined into multi-page input data, determine a fraud risk score of the online application based on the multi-page input data and an online application origination (OAO) model that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device, and control a client server to approve, hold, or deny the online application based on the fraud risk score that is determined.

Devices, methods, and computer-readable media that perform frictionless multi-factor authentication. In one embodiment, a server includes a memory including a frictionless multi-factor authentication (fMFA) service, and an electronic processor communicatively coupled to the memory. The electronic processor is configured to receive a frictionless multi-factor authentication (fMFA) request and collected data of a user interface device, determine, with a unique device identifier (UDID) component, a unique device identifier (UDID) of the user interface device based on device attributes included in the collected data, determine, with a passive biometrics component, a biometrics match assessment against a historical profile, determine whether a user of the user interface device is authenticated under multi-factor authentication (MFA) based on the UDID and the biometrics match assessment, responsive to determining that the user is authenticated under multi-factor authentication (MFA), output a fMFA authentication response that indicates authentication of the user.

Systems, methods, and non-transitory computer-readable media for identifying electronic communications from a virtual private network (VPN). An VPN/Proxy communication identifier application receiving a request, enriched with location information of the client device, from a client device. Determining a geolocation of the client device based on the location information. Determining a geolocation of an internet protocol (IP) address associated with the client device. Determining a distance between the geolocations of the client device and the IP address. Flagging the request that is received as associated with a virtual private network or a proxy based on the distance that is determined.

User identification with blended response from dual-layer identification service. In one embodiment, a server comprising an electronic processor configured to detect an access request by a user of a user interface device, retrieve a plurality of input profile records from an input profile record repository, perform an identification of the user with one or more passive biometrics models and the plurality of input profile records that are retrieved, generate an identification response and an additional identification request based on an outcome of the identification of the user, control the communication interface to transmit the additional identification request to the second server via the network, receive a second identification response from the second server, and generate a blended response by modifying one or more characteristics of the identification response with the second identification response, the blended response indicating the identification of the user.

Embodiment described herein provide a system for authenticating an identity of a user. The system includes a first device, a second device, a user device, and a remote computing device. The first device, the second device, and the user device are connected to and operable to communicate over the first communications network. The user device is connected to and operable to communicate over a second communications network. The user device is configured to detect the first device or the second device when the first device or the second device is connected the first communications network, and to generate one or more signals related to the detection of the first device or the second device. The remote computing device is configured to receive the one or more signals from the user device and authenticate the identity of the user based on the one or more signals.

Systems, methods, and non-transitory computer-readable media for creating labels for training a machine learning model using a limited dataset. A label creation application receives raw data from a storage device. The raw data includes requests associated with user accounts. The application determines an account type of each of the user accounts. The application generates a raw data set based on account types, requests, and user accounts. The application cleans the raw data set using client feedback data. The feedback data is the limited dataset that includes fraud events associated with user accounts identified by a client. The application extracts a request history for a user account from the raw data that is cleaned. The application generates a training profile for the user account based on the request history. The application creates training labels based on the training profile, and the model is trained by processing the created labels.

A system includes memory hardware configured to store instructions and processing hardware configured to execute the instructions. The instructions include loading a first data set, the first data set including (i) a reference profile and (ii) one or more candidate profiles, providing the first data set to a machine learning model to generate output data, generating telemetry for the first data set, the telemetry including (a) one or more role identifiers associated with the reference profile and (b) one or more candidate identifiers associated with the one or more candidate profiles, processing the output data and telemetry to generate a test metric, adjusting parameters of the machine learning model in response to the test metric meeting a first condition, and saving the machine learning model as a validated machine learning model in response to the test metric meeting a second condition.