Mastercard

Abstract Embodiments herein provide a server including a memory and an electronic processor. The server includes a UDID database. The electronic processor is configured to receive an identification request regarding a currently-observed user device having a first set of attributes associated with the currently-observed user device. The first set of attributes includes an IP address of the currently-observed user device. The electronic processor is further configured to generate an IP hardened UDID associated with the currently-observed user device based on the first set of attributes, and store the IP hardened UDID in the UDID database. The generation of the IP hardened UDID compensates for changes in the IP address of the currently-observed user device over a period of time.

Abstract Systems and methods are provided for facilitating biometric-enabled network interactions. One example computer-implemented method includes receiving, at a biometric identity switch (BIS) computing device, from a point-of-sale (POS) terminal of a merchant, a request for an authorization packet for a transaction between the merchant and a user and identifying a biometric provider associated with a biometric in the request. The method also includes requesting, by the BIS computing device, from the identified biometric provider, a biometric identifier for the user, based on the biometric, receiving the biometric identifier from the identified biometric provider, determining the authorization packet for the user based on the biometric identifier, and transmitting the authorization packet to the POS terminal for use by the POS terminal in compiling an authorization request for the transaction.

Abstract A system is configured to retrieve a set of raw transaction data. A transaction categorization model is applied to the raw transaction data. The transaction categorization model infers a category from each transaction and labels each transaction with the inferred category. An entity recognition model is applied to the labelled transaction data. The entity recognition model extracts an entity from each transaction and labels each transaction with the extracted entity. The system generates a plurality of transaction streams from the labelled transactions based on the category and entity labels. The system also labels each transaction stream with either a revenue label or a non-revenue label based on an analysis of the types of transactions defining the transaction stream. The system trains a supervised-based neural network using the labelled transaction streams to generate a revenue stream classifier model.

Abstract A method for enhancing security, reliability, and efficiency of data quality management comprising edge computing and blockchain network is disclosed. An edge server off-chain from the blockchain network is located in close proximity to a plurality of data sources with respect to other servers. The edge server performs an initial data analysis on received data entries. The blockchain node uses the plurality of data entries received from the edge server to determine whether the data quality is above or below a predetermined threshold value, and executes the smart contract based on the determined data quality and predefined rules.

Abstract Systems and methods are provided for authenticating users. An exemplary method includes receiving, by a directory server (DS), an authentication request for a transaction to an account where the request includes a token and a cryptogram, and transmitting the token and cryptogram to a digital service server (DSS). The method also includes mapping, by the DSS, the token to an account number for the account, validating the cryptogram, generating a directory server nonce (DSN) for the request, and transmitting the DSN and the account number to the DS. The method further includes transmitting, by the DS, the DSN and the account number to an access control server (ACS) associated with an issuer of the account and, in response to an issuer authentication value (IAV), compiling an accountholder authentication value (AAV) including the IAV, the DSN and an amount of the transaction and transmitting the AAV to a merchant or server.

Abstract A method for obfuscated storage and transmission of Personal Identifiable Information (PII) includes applying a collisionable hash algorithm to each name in a dataset of potentially personal identifiable information to generate a set of obfuscated names. Applying the collisionable hash algorithm involves selecting a first group of characters from a name proceeding from left to right; selecting a second group of characters from the name proceeding from right to left; combining the first group of characters and the second group of characters to generate a sequence of characters; and applying a cipher to the sequence of characters to generate an obfuscated name for the set of obfuscated names.

Abstract A method of providing a secure service at a computing node is described. The secure service is for a requesting party external to the computing node. The following steps take place at the computing node. A service request is received from the requesting party. This service request comprises a request to generate a credential. The credential is then generated, and service-related information is obtained. The credential and the service-related information are encrypted using an encryption process to form an encrypted message part. A service-identifying clear message part is also created, and a message is sent comprising the clear message part and the encrypted message part to the requesting party. Methods of using such a message to validate the credential, and of using such a message to confirm the integrity of service-related information held in the message, are also described, as is computing apparatus adapted to carry out one or more of these methods.

Abstract A computer system including a first network-based portal, a second network-based portal, and an analyzer computing device including at least one processor is provided. The at least one processor is configured to: (i) receive an initial data message including data elements, (ii) analyze, using machine learning tools, the data elements to determine to route the initial data message to the second network-based portal or to a processing computing device, (iii) in response to determining to route the initial data message to the second network-based portal, route the initial data message to the second network-based portal via a first network communication link, and (iv) in response to determining to route the initial data message to the processing computing device via a second network communication link, reformat the initial data message into a different protocol compatible with the second network communication link, and transmit the reformatted message to the processing computing device.