Abstract: A method for audience recommendation using node similarity in combined contextual graph embeddings can include receiving a merchant identifier of a merchant and generating one or more merchant tags describing merchant data corresponding to the merchant. A set of audience embeddings can be generated from a set of audience auxiliary data using an audience taxonomy and a set of merchant embeddings can be generated from the merchant data relating to the merchant using the one or more merchant tags. The set of audience embeddings and the set of merchant embeddings are used to produce a heterogenous information network of combined audience data and merchant data, which is then analyzed to identify relationships between each audience and the merchant. A score for one or more audiences can be determined based on the relationships between the one or more audiences and the merchant.

Abstract: Embodiments provide methods and systems for predicting overall account-level risks of cardholders. The method performed by server system includes accessing payment transaction data associated with a cardholder from a transaction database. Method includes generating a set of transaction features based on a set of transaction indicators. The method includes determining a plurality of network risk scores associated with the cardholder based on the set of transaction features and a set of trained machine learning models. The plurality of network risk scores includes a payment capacity risk score, a contactless payment risk score, and a set of account-level risk scores. The method includes aggregating the plurality of network risk scores to calculate an overall account risk score associated with the cardholder based on a statistical model. The method also includes transmitting a notification to the issuer server associated with the cardholder based on the overall account risk score.

Abstract: A method for audience recommendation using node similarity in combined contextual graph embeddings can include receiving a merchant identifier of a merchant and generating one or more merchant tags describing merchant data corresponding to the merchant. A set of audience embeddings can be generated from a set of audience auxiliary data using an audience taxonomy and a set of merchant embeddings can be generated from the merchant data relating to the merchant using the one or more merchant tags. The set of audience embeddings and the set of merchant embeddings are used to produce a heterogenous information network of combined audience data and merchant data, which is then analyzed to identify relationships between each audience and the merchant. A score for one or more audiences can be determined based on the relationships between the one or more audiences and the merchant.

Abstract: A method for facilitating entity resolution is provided. A server generates a graph based on a first dataset comprising a first entity and a second dataset comprising a plurality of entities. Each node in the generated graph corresponds to the first entity or one of the plurality of entities. The server generates a plurality of embeddings for the plurality of nodes in the generated graph. Each of the plurality of embeddings represents an entity as a point in a d-dimensional embedding space. The server identifies a set of nearest neighbors for the first entity based on the plurality of embeddings. The server determines a similarity metric for each of the identified nearest neighbor with respect to the first entity. The server associates the first entity with a second entity of the second dataset that corresponds to a nearest neighbor in the set of nearest neighbors.