Mastercard International Incorporated

Gaurav Dhama

Ankur Arora

Hardik Wadhwa

Vikas Bishnoi

Yatin Katyal

Ankur Saraswat

Puneet Vashisht

Debasmita Das

Ganesh Nagendra Prasad

Shashank Dubey

Himanshi Charotia

Kanishka Kayathwal

Ankur Dua

Aakash Deep Singh

Jessica Carta

Smriti Gupta

Shreyansh Singh

Siddharth Vimal

Recommendations of one or more authorizing components are provided to issuers and/or merchants for enhancing approval rates of payment processing requests. A server system receives a payment authorization request for a payment transaction between a cardholder and a merchant in real time. Payment transaction features associated with the payment transaction are identified based on the payment authorization request. A combination of one or more authorizing components to be applied to the payment transaction is predicted to obtain a product recommendation strategy for the payment transaction. The combination of one or more authorizing components is predicted based on a trained machine learning model and the payment transaction features. The payment authorization request and the product recommendation strategy are transmitted to an issuer associated with the cardholder.

Artificial intelligence based product recommendation methods and systems for enhancing approvals of payment processing requests

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.

Method and system for entity resolution

A computing device for determining a new credit card number that is a continuation match with an old credit card number of a credit card account that has changed numbers comprises a processing element programmed to: receive transactional data for a plurality of credit card numbers, determine a plurality of old credit card numbers and a plurality of new credit card numbers, determine a plurality of clusters of new credit card numbers, convert the transactional data for each old credit card number and the associated cluster of new credit card numbers into snapshots with an image-like data format, train a modified siamese network with instances of snapshots of an old credit card number, a first new credit card number, and a second new credit card number, and use the modified siamese network to determine one new credit card number that is an upgrade of one old credit card number.

Method to determine that a credit card number change has occurred

The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.

Recommending the most relevant charity for a news article

Embodiments provide methods and systems for merchant data cleansing in payment network. Method performed by server system includes accessing electronic payment transaction records from transaction database. Each electronic payment transaction record includes merchant data fields. Method includes determining set of electronic payment transaction records with ambiguous merchant data fields having matching probability scores less than predetermined threshold value computed by probabilistic matching model and identifying at least one issue for non-matching of each of set of electronic payment transaction records. Method includes determining data model based on at least one issue of each of set of electronic payment transaction records. Data model is one of: phone-to-city model, payment aggregator model, and merchant name normalization model. Method includes updating set of electronic payment transaction records with unambiguous merchant data fields corresponding to ambiguous merchant data fields by applying data model to each of set of electronic payment transaction records.

Server systems and methods for merchant data cleansing in payment network

A method for detecting fraudulent transactions includes generation of a graph including a plurality of nodes and a plurality edges between the plurality of nodes based on historical transaction data of a plurality of historical transactions. The plurality of nodes include a set of merchant nodes and a set of consumer nodes. A set of static features and a plurality of dynamic features are determined based on the historical transaction data and the generated graph, respectively. A neural network is trained based on the set of static features and the plurality of dynamic features for detection of transaction fraud. The neural network is used to detect a first transaction as one of a fraudulent transaction or a legitimate transaction based on first transaction data of the first transaction.

Method and system for detecting fraudulent transactions

The invention enables optimizing performance of a recommendation server. The invention comprises (i) receiving a first set of customer information corresponding to a first set of events recorded in a first time period in which the recommendation server operates in a first configuration state, (ii) generating a first performance evaluation score based on the first set of customer information, (iii) reconfiguring the recommendation server to operate in a second configuration state having a second performance evaluation score associated therewith, and wherein said second performance evaluation score is generated based on a second set of customer information corresponding to a second set of events recorded in a second time period in which the recommendation server operates in the second configuration state and (iv) transmitting to a terminal device, one or more electronic offers selected for transmission to the customer by the recommendation server operating in the second configuration state.

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Gurgaon, India

Gaurav Dhama