Mastercard International Incorporated

Telefonaktiebolaget Lm Ericsson (Publ)

Tanmoy Bhowmik

Karamjit Singh

Sangam Verma

Aniruddha Mitra

Deepak Bhatt

Rohit Chauhan

Ashutosh Bisht

Yatin Katyal

Ganesh Nagendra Prasad

Bhargav Pandillapalli

Ajay Panwar

Deepak Yadav

Srinivasan Chandrasekharan

Kamal Kant

Hema Gopalakrishnan

Hanamantagoud V Kandagal

Aakash Agarwal

Samarth Goel

Athanasia Xeros

Nitendra Rajput

Siva Kumar Perumalla

A computing device for analyzing data from user spending patterns to determine offers to be presented to credit card customers comprises a processing element configured to: receive transaction data for a plurality of transactions for each of a plurality of credit card numbers; input the transaction data into an encoder that performs linear transformations and nonlinear transformations to produce latent space data with each latent space data point being associated with one credit card number; input the latent space data into a clustering element which associates each credit card number with one of a plurality of clusters; and make an upgrade offer to credit card numbers that have a normal credit status and which are associated with clusters that include credit card numbers that have a preferred credit status.

Data analysis to determine offers made to credit card customers

Embodiments provide methods and systems for predicting merchant level health intelligence. Method performed by server system includes accessing, from a transaction database, payment transaction data of a merchant for a period of time. The payment transaction data includes information of payment transactions between a plurality of cardholders and the merchant for the period of time. The method includes aggregating merchant transaction attributes based, at least in part, on the payment transaction data and calculating a merchant membership vector based, at least in part, on the merchant transaction attributes by applying a soft-clustering model over the merchant transaction attributes. The method further includes predicting a commercial credit score and a merchant delinquency rate associated with the merchant based, at least in part, on the merchant membership vector. The commercial credit score and the merchant delinquency rate are predicted based, at least in part, on a multi-task learning model.

Artificial intelligence based methods and systems for predicting merchant level health intelligence

Systems and methods are provided for determining an environmental impact of one or more transactions. An example method generally includes accessing transaction data representative of a plurality of transactions, where each of the transactions involves a user and a merchant, and accessing at least one index indicative of environmental impact of a plurality of merchants. The method also includes generating a graph based on the users and merchants included in the transaction data, whereby the graph is representative of the plurality of transactions, and determining a mapping between ones of the merchants involved in the transaction data and the environmental impact indicated by the at least one index, based on at least one of: the graph, the accessed at least one index, a graph convolution network (GCN), and a graph neural network (GNN). The method then includes publishing the mapping between the ones of the merchants and the environmental impact.

Systems and methods for linking indices associated with environmental impact determinations for transactions

A method of managing a controller of a software defined networking (SDN) network is implemented by a computing device in the SDN network. The method includes receiving status information for the controller, receiving usage information for the operating environment, generating at least one failure prediction for the controller based on the received status information, and outputting prediction information for the at least one failure prediction.

Improving software defined networking controller availability using machine learning techniques

Embodiments provide methods and systems for determining prospective acquisitions among business entities using machine learning techniques. Method performed by server system includes accessing financial data items and news items associated with finances of business entities from data sources for particular time duration. Method includes generating financial and news feature vectors corresponding to business entities and applying machine learning models over financial feature vectors and news feature vectors associated with business entities for determining candidate set of business entities predicted to be engaged in business acquisition in future. Method includes creating dynamic bipartite knowledge graph for each distinct time durations within particular time duration and generating static bipartite knowledge graph based on dynamic bipartite knowledge graphs for distinct time durations. Method includes predicting occurrence of acquisition of at least one business entity of candidate set of business entities based on supervised machine learning model and static bipartite knowledge graph.

Machine learning models based methods and systems for determining prospective acquisitions between business entities

Aspects of the disclosure provide a computerized method and system that utilizes reference expense reports to build and train one or more neural network learning models that intelligently determine the riskiness of to-be-determined expense reports submitted for reimbursement. In examples, a determined riskiness may inform a reimbursement entity manager when determining whether to approve, reject, and/or flag for further review a to-be-determined expense report. In instances, computerized expense report resolution systems and methods may be further automated in order to omit user interactions with to-be-determined expense reports, such that an intelligent computer determines whether to approve, reject, and/or flag a to-be-determined expense report based on the intelligently determined riskiness of the to-be-determined expense report.

Intelligent expense report determination system

Growing community of inventors

Bengaluru, India

Tanmoy Bhowmik