American Express Travel Related Services Company, Inc.

Kamal Gupta

Tirthankar Choudhuri

Lee C Chau

Subhajit Sanyal

Subhra Shankar Purkayastha

Dawn Thomas

Ajay Choudhary

Amit Jagdish Agarwal

Pranav Mehta

Vidit Jain

Mohd Arshad Naeem

Prakruthi Prabhakar

Vikas Grover

The present disclosure presents systems and related methods for creating real-time predictions. One such method comprises receiving, by a computing device, a first set of data and a second set of data, wherein the first set of data comprises a plurality of items available from a first source for a first set of users and the second set of data comprises transaction purchase data for a second set of users that have reward accounts, utilizing a predictive data model that determines a propensity score for a user from only behavior data that is not attributed to the user; receiving a third set of data from a third source comprising social media channel data for a third set of users; and updating the predictive data model to determine the propensity score for the user based at least in part on the third set of data.

Systems and methods for tailoring marketing

The systems, methods and computer program products (collectively 'system') described herein relate to customized real time data delivery. The system may be configured to receive, by a performance marketing cluster, first data from a first data source. The system may also receive, by the performance marketing cluster, second data from a second data source. The system may determine, by the marketing cluster, an analysis scheme for the first data and the second data based on the first data source. The system may also determine, by the marketing cluster, at least one of a propensity to act or a recommendation selected from a predefined number of available options for a population based on the analysis scheme and the first data source.

The systems and methods herein may include receiving a plurality of transactions for a plurality of consumers, wherein each respective transaction of the plurality of transactions is between a consumer of the plurality of consumers and a merchant of a plurality of merchants; automatically inputting the plurality of transactions into a neural network; automatically analyzing the plurality of transactions over a plurality of iterations, wherein an iteration of the plurality of iterations comprises cycling through a consumer transaction history associated with the consumer, wherein the consumer transaction history has a consumer transaction sequence associated with the consumer; and automatically updating over the plurality of iterations, a previous fraud detection variable associated with the consumer and/or the merchant to generate updated fraud detection variables, in response to the analyzing the plurality of transactions.

Applying multi-dimensional variables to determine fraud

Systems and methods for an automatically-updating fraud detection variable

Growing community of inventors

Bengaluru, India

Kamal Gupta