American Express Travel Related Services Company, Inc.

Tirthankar Choudhuri

Amber Gupta

Kathleen Haggerty

Chao M Yuan

Di W Xu

Anjali Dewan

Dewan Anjali

Prasanta Sahu

Lee C Chau

Subhajit Sanyal

Subhra Shankar Purkayastha

Kamal Gupta

Dawn Thomas

Jayatu Sen Chaudhury

Amit Jagdish Agarwal

Pranav Mehta

Prakruthi Prabhakar

Mohd Arshad Naeem

Vikas Grover

Ajay Choudhary

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 present invention generally relates to financial data processing, and in particular it relates to credit scoring, consumer profiling, consumer behavior analysis and modeling. More specifically, it relates to risk modeling using the inputs of credit bureau data, size of wallet data, and, optionally, internal data.

Total structural risk model

Predictive models are developed automatically for a plurality of modeling variables. The plurality of modeling variables is transformed, based on a transformation rule. A clustering of the transformed modeling variables is performed to create variable clusters. A set of variables is selected from the variable clusters based on a selection rule. A regression of the set of variables is performed to determine prediction variables. The prediction variables are utilized in developing a predictive model. The development of the predictive model may include modification of the predictive model, review of the plurality of transformations, and validation of the predictive model.

Automated predictive modeling of business future events based on transformation of modeling variables

Automated predictive modeling of business future events based on historical data

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Gurgaon Haryana, India

Tirthankar Choudhuri