Mastercard International Incorporated

Ganesh Nagendra Prasad

Deepak Bhatt

Ankur Arora

Karamjit Singh

Gaurav Dhama

Tanmoy Bhowmik

Bhargav Pandillapalli

Chinmay Sharad Sagade

Shiv Markam

Akash Singh

Srinivasan Chandrasekharan

Jessica Peretta

Kanishka Kayathwal

Shreyansh Singh

Jessica Carta

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

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

Systems and computer-implemented methods are described for modeling card inactivity. For example, hierarchical modeling may be used in which a first level classifier may be trained and validated to predict whether a card will be inactive. For cards predicted to become inactive by the first level classifier, a second level classifier may be trained and validated to predict when the card will become inactive. The first level classifier may include a binary classifier that generates two probabilities that respectively predict that the card will and will not become inactive. The second level classifier may include a multi-class classifier that generates a first probability that the card will become inactive at a first time period (such as one or more months in the future) and a second probability that the card will become inactive at a second time period. The multi-class classifier may generate other probabilities corresponding to other time periods.

Card inactivity modeling

Disclosed are exemplary embodiments of systems and methods for use in geolocation analysis. In one exemplary method, a computing device accesses boundary definitions associated with regions. For each region, the computing device determines a centroid of the region, having a location including a latitude and a longitude, based on an associated boundary definition, truncates the centroid location by deleting the latitude or longitude, and stores the truncated location as a partition key for the region. The computing device also accesses a transaction record, including a merchant point location having a latitude and longitude. The computing device truncates the point location by deleting the latitude or longitude, and identifies regions associated with the partition keys based on a comparison of the truncated point location to the partition keys. The computing device then determines whether the point location is included in one of the identified regions.

Systems and methods for use in geolocation tracking

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West New York, NJ, United States of America

Ganesh Nagendra Prasad