Mastercard International Incorporated

Deepak Bhatt

Tanmoy Bhowmik

Ganesh Nagendra Prasad

Karamjit Singh

Bhargav Pandillapalli

Shiv Markam

Deepak Yadav

Srinivasan Chandrasekharan

Jessica Peretta

Akash Singh

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

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

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Dehradun, India

Deepak Bhatt