Mastercard International Incorporated

Hardik Wadhwa

Gaurav Dhama

Ankur Arora

Ankur Saraswat

Puneet Vashisht

Vikas Bishnoi

Yatin Katyal

Debasmita Das

Smriti Gupta

Chandan Garg

Shubham Kumar

Deepak Yadav

Rajesh Kumar Ranjan

Govind Vitthal Waghmare

Jessica Peretta

Suhas Powar

Garima Arora

Nitish Kumar

Puneet Keshtwal

Diksha Shrivastava

Aakash Deep Singh

Shashank Dubey

Kanishk Goyal

Rupesh Kumar Sankhala

Anubhav Gupta

Siddhartha Asthana

Ammar Ahmad Khan

Siddharth Vimal

Gaurav Oberoi

Sourojit Bhaduri

Akash Choudhary

Adarsh Patankar

Amrita Kundu

Brian M McGuigan

Ayush Agarwal

Awanish Kumar

Lekhana Vusse

Rohit Bhattacharya

Niranjan Gulla

Soumyadeep Ghosh

Alekhya Bhatraju

Srijita Tiwari

Ram Ganesh V

Maneet Singh

Karthik Prasad

Methods and server systems for computing fraud risk scores for various merchants associated with an acquirer described herein. The method performed by a server system includes accessing merchant-related transaction data including merchant-related transaction indicators associated with a merchant from a transaction database. Method includes generating a merchant-related transaction features based on the merchant-related indicators. Method includes generating via risk prediction models, for a payment transaction with the merchant, merchant health and compliance risk scores, merchant terminal risk scores, merchant chargeback risk scores, and merchant activity risk scores based on the merchant-related transaction features. Method includes facilitating transmission of a notification message to an acquirer server associated with the merchant. The notification message includes the merchant health and compliance risk scores, the merchant terminal risk scores, the merchant chargeback risk scores, and the merchant activity risk scores.

Artificial intelligence-based fraud and risk management methods and systems for acquirers

Recommendations of one or more authorizing components are provided to issuers and/or merchants for enhancing approval rates of payment processing requests. A server system receives a payment authorization request for a payment transaction between a cardholder and a merchant in real time. Payment transaction features associated with the payment transaction are identified based on the payment authorization request. A combination of one or more authorizing components to be applied to the payment transaction is predicted to obtain a product recommendation strategy for the payment transaction. The combination of one or more authorizing components is predicted based on a trained machine learning model and the payment transaction features. The payment authorization request and the product recommendation strategy are transmitted to an issuer associated with the cardholder.

Artificial intelligence based product recommendation methods and systems for enhancing approvals of payment processing requests

A computing device for determining a new credit card number that is a continuation match with an old credit card number of a credit card account that has changed numbers comprises a processing element programmed to: receive transactional data for a plurality of credit card numbers, determine a plurality of old credit card numbers and a plurality of new credit card numbers, determine a plurality of clusters of new credit card numbers, convert the transactional data for each old credit card number and the associated cluster of new credit card numbers into snapshots with an image-like data format, train a modified siamese network with instances of snapshots of an old credit card number, a first new credit card number, and a second new credit card number, and use the modified siamese network to determine one new credit card number that is an upgrade of one old credit card number.

Method to determine that a credit card number change has occurred

Embodiments provide methods and systems for merchant data cleansing in payment network. Method performed by server system includes accessing electronic payment transaction records from transaction database. Each electronic payment transaction record includes merchant data fields. Method includes determining set of electronic payment transaction records with ambiguous merchant data fields having matching probability scores less than predetermined threshold value computed by probabilistic matching model and identifying at least one issue for non-matching of each of set of electronic payment transaction records. Method includes determining data model based on at least one issue of each of set of electronic payment transaction records. Data model is one of: phone-to-city model, payment aggregator model, and merchant name normalization model. Method includes updating set of electronic payment transaction records with unambiguous merchant data fields corresponding to ambiguous merchant data fields by applying data model to each of set of electronic payment transaction records.

Server systems and methods for merchant data cleansing in payment network

A method for detecting fraudulent transactions includes generation of a graph including a plurality of nodes and a plurality edges between the plurality of nodes based on historical transaction data of a plurality of historical transactions. The plurality of nodes include a set of merchant nodes and a set of consumer nodes. A set of static features and a plurality of dynamic features are determined based on the historical transaction data and the generated graph, respectively. A neural network is trained based on the set of static features and the plurality of dynamic features for detection of transaction fraud. The neural network is used to detect a first transaction as one of a fraudulent transaction or a legitimate transaction based on first transaction data of the first transaction.

Method and system for detecting fraudulent transactions

A method for improving consensus in a blockchain network through decentralized grouping includes: identifying, by each node of a plurality of nodes in a blockchain network that manages a blockchain, a plurality of groups, where each is comprised of a subset of nodes; generating, by each node in each subset of nodes, a new block for the blockchain; performing, by each subset of nodes, a first consensus operation among all nodes in the subset of nodes for the new block generated by in the subset of nodes to identify a group consensus block, where each node in the subset of nodes receives the group's group consensus block; and performing, by the blockchain network, a second consensus operation among all groups for the identified group consensus block to identify an overall consensus block, where a majority of groups of the plurality of groups receives the overall consensus block.

Method and system for grouping of blockchain nodes for greater consensus

Growing community of inventors

Bengaluru, India

Hardik Wadhwa