Mastercard International Incorporated

Kamal Kant

Karamjit Singh

Yatin Katyal

Ankur Saraswat

Tanmoy Bhowmik

Debasmita Das

Bhargav Pandillapalli

Ajay Panwar

Aniruddha Mitra

Rajesh Kumar Ranjan

Sonali Syngal

Nishant Pant

Nitish Kumar

Suhas Powar

Kshitij Gangwar

Pritam Kumar Nath

Samarth Goel

Embodiments provide artificial intelligence-based methods and systems for predicting account-level risk scores associated with cardholders. Method performed by server system includes accessing payment transaction data and cardholder risk data associated with cardholder. The payment transaction data includes transaction variables associated with past payment transactions performed at Point of Interaction (POI) terminals within a particular time window. Method includes generating cardholder profile data based on the transaction variables and the cardholder risk data. Method includes determining account-level risk scores associated with the cardholder based on cardholder profile data. Each account-level risk score of account-level risk scores is determined by a trained machine learning model. The account-level risk scores include a wallet reload risk score, an account reissuance risk score, and a transaction channel risk score. Further, the method includes transmitting a recommendation message to an issuer server associated with the cardholder based on the account-level risk scores.

Methods and systems for predicting account-level risk scores of cardholders

Embodiments provide methods and systems for determining prospective acquisitions among business entities using machine learning techniques. Method performed by server system includes accessing financial data items and news items associated with finances of business entities from data sources for particular time duration. Method includes generating financial and news feature vectors corresponding to business entities and applying machine learning models over financial feature vectors and news feature vectors associated with business entities for determining candidate set of business entities predicted to be engaged in business acquisition in future. Method includes creating dynamic bipartite knowledge graph for each distinct time durations within particular time duration and generating static bipartite knowledge graph based on dynamic bipartite knowledge graphs for distinct time durations. Method includes predicting occurrence of acquisition of at least one business entity of candidate set of business entities based on supervised machine learning model and static bipartite knowledge graph.

Machine learning models based methods and systems for determining prospective acquisitions between business entities

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Patna, India

Kamal Kant