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

US12217263B2 · kind B2 · utility

Assignee

• MASTERCARD INTERNATIONAL INCORPORATED · US

Inventors

• Bhargav Pandillapalli · Atmakur, IN
• Rajesh Ranjan · Princeton, US
• Ankur Saraswat · Bhangrola, IN
• Kshitij Gangwar · Bareilly, IN
• Kamal Kant · Patna, IN
• Sonali Syngal · Bhangrola, IN
• Suhas Powar · Kolhapur, IN
• Debasmita Das · Sherghati, IN
• Pritam Kumar Nath · Los Angeles, US
• Nishant Pant · Dehradun, IN
• Yatin Katyal · Rohtak, IN
• Nitish Kumar · Eindhoven, NL
• Karamjit Singh · Gurugram, IN

Key dates

• Filing date: May 6, 2022
• Grant date: Feb 4, 2025
• Expiry date: May 6, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q40/03
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

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.