Neural network based methods and systems for increasing approval rates of payment transactions

US12423702B2 · kind B2 · utility

Assignee

• MASTERCARD INTERNATIONAL INCORPORATED · US

Inventors

• Rajesh Ranjan · Princeton, US
• Garima Arora · Delhi, IN
• Debasmita Das · Sherghati, IN
• Ankur Saraswat · Bhangrola, IN
• Yatin Katyal · Rohtak, IN

Key dates

• Filing date: Oct 7, 2022
• Grant date: Sep 23, 2025
• Expiry date: Jul 26, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/20
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Embodiments of present disclosure provide methods and systems for increasing transaction approval rate. Method performed includes accessing transaction features and determining via fraud model and approval model, first and second set of rank-ordered transaction features. Method includes computing difference in ranks of transaction features and determining set of utilized and unutilized transaction features and generating simulated authorizing model and computing simulated transaction approval rate and simulated fraud transaction rate for simulated authorizing model. Method includes generating plurality of proxy authorization models. Method includes computing transaction approval rates and fraud transaction rates for each of plurality of proxy authorization models and computing an increase in transaction approval rate and change in fraud transaction rate for each of plurality of proxy transaction approval models. Method includes determining one or more recommended transaction features from set of unutilized transaction features and transmitting one or more recommended transaction features to authorizing entity.