Payment Fraud Detection Models through the Integration of Behavioral Biometrics and Contextual Data

Abstract

Payment fraud has become a significant concern for businesses and consumers alike, with fraudulent activities causing substantial financial losses and eroding trust in digital payment systems. Traditional fraud detection methods often rely on static rules and limited data points, making them less effective in identifying sophisticated fraud schemes. This research article explores the potential of integrating behavioral biometrics and contextual data to enhance the accuracy and efficiency of payment fraud detection models. By leveraging machine learning algorithms and analyzing user behavior patterns and contextual information, we propose a comprehensive framework that can detect fraudulent transactions in real-time while minimizing false positives. The proposed models aim to provide a robust and adaptive solution to combat the ever-evolving landscape of payment fraud.