AI-Powered Fraud Risk Scoring for Buy Now, Pay Later (BNPL) Platforms

Abstract

Buy Now, Pay Later platforms represent a growing segment in financial technology that offers consumers flexible payment options while creating unique fraud prevention challenges. This article examines how artificial intelligence and machine learning technologies are transforming fraud risk scoring for BNPL services. By leveraging advanced algorithms and vast amounts of transaction data, BNPL providers can detect sophisticated fraud patterns while maintaining seamless customer experiences. The implementation of AI-powered fraud detection systems involves multiple strategic considerations, from data collection and feature engineering to model training and continuous improvement. While offering significant benefits in detection accuracy, operational efficiency, and adaptability to emerging threats, these systems also present challenges related to data privacy, model interpretability, customer experience balancing, and ongoing maintenance. The successful deployment of AI fraud risk scoring ultimately requires thoughtful implementation strategies that incorporate phased approaches, hybrid detection models, and industry collaboration.