Mitigating Disputes and Chargebacks: A Comprehensive Framework

Card-Not-Present (CNP) fraud continues to impose substantial financial and reputational burdens on businesses globally, with worldwide losses reaching $ 33.83 billion in 2023 and chargeback volumes projected at 261 million transactions in 2025. This paper presents a comprehensive framework integrating proactive prevention through Time-Bound Deferred Authorisation within 3DS 2.0, reactive detection using SelfSupervised Tabular Learning (S-STL) models, and operational excellence through a five-phase dispute lifecycle management system.

We analyse recent regulatory updates from RBI and NPCI, examine the superior performance of advanced machine learning approaches, including Hyphatia, over traditional methods such as XGBoost, and present an actionable implementation roadmap. Our integrated approach addresses both technical and operational dimensions, balancing robust security with a seamless user experience whilst ensuring regulatory compliance.

Card-Not-Present fraud, 3D Secure 2.0, XGBoost, Self-Supervised Learning, fraud detection, chargebacks, machine learning, dispute resolution, UPI