Payment processing APIs in modern financial systems face increasing fraud threats that traditional security measures cannot adequately address. Current fraud detection systems operate primarily at the backend level, allowing fraudulent transactions to enter the processing pipeline before detection occurs. This paper presents a conceptual framework for integrating real-time fraud detection capabilities directly within API gateways handling payment transactions. Our framework, called the Smart Payment Gateway (SPG), combines behavioral analysis, transaction pattern recognition, and adaptive risk assessment to identify fraudulent activities at the point of API entry. The framework employs a multilayered approach including request analysis, contextual evaluation, and intelligent decision-making to provide immediate fraud risk assessment without impacting transaction processing performance. Unlike existing solutions that require extensive historical data and complex infrastructure, our conceptual framework operates with minimal data requirements and can be integrated into existing API gateway architectures. The framework addresses key challenges including real-time processing constraints, limited contextual information at the API level, and the need for adaptive responses to evolving fraud patterns. Theoretical analysis demonstrates that the proposed approach can significantly reduce fraud losses while maintaining the performance and scalability requirements of modern payment processing systems. The framework provides a foundation for developing practical fraud detection solutions that can be deployed across diverse payment processing.

Real-time Fraud Detection Payment Processing API Gateway Security Behavioral Analysis Transaction Monitoring Financial Security Smart Gateways