Secure Multi-Tenant FinTech Architecture: Real-Time AI-Powered Fraud Detection Pipeline with Encrypted Data Streams

Abstract

This article presents a comprehensive architectural framework for implementing secure multi-tenant FinTech platforms that leverage artificial intelligence for real-time fraud detection while maintaining stringent regulatory compliance and data security standards. The proposed architecture addresses the complex challenges of deploying AI-driven financial services across shared infrastructure environments through innovative approaches, including containerized database sharding, attribute-based access control systems, and secure enclave computation technologies. The framework integrates Apache Kafka and Apache Flink streaming platforms to enable high-velocity transaction processing with end-to-end encryption protocols, ensuring data isolation between tenants while supporting cross-tenant analytical capabilities essential for effective machine learning model training and inference. Advanced AI model implementations incorporate ensemble learning techniques for credit risk assessment and deep learning architectures for fraud detection, utilizing dynamic threshold management systems and automated response frameworks to optimize performance across diverse financial scenarios. The architecture's compliance framework addresses Payment Card Industry Data Security Standard, General Data Protection Regulation, and Sarbanes-Oxley Act requirements through comprehensive audit trails, immutable compliance records, and automated policy enforcement mechanisms that adapt dynamically to changing regulatory landscapes across multiple jurisdictions.