Scalable AI Infrastructure for Real Time Payment Processing and Big Data Handling

Abstract

Instant payment rails, open banking, and 24×7 settlement are driving a step change in throughput, tail-latency, and reliability requirements for AI-enabled risk controls in financial systems. This paper proposes reference architecture for scalable AI infrastructure that unifies real-time payment processing with big-data handling while meeting stringent consistency, governance, and regulatory constraints. Building on advances in hybrid/edge–cloud scale-out design, streaming analytics, and AI-driven decisioning, we architect a stream-first stack that couples (i) exactly-once event ingestion and CDC-based ledger integration with Kafka/Flink; (ii) an online–offline feature store synchronized by feature-time contracts; (iii) low-latency model serving on auto scaled GPU/CPU pools with canary/shadow deployment; and (iv) a lake house backbone for repayable training, auditability, and continuous learning. Methodologically, we decompose the latency budget from ingress to decision (SLO p99.9 ≤ 50 ms) and introduce two mechanisms: risk-adaptive autoscaling, which modulates concurrency using model uncertainty and event mix; and consistency-preserving stream/model synchronization, which binds inference to versioned features, schemas, and watermark-aware windows to prevent training/serving skew. We incorporate concept-drift alarms, active learning for rare-event enrichment, and rule-graph/ML co-execution for explainable fraud, AML, and sanctions screening. The work delivers (1) a vendor-agnostic blueprint for regulated, real-time AI at payment scale; (2) formal contracts for data/feature/version governance; and (3) an evaluation methodology that ties risk metrics (precision/recall, alert fatigue) to systems metrics (p99/p99.9, backpressure, recovery time). We discuss implications for interoperable instant-payment schemes, cross-border corridors, and future integration with programmable settlement and privacy-preserving consortium analytics.