Edge-AI microservice orchestration for private, real-time generative FinTech applications

The financial services industry faces mounting pressures to deliver real-time, personalized services while safeguarding sensitive user data under tight regulatory environments. Yet, prevailing AI systems in FinTech remain largely cloud dependent, which introduces latency bottlenecks, privacy exposure, and compliance risk.  Meanwhile, industry analyses suggest that Edge AI is rapidly becoming a foundational shift, with predictions that 60% of AI deployments will run partially on device by 2029. However, existing edge AI research often focuses on inference optimization, not full-stack orchestration of financial microservices, and therefore, lacks the integrated, decision-oriented intelligence that is required to operate wholly on the device. In this work, we present an architecture for on-device microservice orchestration of generative AI tailored for FinTech use cases.  Our system modularizes AI tasks, such as local LLM inference, fraud detection, biometric authentication, and credit scoring, into services coordinated via lightweight orchestrators (e.g. WASMEdge, Open Horizon). Unlike prior approaches, our system coordinates these services using lightweight WebAssembly-based runtimes, enabling secure, isolated, and efficient execution even on resource-constrained devices.  Sensitive data, such as transaction history and biometric templates, remains strictly local, with optional federated synchronization for global fraud pattern sharing. With quantized LLMs, we attain inference latency under 90ms, while local anomaly detection achieves 72% accuracy in simulated financial fraud scenarios. The architecture integrates modular microservices, privacy-first orchestration, and a hybrid federated intelligence layer and is among the first to present a decentralized, compliant, and performance-sensitive AI infrastructure for the FinTech of reality.