Real-Time AI Risk Management for Multi-Party Derivatives Trading A Cloud Compliance Framework
DOI:
https://doi.org/10.5281/zenodo.20678970Keywords:
AI, Cloud Computing, Derivatives, Distributed Systems, Event-Driven Architecture, Financial Services, Microservices, Risk Management, Service Mesh.Abstract
The problem of real-time risk for enterprise-scale derivatives, on the sell-side business, and the orchestration of workflows across multiple counterparties, such as for FX, are well understood; indeed, gap analysis templates exist. Yet no solution is in production. Hedge proposals, whether for market-making services or for wholesale multi-country liquidity coverage fundamentals in bankruptcy planning, are self-evidently Rule 63 costs for which risk-based decision engines at enterprise scale should generate a valid counterparty profile with risk-adjusted positions, identifying the best bid/offer patch for execution and settlement. Furthermore, no architecture, systemic or architectural, meets the needs of all key stakeholders and disciplines.
Leveraging the capabilities of a cloud-native foundation and embedding an AI-driven decision engine within the distinction between decision and execution engine on observed latencies supports risk-aware automation and real-time orchestration. It also strengthens the integration of risk management, regulatory compliance, and risk governance into a coherent whole across the previously examined views of risk types, risk data, the control plane, and the regulatory requirements of the system.
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