Portfolio Accelerator · Capital Markets · Microsoft Azure
Trade Surveillance AI on Azure: A Working Implementation, Not a Slide Deck
A documented, buildable trade surveillance architecture on Azure for spoofing, layering, front-running, insider trading, and wash trading detection.
problem
Why Broker-Dealers Need an AI-Native Trade Surveillance Architecture
Market abuse detection under MiFID II, MAR, Dodd-Frank, SEC Rule 15c3-5, and FINRA obligations increasingly requires surveillance across order flow, execution data, and trader communications at a scale legacy rules engines were never built for. Spoofing, layering, front-running, insider trading, and wash trading each leave a different data fingerprint, and a head of compliance evaluating a modernization project needs to see the actual data flow and detection logic, not a capability slide.
demo
Inside the Accelerator: A Documented Azure Architecture, Built in the Open
This page showcases Kriv AI's trade surveillance accelerator on Microsoft Azure — a fully specified, buildable architecture with its implementation phases tracked in the open. In the interest of transparency: the repository research and reference-architecture phase is complete; the data pipeline, model training, and surveillance application phases are scoped and specified but not yet built out. We show the real state below rather than a polished but misleading demo.
Architecture: Event Hubs, Synapse, Cosmos DB, Azure ML, and Purview
The design ingests market data and order flow through Azure Event Hubs, runs real-time pattern detection through Stream Analytics, warehouses trade data in Azure Synapse Analytics for historical analysis, stores alerts and investigation cases in Azure Cosmos DB, trains abuse-detection models in Azure ML, and governs the full data estate through Microsoft Purview, with Power BI surveillance dashboards for compliance officers.
The design specifies five purpose-built Azure OpenAI agents: a SurveillanceCoordinator that routes and correlates alerts, a MarketAbuseDetector for spoofing/layering/wash-trading pattern detection, a CommunicationsAnalyzer for MNPI keyword and code-word detection in trader communications, an AlertInvestigator that compiles investigation packages, and a RegulatoryReporter for STR/SAR and MiFID II Article 26 filings — each scoped with its own tool access and RBAC role, and a content filter that blocks trading-advice and detection-evasion outputs.
What's Real Today, and What's Roadmap
Honestly: this accelerator's repository-research and reference-architecture phase is complete (24 reference repositories reviewed across Synapse, streaming, Cosmos DB, Azure ML, Purview, and financial anomaly-detection categories), and the agent design, data flow, and regulatory scope above are fully specified. The data pipeline build, model training, and end-to-end surveillance application are the next phases and are not yet deployed. We'd rather tell you exactly where the build stands than dress up a specification as a finished demo.
differentiation
Why an Implementation Partner Beats a Big 4 Surveillance Modernization Deck
Big 4 advisory engagements for trade surveillance modernization typically start with a current-state assessment and a multi-quarter roadmap document. Kriv AI starts from a fully specified, buildable Azure architecture with the agent roles, data flow, and regulatory mapping already designed — shortening the distance from advisory engagement to a working pilot.
governance
Regulatory Scope: MiFID II, MAR, Dodd-Frank, SEC, and FINRA
Multi-Jurisdiction Coverage by Design
The architecture is scoped against MiFID II and MAR (EU transaction reporting and market manipulation surveillance), Dodd-Frank (US swap reporting and anti-manipulation provisions), SEC Rule 15c3-5 (market access controls), and FINRA regulatory reference requirements — so a broker-dealer operating across US and EU venues has a single surveillance data model rather than jurisdiction-specific silos.
engagement
From Blueprint to Production: How the Engagement Works
A scoped engagement takes this architecture and builds out the data pipeline and model layer against your actual order flow and communications data sources, with the same regulatory mapping and agent-based design carried through from day one.
Straight answers
Frequently asked questions about Trade Surveillance AI on Azure: A Working Implementation, Not a Slide Deck
Is the trade surveillance accelerator fully built and deployed?
Not yet, and we'd rather be upfront about it. The reference-architecture and repository-research phase is complete; the data pipeline, model training, and surveillance application are specified but not yet built. This page shows the real architecture, not a finished-product claim.
What Azure services does the design use?
Azure Event Hubs for real-time ingestion, Stream Analytics and Azure Synapse for pattern detection and historical analysis, Cosmos DB for alerts and case management, Azure ML for abuse-detection models, Microsoft Purview for governance, and Power BI for compliance dashboards.
What market abuse patterns does the architecture target?
Spoofing, layering, front-running, insider trading, and wash trading — the five abuse patterns most commonly cited in SEC and FINRA enforcement actions, each mapped to a specific detection approach in the design.
What regulations does this map to?
MiFID II and MAR for EU market abuse and transaction reporting, Dodd-Frank for US swap and anti-manipulation rules, SEC Rule 15c3-5 for market access controls, and FINRA regulatory reference requirements.
Can Kriv AI build this out for our actual data?
Yes — a scoped engagement adapts this architecture to your order flow, execution, and communications data sources. Contact us to discuss scope and timeline.
Why show an architecture that isn't fully built yet?
Because an honest picture of what's built versus what's designed is more useful to a compliance leader evaluating a partner than a polished demo that overstates readiness. We'll tell you exactly where any accelerator in our portfolio stands.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
Book a Discovery Call