Sovereign AI for Capital Markets
Positions, risk, market data and surveillance each live in their own system, and the models that matter most are the ones a firm will never send to a cloud. The platform gives desks and risk teams one live picture of positions and flows, and AI that acts on it inside the firm's perimeter, with every decision traceable.
One live picture of positions and flows
Positions, orders, market data, reference data and flows fused on a financial ontology, so desk, risk and compliance see the same state.
Signals become approved actions
Decision intelligence turns a risk or credit signal into a recommended action, a limit change, a hedge, an alert, routed through the firm's approval workflow.
Proprietary stays proprietary
Strategies, models and licensed data run and stay inside the firm. No prompt, feature or model leaves in an inference call.
A trading firm or asset manager runs on latency, proprietary models and data it has paid for. Its risk, compliance and surveillance teams need the same picture the desk sees, a day earlier than the batch gives it to them. AI can join positions, market data and flows into one live model and turn a signal into a routed, approved action, but the strategies, the data licences and the regulator all forbid sending any of it outside the firm.
Read this if you're leading trading, risk, compliance, surveillance, data or technology at an asset manager, a trading firm, a broker, an exchange or a market-infrastructure provider.
For capital markets, Scrydon is the sovereign AI and data platform that fuses positions, market data, flows and reference data into one live financial ontology, runs risk, surveillance and decision-intelligence models and agents on it inside the firm's own perimeter, and keeps the versioned, attributed record that MiFID II, DORA and the EU AI Act expect.
It runs on-premises, in a sovereign cloud or in the firm's own tenancy, with open-weight models served locally, proprietary models and licensed data never leaving the perimeter, and every model and agent action logged.
The desk sees today; risk and compliance see yesterday
A trading firm or asset manager runs on latency, proprietary models and data it has paid for, and its edge is exactly what it can never send outside. The desk sees the book in real time; risk, finance and compliance see it the next morning, after the overnight reconciliation of their own copies of positions and market data. Market-abuse and conduct surveillance runs on that batch, so the alert arrives after the trade and the context that would have explained it is gone.
The constraints are structural. Alpha, risk and pricing models are the firm; market and reference data come with licence terms; neither may be sent to an external AI service. MiFID II, EMIR and DORA ask for record-keeping, resilience and third-party risk management the firm has to evidence, and a black-box model outside the perimeter fails all three. So the AI that helps has to fuse the book inside the firm, act only through the firm's approval workflow, and keep the trail.
Split views of the same book — Front office, risk, finance and compliance each have their own copy of positions and market data, reconciled overnight.
Surveillance after the fact — Market-abuse and conduct surveillance runs on batch data, so the alert arrives after the trade and the context is gone.
Models that cannot travel — Alpha, risk and pricing models are the firm; licensed data has usage terms. Neither may be sent to an external AI service.
Regulators want the trail — MiFID II, EMIR and DORA ask for record-keeping, resilience and third-party risk management the firm has to evidence.
Fuse the book, model the risk, route the action
The platform fuses positions, orders, executions, market data, reference data and flows onto one financial ontology in real time, through an analytics layer built for high-throughput, low-latency work over streaming and historical data. Desk, risk, finance and compliance then work on the same live state rather than on copies reconciled overnight, and every fused value keeps a trace to its source and time.
On that model, risk, pricing and surveillance models run on the firm's own infrastructure with features drawn from the ontology, versioned and explainable. Decision intelligence turns a flagged exposure or credit signal into a recommended action, a limit change, a hedge, an alert, with its reasoning, routed to the person who approves it. Surveillance sees a trade in its context, with entity linking across accounts, counterparties and communications. Every model version, data input, agent action and approval is logged under AI governance, which is the record-keeping the regulator asks for.
- 1
Fuse
Positions, orders, executions, market and reference data land on one financial ontology in real time, so desk, risk and compliance share a live state.
- 2
Model
Risk, pricing and surveillance models run on the firm's own infrastructure with features from the ontology, versioned and explainable.
- 3
Decide
Decision intelligence turns a flagged exposure or credit signal into a recommended action with its reasoning, routed to the approver.
- 4
Record
Every model version, data input, agent action and approval is logged, which is the record-keeping the regulator asks for.
The edge is in the models and the data, and both stay inside
The edge is in the models and the data, and both stay inside. The platform runs on-premises, in a sovereign cloud or in the firm's own tenancy, with open-weight models served locally and external models available only by explicit opt-in under policy. Strategies, proprietary models and licensed data are used within the firm under their own terms and never leave in an inference call. On shared infrastructure, confidential computing keeps positions and models encrypted while they are processed.
The trail is built into the way it runs. Every model, dataset and agent action is versioned, attributed and logged; agents act under scoped identities; lineage links a decision to the data and concepts behind it. That record is the DORA and MiFID II evidence, and the EU AI Act documentation, produced by operating the platform rather than assembled for the regulator.
Inside the firm — On-premises, in a sovereign cloud or the firm's own tenancy, with open-weight models served locally and external models by opt-in only.
Licensed data stays licensed — Market and reference data are used within the firm under their terms; nothing is sent to a third-party model.
Encrypted in use — Confidential computing keeps positions and models encrypted while processed on shared infrastructure.
Governance for the trail — Versioning, lineage, attribution and an immutable audit log for every model and agent, as MiFID II and DORA expect.
Keep an eye on this space
What changed for your sector in European AI sovereignty, and what we learned in the field. A few times a year, no drip campaign.
Use cases for capital markets
Risk, surveillance and decision intelligence on one sovereign platform.
Algorithmic Risk Management
Investment
Challenge
Market volatility requires instant analysis of massive datasets, but latency and data privacy concerns limit cloud usage.
Solution
Sovereign AI models analyse market data, news sentiment, and internal positions locally to adjust risk exposure in milliseconds.
Read more
Faster reaction to market events and improved risk-adjusted returns while keeping strategies proprietary.
Regulatory Reporting
Operations
Challenge
Compiling reports for regulators involves gathering data from siloed legacy systems, a tedious and manual process.
Solution
Data space connectors unify disparate data sources, and AI agents generate compliant reports automatically.
Read more
100% on-time reporting accuracy and significant reduction in operational overhead.
Decision Intelligence for Credit & Trading Desks
Risk & Trading
Challenge
Credit and trading decisions are informed by dashboards, but turning an insight into an approved action still means someone manually starts the next process.
Solution
Decision intelligence connects the ontology directly to the next action, recommending a credit limit change or hedge and routing it through the appropriate approval workflow.
Read more
Insight-to-action time cut from hours to minutes, with every automated recommendation traceable back to the data and policy behind it.
Financial Crime Network Investigation
Financial Crime
Challenge
Money-laundering rings operate through webs of accounts, shell companies and shared devices, but transaction monitoring flags one account at a time — so banks close individual accounts while the network simply reroutes.
Solution
A knowledge graph resolves customers, accounts, devices, addresses and counterparties into one entity graph, so investigators expand outward from a single alert to map the full ring before acting.
Read more
Entire networks identified and dismantled from a single alert, instead of a years-long game of closing one account at a time.
What Capital Markets has to comply with
The regulations that decide whether AI can run on this data at all. Each page lists what the framework asks and which platform controls answer it — properties and refusals, not a checklist.
DORA · Digital Operational Resilience Act
Applies to: Financial entities across the EU — banks, insurers, investment firms, payment and crypto-asset providers and others — and the ICT third-party providers that serve them.
How the platform supports itGDPR · General Data Protection Regulation
Applies to: Any organisation that processes the personal data of people in the EU/EEA, whether established in the Union or offering goods, services or monitoring from outside it.
How the platform supports itAI Act · EU AI Act
Applies to: Providers, deployers, importers and distributors placing AI systems on the EU market or whose AI output is used in the EU.
How the platform supports itISO 27001 · ISO/IEC 27001
Applies to: Any organisation that operates an information security management system — routinely required of software vendors, service providers and regulated enterprises by customers, regulators and procurement.
How the platform supports it
What these outcomes actually run on
A capital-markets architect asks how the book is modelled in real time, how a signal becomes an action and where proprietary models run. These are the pages that answer those questions.
Analytics
Self-service analytics over a governed semantic model, so numbers mean the same thing in every report.
Cognitive Enterprise
The ontology that links your systems, data and processes into one model of how the organisation actually works.
AI OS
The runtime that maps a process to steps, routes each step to a system, an agent or a person, and gives it the context to act.
Ontology Based Data Platform
The semantic layer that turns tables into the entities your business talks about.
Confidential Compute
Workloads that stay encrypted while they run, inside hardware-attested enclaves.
AI Governance
Policy, data-loss prevention and audit applied to every AI action, with the evidence an assessor can read.
Frequently asked questions
Can the platform turn a risk signal into an action rather than another dashboard?+
Do our proprietary models and licensed data stay inside the firm?+
How does it support surveillance and market-abuse detection?+
What about MiFID II and DORA record-keeping?+
Can risk analytics run on the same platform as the desk's data?+
Do you partner on tenders and framework contracts?+
Prefer to write? Email hello [at] scrydon.com and we will get back to you.
Keep going
Three routes from here: the rules you will be measured against, the platform these outcomes run on, and the sessions where we walk through them.
The rules that apply
Each regulation, what it asks, and the controls the platform provides for it.
The platform behind it
The AI Operating System, Analytics and Sovereign Foundations, page by page.
Every use case
All of them in one place, grouped by the sector that knows them best.
How – Inside the AI OS: running governed agents on your own cluster, live
17 Sept 2026, 09:00
Part 2 of the Sovereign AI series, for engineers and architects. No slides after minute five: an agent gets an identity and scoped permissions, calls tools over governed MCP, retrieves from the ontology rather than raw tables, runs inside the sandbox and is stopped when it steps outside policy, and everything lands in the audit trail — then the same stack brought up on a disconnected network. Properties and refusals, shown rather than claimed.
Other parts of Financial Services
The other dedicated pages under Financial Services, and the sector overview they hang off.
Banking
Retail, corporate, private and cooperative banks: real-time fraud, financial crime and credit on one financial ontology, inside the bank, with every model decision traceable for DORA.
Read the pageInsurance
Life, non-life, health and reinsurance: underwriting, claims and fraud on one insurance ontology, inside the insurer, with a human deciding where the law requires and every model decision traceable.
Read the page
Partners
