Sovereign AI for
Financial Services
Fraud, risk, and compliance teams sit on more data than ever and still decide too slowly: the signals are scattered across core systems, and the black-box cloud AI that promises to help is exactly what DORA and your regulator will not accept. That is the problem we solve: frontier predictive analytics and autonomous agents for fraud detection, risk management, and personalised banking, on one ontology of customers, counterparties, and exposure that keeps every AI decision explainable — with DORA compliance and data sovereignty on our European-native platform.
Built for DORA
Support your Digital Operational Resilience Act obligations with resilient, on-premise AI infrastructure.
Fraud Prevention
Real-time transaction monitoring and anomaly detection with zero data leakage.
Legacy Modernization
Wrap legacy core banking systems with intelligent API layers and AI agents.
Inside Financial Services
Financial Services is not one buyer. Each of these pages takes one part of it and describes the problem, what the platform does there and the rules that apply.
Banking
For: leading fraud, financial crime, credit, operations, data or IT at a retail, corporate, private or cooperative bank, or accountable for its DORA and AI-governance obligations.
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.
- One view of customer and transaction
- Real-time, inside the bank
- Every decision traceable
Insurance
For: leading underwriting, claims, actuarial, data, operations or IT at a life, non-life, health or reinsurance company, or accountable for its DORA, Solvency II and AI-governance obligations.
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.
- One view of the policyholder
- Human where the law requires
- Fair and explainable
Capital Markets
For: leading trading, risk, compliance, surveillance, data or technology at an asset manager, a trading firm, a broker, an exchange or a market-infrastructure provider.
Asset managers, trading desks, exchanges and market infrastructure: one live picture of positions and flows, risk and surveillance models inside the firm, and signals that become approved actions.
- One live picture of positions and flows
- Signals become approved actions
- Proprietary stays proprietary
The same thirty questions, read with DORA and model risk in mind.
Enterprise AI to production: the checklist
About thirty questions to put to your own team, grouped under grounding, governance, orchestration and sovereignty. Tick what is true for you today and see a score per dimension. One page, yours to print.
Secure Financial Innovation
Sovereign AI solutions for regulated financial services.
Real-Time Fraud Detection
Security
Challenge
Traditional rule-based systems generate too many false positives and fail to catch sophisticated new fraud patterns.
Solution
Deploy autonomous agents that learn transaction patterns in real-time, flagging anomalies with high precision without moving data off-premise.
Read more
Reduction in fraud losses by 40% and false positives by 60%, improving customer trust.
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.
Automated KYC/AML
Compliance
Challenge
Manual review of Know Your Customer (KYC) and Anti-Money Laundering (AML) alerts is slow, expensive, and error-prone.
Solution
AI agents automatically gather and verify customer data from multiple sources, summarising findings for compliance officers.
Read more
Onboarding time reduced from days to minutes, with a fully auditable decision trail.
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.
Enterprise AI for financial services
For a bank, an insurer or an asset manager, enterprise AI is the AI that sits inside regulated processes: transaction monitoring and fraud, credit and underwriting decisions, KYC and AML case work, complaints and client servicing, model validation and the reporting that follows. It runs on core banking and policy systems, payment flows, counterparty and exposure data, and years of case files — data that is personal, price-sensitive or both, in processes a supervisor can ask you to explain decision by decision.
It stalls for reasons specific to the sector. DORA treats an AI service as ICT third-party risk, so a copilot with an outside dependency becomes a concentration question rather than a productivity one. The EU AI Act puts creditworthiness assessment of natural persons, and life and health insurance pricing for natural persons, in the high-risk tier. And model risk management wants a version, a dataset and a rationale behind every decision the firm has already taken.
Production therefore starts with custody. Models and data stay inside the firm's perimeter; one ontology of customers, counterparties and exposure gives an answer its lineage; agents act under scoped identities; and the audit trail is produced by running the system rather than assembled for the examiner. That is what enterprise AI means in financial services.
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.
Your Data, Your AI, Your Control
Deploy the Scrydon platform where it makes sense for you — from air-gapped environments to public cloud — with sovereignty, compliance, and auditability built in.
Deployed on-premises, air-gapped or in a sovereign cloud, no data leaves your jurisdiction. No black-box AI. No compromises on control.
This is sovereignty by design.
What these outcomes actually run on
Everything above is delivered by one platform, with the model governance and audit trail your regulator will ask for. For the architects and engineers evaluating it, these are the pieces that matter.
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.
Analytics
Self-service analytics over a governed semantic model, so numbers mean the same thing in every report.
AI Governance
Policy, data-loss prevention and audit applied to every AI action, with the evidence an assessor can read.
Ontology Based Data Platform
The semantic layer that turns tables into the entities your business talks about.
Lakehouse
The sovereign data foundation — lake and warehouse in one, on open formats.
Confidential Compute
Workloads that stay encrypted while they run, inside hardware-attested enclaves.
Laws and standards that matter for Financial Services
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: Applies to dual-use and civilian administrative AI. AI used exclusively for military, defence or national-security purposes is outside its scope (Art. 2(3)).
How the platform supports itISO 42001 · ISO/IEC 42001
Applies to: Any organisation that develops, provides or uses AI systems and wants a certifiable management system for doing so responsibly — providers and deployers preparing for the EU AI Act in particular.
How the platform supports it
Read for DORA: the operator, the keys, and an exit that was rehearsed rather than described.
“Sovereign” is not a label on a datasheet
It is five tests you can run against a live system. Score any vendor on jurisdiction, keys, operating staff, disconnected mode and exit — plus whether the offer covers all four layers, and whether each answer was demonstrated or asserted. Twelve questions, nothing leaves your browser.
Score a vendor- Jurisdiction
- Keys
- Operating staff
- Disconnected mode
- Exit
- Four layers
Run it once per name on the shortlist — including the incumbent, and including us.
Frequently asked questions
What is sovereign AI for financial services and why does it matter?+
How does the platform support DORA and operational resilience?+
Can we run real-time fraud, transaction and risk analytics on the platform?+
How do you handle model governance, explainability and audit for regulated AI?+
We piloted chatbots and copilots — how do we move to organisation-wide, governed AI across the bank?+
Can sensitive financial data stay protected from the cloud operator, or run fully on-premise?+
How do you manage identity, attribution and policy for AI agents acting on financial systems?+
How do you avoid vendor lock-in and stay model-agnostic?+
Can the platform turn an insight directly into an approved action, not just a dashboard?+
Do investigative agents retain memory across a multi-week AML or fraud case?+
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.
Scale – From personal AI to organisational AI: why copilots plateau, and what an AI operating system changes
13 Oct 2026, 09:00
Part 4 of the Sovereign AI series, for CIOs, COOs and the people who own processes and AI Centres of Excellence — in any sector. Personal AI raises the productivity of a person; organisational AI changes the outcome of a process. The gap is not a better model but four missing things: shared context (an ontology, not each person's chat history), governed action (agents that act on systems under policy), identity and permissions that follow the work across teams, and evidence a board or regulator will accept. A live contrast between a personal assistant and the AI OS on the same question, then one end-to-end process run by agents with people in the loop.
Partners
