Sovereign AI for Banking
Fraud, financial crime, credit and customer teams sit on more data than ever and still decide too slowly, because the signals are scattered across core systems and the cloud AI that promises to help is what DORA and your supervisor will not accept. The platform runs it on one governed data platform, inside the bank.
One view of customer and transaction
Core, card, onboarding and case data resolved into one financial ontology, so a score, a case or a decision has one consistent basis.
Real-time, inside the bank
Fraud and financial-crime models run on streaming and historical data on the bank's own infrastructure; nothing leaves in a prompt.
Every decision traceable
Models, datasets and agent actions are versioned and attributed, so a declined payment or a filed report can be explained to the customer and the supervisor.
A bank's decisions are only as good as its view of the customer and the transaction, and that view is split across the core, the card system, onboarding, the case management tool and a data warehouse that is always a day behind. AI can score, detect and prepare decisions in real time, but a bank cannot send its customers' data to an outside model and cannot explain a decision it did not control. The platform gives the bank both: real-time AI on its own data, and a traceable record of every model decision.
Read this if you're 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.
For banks, Scrydon is the sovereign AI and data platform that unifies core, card, onboarding and case data in one financial ontology, runs real-time fraud, financial crime and credit models and agents on it inside the bank's own infrastructure, and produces the model-governance and audit records DORA, GDPR and the EU AI Act require.
It runs on-premises, in a sovereign cloud or in the bank's own tenancy, with open-weight models served locally and every model, dataset and agent action versioned, attributed and logged.
The signals exist; the view does not, and the regulator wants the reasoning
A bank's fraud, financial-crime, credit and customer teams sit on more data than ever and still decide too slowly. The customer exists in six systems, the core, the card platform, onboarding, the CRM, case management and a warehouse that is always a day behind, and an analyst reconciles them by hand before a decision can be taken. Batch scoring and rule engines find yesterday's fraud; real-time detection needs streaming data and models that run where the transaction is.
The regulator sets the other constraint. Under DORA, dependence on a single foreign AI provider is an ICT third-party and concentration risk the supervisor will ask about, and a declined loan, a blocked payment or a filed suspicious-activity report has to be explained to the customer and the authority, which a black-box cloud model cannot do. So the bank needs AI that runs on its own infrastructure, on its own data, with the reasoning kept.
A customer in six systems — Core, card, onboarding, CRM, case management and the warehouse each hold a piece. Fraud and financial-crime analysts reconcile them by hand.
Detection after the fact — Batch scoring and rule engines find yesterday's fraud. Real-time needs streaming data and models that run where the transaction is.
DORA and concentration risk — Depending on a single foreign AI provider is an ICT third-party risk the supervisor will ask about.
Explainability by law — A declined loan or a filed suspicious-activity report has to be explained; a black-box cloud model cannot.
One financial ontology, real-time models, governed agents
The platform models the bank in one financial ontology: customers, accounts, transactions, counterparties, products, cases and the relationships between them, mapped from the core and the surrounding systems without migrating any of them. On that model, fraud, financial-crime and credit models run on streaming and historical data through the platform's analytics layer, with features drawn from the ontology, so a score has the same basis as the case it triggers.
Agents take the work around the decision. They assemble a case, link entities across accounts and counterparties, keep memory across a multi-week investigation and cite every source, and decision intelligence turns a flagged signal into a recommended action routed through the bank's approval workflow. A person approves the decision or the filing. Models, datasets and agent actions are versioned, attributed and logged under AI governance, which is what lets the bank explain any outcome to the customer and the supervisor.
- 1
Model
Customers, accounts, transactions, counterparties, products and cases modelled once, mapped from the core and the surrounding systems.
- 2
Score
Fraud, financial-crime and credit models run on streaming and historical data on the bank's own infrastructure, with features from the ontology.
- 3
Investigate
Agents assemble cases, link entities across accounts and keep memory across a multi-week investigation, citing every source.
- 4
Decide and record
A person approves the decision or the filing; models, data and agent actions are versioned and logged for the supervisor.
Resilience and control are what DORA asks for
Resilience and control are what DORA asks for, and both come from where the platform runs and how it is governed. It runs on-premises, in a sovereign cloud or in the bank's own tenancy, with open-weight models served locally and external models available only by explicit opt-in under policy. Where cloud infrastructure is used, confidential computing keeps customer data encrypted in use, so the operator cannot read it. Because the platform is model-agnostic, the bank can swap models without re-architecting, which is the practical answer to concentration risk.
Governance is built into the way it runs. Every model, dataset and agent action is versioned, attributed and logged; agents act under scoped identities; and lineage links a decision to the data and concepts behind it. That record is the DORA, GDPR and EU AI Act evidence, produced by operating the platform rather than assembled for the supervisor's visit.
Inside the bank — On-premises, in a sovereign cloud or the bank's own tenancy, with open-weight models served locally and external models by opt-in only.
Encrypted in use — Confidential computing keeps customer data encrypted while processed, so a cloud operator cannot read it.
Model governance built in — Versioning, attribution, lineage and an immutable audit log for every model and agent, as DORA and the EU AI Act expect.
No lock-in — Model-agnostic and open-weight, so the bank swaps models without re-architecting and reduces concentration risk.
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 banking
Fraud, financial crime, credit and customer data on one sovereign platform.
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.
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.
Shadow AI Discovery for EU AI Act Readiness
Compliance & Risk
Challenge
Individual teams have quietly adopted their own AI tools and agents, so the organisation cannot produce a complete inventory ahead of EU AI Act high-risk obligations.
Solution
Continuous discovery surfaces every agent and AI tool in use across departments, classifying each against EU AI Act risk categories and routing unsanctioned use into governed alternatives.
Read more
A complete, defensible AI inventory and a measurable reduction in unauthorised tool use ahead of the 2 December 2027 enforcement deadline.
Persistent Memory for AML Investigations
Compliance
Challenge
AML investigations span weeks and multiple analysts, but each new session starts from scratch because the investigating agent has no memory of prior findings.
Solution
An entity-grounded memory layer keeps the full history of an investigation — evidence reviewed, hypotheses ruled out, related cases — so any analyst or agent picking it up resumes with full context.
Read more
Investigations close faster with no duplicated work, and a complete, auditable reasoning trail for regulators.
Coordinated Human-Agent Loan Underwriting
Underwriting
Challenge
An underwriter's final decision depends on analysis an agent can assemble in seconds, but today that analysis and the underwriter's decision live in separate systems, so the handoff itself takes longer than either step.
Solution
The AI OS coordinates the underwriting process end to end: agents assemble financials, risk signals and policy checks, present them at the point of decision, and carry the underwriter's verdict back into the system of record automatically.
Read more
Underwriting decisions move from application to verdict in a single coordinated flow, with a complete record of what the agent found and what the human decided.
Ontology-Based Single Customer View
Customer Data
Challenge
The same customer exists as slightly different records in core banking, CRM and the mortgage system, so building one accurate view of a relationship means manually reconciling accounts that don't obviously belong together.
Solution
An ontology resolves customer, account and product records from every system into one entity graph, so a single customer means the same thing across the whole bank.
Read more
Relationship managers and agents work from one accurate customer view instead of stitching records together by hand.
What Banking 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 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
What these outcomes actually run on
A bank's architect asks how models are governed, how the customer is modelled and where the data is encrypted. These are the pages that answer those questions.
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.
Frequently asked questions
Can we run real-time fraud and financial-crime detection on the platform?+
How does the platform support DORA?+
How are model decisions explained to a customer or the supervisor?+
Does customer data stay protected from the cloud operator?+
We have copilots in pilot. How do we move to bank-wide, governed AI?+
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.
Insurance
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 pageCapital Markets
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.
Read the page
Partners
