Sovereign AI for Insurance
Underwriting, claims and reporting each run on their own view of the policyholder, and the AI that could join them up would have to see health, financial and behavioural data the regulator watches closely. The platform joins them on one governed data platform inside the insurer, with every model decision traceable.
One view of the policyholder
Policy, claims, actuarial and external data resolved into one insurance ontology, so pricing, acceptance and claims decisions share a basis.
Human where the law requires
Agents prepare the underwriting file or the claim; an underwriter or handler decides on the cases the rules reserve for a person, and the decision is recorded.
Fair and explainable
Every model decision is versioned and traceable to its data, so pricing and claims outcomes can be explained to the customer and the supervisor.
An insurer decides the same things thousands of times a day: whether to accept a risk, at what price, whether a claim is genuine, how much to reserve. Each decision depends on data spread across policy administration, claims, actuarial models and external sources, and each is subject to fairness, explainability and solvency rules. AI can prepare and speed those decisions, but only on the insurer's own infrastructure, with a human deciding where the law requires, and a record of why.
Read this if you're 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.
For insurers, Scrydon is the sovereign AI and data platform that unifies policy, claims, actuarial and external data in one insurance ontology, runs underwriting, claims and fraud models and agents on it inside the insurer's own infrastructure, and produces the model-governance, fairness and audit records DORA, GDPR, Solvency II and the EU AI Act require.
It runs on-premises, in a sovereign cloud or in the insurer's own tenancy, with open-weight models served locally and every model, dataset and agent action versioned, attributed and logged.
Thousands of decisions a day, on split data, under fairness rules
An insurer decides the same things thousands of times a day: whether to accept a risk and at what price, whether a claim is genuine and how much to pay, how much to reserve. Each decision depends on data that lives in policy administration, claims, actuarial models and external sources, and the file behind it is still assembled by hand. Genuine claims wait in the queue behind suspicious ones because triage is manual and fraud is found after payment.
The rules around those decisions are among the strictest in financial services. Health, financial and behavioural data are special-category data under GDPR; AI used in insurance pricing and claims is listed as high-risk under the EU AI Act, with fairness, explainability and human oversight obligations; Solvency II supervises model risk and reporting; and DORA makes dependence on a foreign black-box model an ICT third-party risk. AI that helps has to run inside the insurer, keep the human decision where the law puts it, and keep the reasoning.
The policyholder in pieces — Policy administration, claims, actuarial models and external data each hold part of the risk; the file is assembled by hand.
Claims by queue — Genuine claims wait behind suspicious ones because triage is manual and fraud detection runs after payment.
Special-category data — Health, financial and behavioural data under GDPR, and AI in insurance pricing and claims listed as high-risk under the EU AI Act.
Solvency and DORA — Model risk, reporting and ICT third-party risk are supervised; a foreign black-box model is a finding waiting to happen.
One insurance ontology, prepared decisions, human where it counts
The platform models the insurer in one insurance ontology: policyholders, policies, risks, claims, providers, reserves and the relationships between them, mapped from policy administration, claims and actuarial systems without migrating any of them. On that model, agents assemble the underwriting file or the claim, gather the evidence, check it against the rule and flag what needs a person, and fraud, triage and pricing models run on the insurer's own infrastructure with features drawn from the ontology.
The decision stays where the law puts it. An underwriter or claims handler decides on the cases the rules reserve for a person, Human+AI rather than automation, and the outcome, the model version and the reasoning are recorded together. Fairness and drift are monitored through AI observability, and solvency and conduct reports are drafted from the same governed data and checked by a person, so the supervisor sees one consistent story from data to decision to report.
- 1
Model
Policyholders, policies, risks, claims, providers and reserves modelled once, mapped from policy administration, claims and actuarial systems.
- 2
Prepare
Agents assemble the underwriting file or the claim, gather evidence, check it against the rule and flag what needs a person.
- 3
Score
Fraud, triage and pricing models run on the insurer's own infrastructure with features from the ontology, versioned and explainable.
- 4
Decide and report
The underwriter or handler decides; the outcome, the model version and the reasoning are recorded, and regulatory reports are drafted from the same model.
The insurer answers for the decision, so the insurer runs the model
The insurer answers for the decision, so the insurer runs the model. The platform runs on-premises, in a sovereign cloud or in the insurer's own tenancy, with open-weight models served locally and external models available only by explicit opt-in under policy. On shared infrastructure, confidential computing keeps health and financial data encrypted in use. Being model-agnostic, the platform lets the insurer 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 under AI governance; agents act under scoped identities; lineage links a decision to the data and concepts behind it. That record is the DORA, GDPR and EU AI Act evidence, and the basis of the solvency and conduct reports, produced by operating the platform rather than assembled for the supervisor.
Inside the insurer — On-premises, in a sovereign cloud or the insurer's own tenancy, with open-weight models served locally and external models by opt-in only.
Encrypted in use — Confidential computing keeps health and financial data encrypted while processed on shared infrastructure.
Model governance built in — Versioning, lineage, fairness monitoring and an immutable audit log for every model and agent.
Reporting from the model — Solvency and conduct reports drafted from the same governed data, checked by a person.
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 insurance
Underwriting, claims, fraud and reporting 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.
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.
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.
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 Insurance 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
An insurer's architect asks how models are governed and monitored, how the policyholder 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.
AI Governance
Policy, data-loss prevention and audit applied to every AI action, with the evidence an assessor can read.
Analytics
Self-service analytics over a governed semantic model, so numbers mean the same thing in every report.
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 Observability
Monitoring, tracing and evaluation for agents and AI workflows, so failures are diagnosable.
Frequently asked questions
Can AI decide on claims or pricing on its own?+
How are pricing and claims decisions kept fair and explainable?+
How does the platform support DORA and Solvency II?+
Can we detect claims fraud before payment?+
Is health data protected from the cloud operator?+
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 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
