AI in banking only works when the foundation does.
Approve more borrowers and default less. Detect fraud before losses occur. Prioritise collections where it actually moves the needle. Trigger the next product offer at the moment of highest intent. These outcomes are possible because Oradian's core was built API-first, with open data access, a programmable extension framework, and a native MCP foundation, the prerequisites AI in banking actually depends on.
The stack
AI & Automation
Agents, decisioning, autonomous workflows
API-first foundation
Every capability, stable & versioned
Open data access
Full export & real-time streams
Programmable core
Controlled extension framework
Proof points · AI-Native Core
The foundation, measured in production.
99.98%
Platform uptime
12,000/s
Journal entries processed per second
8,800/s
Transactions processed per second
+648%
Loan portfolio growth on Oradian in 12 months
Salmon
Why AI works on Oradian today and doesn't work on most cores.
AI in banking is not a feature you switch on. It runs on infrastructure. It depends on data that is accessible, current and complete; APIs that expose every function in real time; a programmable extension framework where new logic ships safely; and a native MCP surface so agents can compose against the platform without forking it.
Open data access
A structured data model with full export and streaming. Every transaction, every customer event, every risk signal, queryable, streamable, exportable. The data foundation AI actually needs.
API-first architecture
Every capability exposed through stable, versioned APIs. Models and agents call the core to retrieve data, check eligibility, trigger disbursements and post decisions back, in real time, at scale.
AI generates configuration, not workflows in the core
An AI agent trained on our platform, documentation and extension patterns helps your team author governed product configuration and Custom Code. AI accelerates how you build on the core. The core itself stays deterministic, versioned and explainable.
Native MCP foundation
The Model Context Protocol layer lets your internal agents and partner tools compose against Oradian safely. Discover capabilities, retrieve data and trigger actions through the same governed API surface that runs the bank.
Governed by design
Every model interaction, extension and integration is role-scoped, versioned and auditable. When regulators ask how decisions get made on your platform, you have a defensible answer.
Independent of your vendor
Programmable core, controlled extension framework and open data mean your team builds, customises and ships independently. AI capabilities compound because your team owns the cycle.
AI use cases that work because the foundation is right.
Real-time fraud detection
AI fraud models trained on complete transaction histories, device fingerprints and behavioural signals catch what rules miss. Real-time event streams from the core, instant alerts, continuous retraining from confirmed cases. Detection gets better over time.
Churn prediction and retention
Identify customers likely to go inactive 30, 60 days before it happens by analysing login patterns, transaction frequency, product usage and support contacts. Models run against Database Access and trigger targeted interventions through the messaging module at the moment of highest impact.
Collections prioritisation
Distinguish borrowers likely to self-cure from those needing proactive outreach and prioritise collections effort accordingly. Logic runs against real-time portfolio data and feeds directly into collections workflows.
Personalised product recommendations
When a borrower has repaid three loans on time, or a depositor's balance is growing, or an SME has a predictable monthly cash flow gap, that is an embedded credit or savings opportunity. AI models trigger contextual offers through the messaging module.
Database Access: the governed data layer your AI models depend on.
Running AI models directly against a production core banking database creates a problem: analytical workloads compete with the transaction processing workloads customers depend on. Under peak load, something has to give.
Database Access is Oradian's solution. A secure, read-only replica of the production PostgreSQL database, continuously synced, stored on your own infrastructure, available for analytics and AI teams to query freely, without creating any load on the live system.
- ›Fraud models always see current transaction data, not last night's export
- ›Credit engines train on complete 24-month histories without a data engineering project
- ›Compliance can query any customer's complete history in minutes, not days
- ›Product teams run experiments on real data without risking production
- ›Every model operates on a single, governed source of truth, explainable and auditable
The technical architecture behind our AI-native core.
Event-driven core
Every transaction, every status change, every customer event is processed and published as an event in real time. AI systems subscribe to event streams and receive data as it happens, not on a polling cycle.
Programmable extension framework + MCP
Add custom logic safely through the controlled extension framework, governed, versioned and auditable. Expose those capabilities to internal agents and partner tools through the Model Context Protocol, on the same API surface that runs the bank. Our AI agent helps your team author the configuration and Custom Code that ship into it.
Open APIs for AI integration
AI services, in-house, third party, or open-source, connect through the same stable, versioned API layer as everything else. Retrieve data, post decisions, trigger disbursements, update records, through APIs designed for production workloads.
Backwards-compatible releases
AI integrations you build today continue working as the platform evolves. We don't break production integrations with new releases. Your team builds with confidence.
Put your AI roadmap on a foundation that can carry it.
API-first, open data, programmable core, the prerequisites AI in banking actually depends on. Already in production.
Plan your AI rollout