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Cloud-native core
A modern real-time ledger, product engine and API layer designed for elastic cloud deployment — not a screen-scrape over a legacy mainframe and not a workflow tool stitched on top of someone else’s core.
Platform / Agentic AI Cloud Core Banking
Agentic AI Cloud Core Banking
The real-time banking core, lifecycle workflows and governed AI control plane for regulated financial institutions. CoreFi runs accounts, payments, lending and onboarding on a cloud-native core — and lets AI agents operate those workflows under permissions, policy gates, audit trails and human approvals.
Category definition
What is Agentic AI Cloud Core Banking?
Agentic AI Cloud Core Banking is a new category of banking platform that combines three layers in one product: a cloud-native real-time core, end-to-end lifecycle workflows for onboarding, lending and servicing, and a governed AI control plane that lets agents run those workflows under enforceable banking controls.
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Lifecycle workflows
Onboarding, lending, servicing, operations and compliance are first-class workflows inside the platform — not afterthoughts bolted on through third-party orchestration engines.
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Governed AI control plane
AI agents act as operators of those workflows through scoped APIs, policy gates and human approval queues. The model proposes; CoreFi enforces; the audit log keeps one record per case.
The gap
Why legacy cores are not ready for AI agents.
Most banking cores were never designed to be operated by a non-human caller. Adding an AI agent on top of them creates three structural problems no model can solve on its own.
No scoped operator identity
Legacy cores expose batch jobs and operator screens, not fine-grained APIs with role, scope and limit per caller. An agent either gets too much access or none at all.
No enforceable policy layer
Risk thresholds, segment rules, jurisdictional limits and approval routings sit in tribal knowledge, spreadsheets and committee minutes — not in a runtime that an agent must pass through before any side effect on the ledger.
No case-level audit
Audit trails are transaction-level, not case-level. There is no single record that ties a prompt, the retrieved data, the model output, the policy decision and the resulting API calls to one customer case — the format supervisory review increasingly expects.
Platform overview
Four pillars in one platform.
CoreFi is delivered as one product with four functional pillars. They share the same data model, the same identity and permission model, and the same governed control plane — so an agent that opens an account, decides a loan and resolves a service ticket does it inside one platform, not three.
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Core Banking
Cloud-native real-time ledger, accounts, wallets, product engine, payments and reporting. The system of record everything else posts into.
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Digital Onboarding
KYC, KYB, AML, document capture and consent for retail and business customers — the funnel an Onboarding Agent prepares cases inside, with policy gates before any account opens.
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Lending Automation
Origination, decisioning, servicing, restructuring and collections as one lifecycle — the workflow a Credit Agent operates in, with risk thresholds and human underwriter approval built in.
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AI Governance Layer
The control plane that wires permissions, policy gates, approval routing and audit across every pillar. Agents act through it — or they do not act at all.
How governed agents work
Seven steps from prompt to posted transaction.
Whether the agent is opening an account, deciding a loan, reconciling treasury or investigating a compliance alert, every CoreFi workflow follows the same governed lifecycle. The model proposes; CoreFi enforces; the human approves what the policy says they must.
Sense
The agent receives a banking trigger — a new applicant, an inbound payment, a customer message, a portfolio breach, a scheduled reconciliation — and pulls the relevant context from the core, KYC vendor, document store and ledger through permissioned APIs.
Plan
The agent drafts a structured action plan: which APIs it will call, which limits and rules apply, which evidence supports the decision. It is a machine-readable proposal, not free text.
Check
CoreFi runs the plan through policy gates before any side effect — role permissions, customer consent, transaction and exposure limits, AML and sanctions filters, model-output guardrails, jurisdiction rules. Failed checks stop the workflow.
Act
If checks pass, CoreFi executes the plan through the same APIs a human operator would call — open an account, post a journal entry, release a payment, update a case. Nothing bypasses the core.
Record
Every step — model context, retrieved data, plan, policy decision, API calls, outcomes — is written to a tamper-evident audit record keyed to the workflow, the customer and the model version. One record per case, designed to support supervisory review.
Escalate
When a policy gate routes a step to a human, CoreFi prepares the case in the reviewer dashboard with the evidence, the agent’s recommendation and the exact action awaiting approval. The human approves, rejects or edits — the workflow resumes from there.
Learn
Outcomes feed back into the workflow: which agent recommendations got accepted, which were overridden, which produced exceptions downstream. Risk and model owners see the signal — the model never gets uncontrolled write access to its own behaviour.
Example agents
Five agent roles already wired into the core.
Each role uses the same seven-step lifecycle. The difference is which APIs it can call, which policies apply and which humans approve.
Credit Agent
Runs credit applications end-to-end: pulls bureau and open-banking data, classifies bank statements, drafts the underwriting memo, proposes a limit and pricing, prepares the offer document.
Approval gate: every credit decision above policy thresholds requires a human underwriter sign-off before the offer is issued.
Onboarding Agent
Triages KYC and KYB intake: parses ID documents, extracts beneficial owners, runs sanctions and PEP screens, scores risk indicators and prepares a structured review packet for the queue.
Approval gate: any high-risk classification, edge-case document or sanctions hit routes to a human reviewer.
Service Agent
Handles inbound questions across chat, email and in-app. Performs read-only actions itself (statements, balances, status) and prepares ticketed write actions for an operator to release.
Approval gate: any monetary action — refund, reversal, fee waiver, limit change — requires a human agent to approve before it is posted.
Compliance Agent
Investigates AML alerts, transaction-monitoring hits and suspicious-activity cases. Pulls customer history, ranks indicators, drafts the case narrative and prepares the report template with evidence.
Approval gate: filing decisions, customer offboarding and high-severity case closures stay with the compliance officer.
Operations Agent
Watches exceptions, reconciliation breaks, payment queues and back-office tasks. Proposes resolutions, prepares end-of-day packs and routes high-impact items to the right desk.
Approval gate: any movement above defined limits or outside approved counterparties is held for operator approval.
Banking controls
The agent is treated like any other operator.
CoreFi is built with controls for agent operations so an AI workflow inherits the same scopes, limits, approvals and audit a human operator would face. The control plane is the platform — not a checklist on a slide.
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Permissions
Role-based access and scoped API tokens per agent, per workflow and per environment. An agent that can read balances cannot post a journal entry unless its role grants it.
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Policy gates
Transaction and exposure limits, customer-segment rules, jurisdictional restrictions and model-output filters — evaluated on every step before any side effect on the ledger. Failed checks stop the workflow.
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Approval routing
Workflows declare which steps require a human. CoreFi routes the case to the right reviewer dashboard with full evidence; the decision (approve, reject, edit) is logged and resumes the workflow.
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Audit trail
One tamper-evident record per workflow: trigger, retrieved data, model and prompt version, plan, policy outcome, API calls, side effects, escalations, human decisions, final state — exportable for internal, external and supervisory review.
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Model flexibility
Run governed workflows on ChatGPT, Claude, Gemini, your own hosted models, or a mix. The control plane, audit log and policy gates do not change when you swap the underlying model.
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Monitoring
Real-time dashboards on agent volumes, override rates, escalation queues, policy hits and model drift signals — with alerting hooks for risk and model owners.
Deployment paths
Cloud-native, with options for institutions that need more isolation.
CoreFi runs on the same platform across deployment models. The difference is who runs the infrastructure, where customer data lives and how much of the control plane the institution owns end-to-end.
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SaaS
CoreFi-operated multi-tenant cloud. Fastest go-live, shared upgrades, regional residency on request. Designed for fintechs, neobanks and digital lenders that want infrastructure without operations overhead.
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Private cloud
Single-tenant deployment in a CoreFi-managed cloud region. Dedicated database, dedicated network, dedicated upgrade window. Designed to support institutions with stricter isolation requirements.
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Dedicated / customer cloud
CoreFi deployed into the institution’s own cloud account (AWS, Azure, GCP) or a sovereign-cloud region. The institution controls the perimeter; CoreFi operates the platform on it. Available on request.
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Data residency
Customer data and audit records pinned to a chosen jurisdiction — EU, LATAM regions and others on request. Backups and processing follow the same residency boundary.
Put a governed banking agent into production.
We will walk you through the seven-step lifecycle on a live workflow, show the case-level audit record and scope a first deployment together — onboarding, credit or operations.