Customer & identity fabric
CRM, onboarding portals, KYC registry, beneficial-owner records, document vaults, eID providers.
Banking Agentic OS Control Plane
On-premise AI for banks: banking AI agents that turn objectives into governed execution
On-premise AI for banks runs the language models, retrieval indexes and agent audit trail on infrastructure the bank controls, so customer, transaction and credit data is never sent to an AI vendor's cloud. VDF.AI sits above the systems your bank already runs and coordinates specialized agents for KYC, AML, fraud, credit, servicing, reporting, reconciliation, and treasury. No core migration. No customer-data egress. Every action explainable at execution.
12Banking agents across front, middle & back office
0Core migrations required before rollout
100%On-premise or sovereign-cloud execution
EveryDecision logged with source, model & policy context
Built for banking control
EU AI Act GDPR DORA MiFID II Basel III Human-in-the-loop
Control plane architecture
Your banking systems stay. VDF.AI becomes the intelligence layer above them.
The control plane reads the live operating context, selects the right agents, applies the bank's autonomy and approval policy, and returns a ranked execution plan with evidence attached.
Live governed execution
Banking Agentic OS Control Plane
Objective intake · policy gates · agent routing · execution evidence · institutional memory
01 Context 02 Govern 03 Execute 04 Evidence
Bank systems and data fabrics
Transaction & financial-crime fabric
Cards, wires, payments, sanctions lists, AML cases, fraud signals, device and behavioral events.
Credit, collateral & servicing fabric
Origination systems, bureau data, collateral files, covenants, payment history, collections queues.
Regulatory, policy & risk fabric
Obligations, policies, model inventory, limits, risk appetite, regulator correspondence, control tests.
Operations & ledger fabric
Core balances, GL, reconciliations, exception queues, network disputes, audit evidence stores.
Objective intake
The bank states the outcome, SLA, risk appetite, product scope, and customer segment.
Policy and autonomy gates
RBAC, data class, approval rule, model route, confidence threshold, and escalation path are applied before execution.
Objective to agent execution plan
VDF.AI ranks the work by customer impact, risk, SLA, evidence completeness, confidence, and required human approval before activating agents.
Financial-crime operations
Identity checks, sanctions screening, AML narratives, fraud holds, investigator-ready case packs.
Credit and lending operations
Credit memos, policy checks, servicing actions, hardship prep, covenant and liquidity context.
Customer and wealth operations
Account servicing, dispute responses, suitability notes, portfolio commentary, advisor sign-off.
Regulatory and back-office operations
Control monitoring, report assembly, reconciliation reasoning, review workflows, sign-off trail.
Execution evidence ledger
Objective, source, retrieval, tool call, model route, policy check, approval, output, and disposition.
Institutional memory
Reusable typologies, credit positions, dispute evidence, regulator answers, and servicing patterns.
Banking outcomes
Faster customer onboarding
Completed KYC packs, exceptions routed, account-opening decisions explained.
Smarter fraud and dispute action
False positives reduced, suspicious activity escalated, chargeback evidence assembled.
Credit decisions with evidence
Underwriting memos, servicing options, policy checks, collateral and risk context.
Continuous compliance reporting
Obligations monitored, reports assembled from primary data, reviewer actions captured.
Daily risk and liquidity brief
Treasury, market, counterparty, reconciliation, and portfolio signals summarized for decision makers.
From objective to outcome
This is how banking operations transform
VDF.AI does not start with a generic process map. It starts with a measurable business objective and then turns that objective into governed, auditable agent execution.
- 1
Your systems stay. The control plane sits above them.
Connect core banking, CRM, KYC, AML, fraud, servicing, risk, and regulatory systems without migrating customer data or disrupting current operating teams.
Zero rip-and-replace - 2
State the business objective. The network builds the plan.
Ask for outcomes, not generic automation: reduce KYC onboarding time, raise AML monitoring coverage, cut loan-origination cost, or improve dispute cycle time while preserving risk controls.
Objective-first, compliant by design - 3
The right agents activate inside defined autonomy boundaries.
KYC, sanctions, fraud, credit, servicing, reporting, reconciliation, and treasury agents work with process-specific permissions, escalation rules, and human approval gates.
Staged autonomy, earned by workflow - 4
The platform acts, documents, and learns from outcomes.
Every action returns an audit trail, source-backed rationale, control evidence, and performance insight so operating knowledge compounds inside the institution.
Explainable at execution, auditable always
The Banking Challenge
Banks do not need more isolated copilots. They need an operating layer.
The value is not in one assistant drafting one answer. It is in coordinating regulated work across fragmented systems, preserving human accountability, and proving why every action was taken.
01
Systems Fragmentation
Core banking, KYC, AML, fraud, CRM, credit, servicing, and reporting platforms each hold part of the answer. Humans spend the day reconciling context by hand.
02
Regulated Autonomy
Banks can automate, but not blindly. KYC refresh, fraud blocking, credit memo drafting, and customer remediation each need different autonomy and approval boundaries.
03
Alert and Case Overload
Financial-crime, fraud, servicing, and operations teams are buried in alerts, exceptions, documents, and aging work queues while customer expectations keep rising.
04
Audit Evidence After the Fact
Too much AI governance is documented after execution. Banking AI needs evidence generated as work happens: source, rationale, policy check, approval, and disposition.
The VDF AI Solution
A banking control plane that governs agents by process, risk, and objective
No migration
Keep Finacle, Temenos, FIS, Finastra, Salesforce FSC, and the systems around them
The agentic layer connects what already works.
VDF.AI connects to core banking, KYC registries, AML platforms, fraud engines, document stores, CRM, reporting, and risk systems through governed tools. Data stays inside your bank's environment; agents receive only the scoped access needed for the objective.
Core replacement is a decade-long bet. A governed agentic control plane can start with one workflow and expand across the operating model.
0
Core Migrations Required
Control plane above existing systems
Core bankingKYC / AMLCRMRisk systems
Objective engine
Start with a target outcome, not a process-improvement wish list
The plan is ranked by value, risk, and control impact.
Examples of objective-first banking execution:
- KYC: reduce onboarding from weeks to days while preserving screening, documentation, and escalation controls
- AML: improve alert coverage and narrative quality without removing analyst accountability
- Credit: reduce origination cost while keeping final credit decisions with authorized officers
- Reporting: assemble regulatory packs from primary data with lineage, review status, and sign-off trail
- Operations: resolve breaks and exceptions with explanations supervisors and auditors can inspect
Goal
To Execution Plan
Business outcome to governed workflow
Ranked actionsRisk logicPolicy checks
Staged autonomy
Autonomy is governed by workflow, not assumed by platform
Each banking process gets the boundary it deserves.
VDF.AI lets risk, compliance, operations, and business owners define autonomy at the process level:
- Assistive: credit, suitability, and policy-adjacent decisions stay with a named human reviewer
- Delegated: KYC refresh, document chasing, reconciliation, and servicing prep can execute under policy
- Autonomous: fraud holds, queue routing, and low-risk operational actions can run within approved thresholds
- Escalated: exceptions, conflicts, low confidence, and policy ambiguity route to the accountable team
- Measured: each workflow reports throughput, risk impact, exceptions, cost, and control evidence
HITL
By Process Type
Assistive · delegated · autonomous
Approval gatesThresholdsEscalations
On-premise AI for banks
On-premise AI for banks: what stays inside the perimeter, and what reviewers will ask
On-premise AI for banks is a deployment in which the language models, the retrieval index, the agent orchestration and the audit log all run in the bank's own data centre or sovereign private cloud. Customer, transaction and credit data is processed where it already lives, and no AI vendor holds a copy of it.
| Question | What the rule says | What self-hosting changes |
|---|---|---|
| Data location | DORA Article 30(2)(b): ICT contracts must state the regions or countries where services are provided and data is processed and stored, with advance notice of any change | Inference, embeddings and logs run in data centres the bank already lists, so the vendor contract adds no new place where bank data is processed |
| Register and exit | DORA Article 28(3) register of information for every ICT service arrangement; Article 28(8) exit strategies for services that support critical or important functions | The platform vendor still goes in the register, but models, indexes and logs sit on bank hardware, so an exit means swapping software, not retrieving data |
| Audit and inspection | DORA Article 30(3): unrestricted access, inspection and audit rights for the bank, an appointed third party and the competent authority | Each agent run is recorded on bank systems with its objective, sources, tool calls, model route, approvals and output, ready for internal audit or a supervisor |
| Model risk | US: SR 26-2 (17 April 2026) replaced SR 11-7 and leaves generative and agentic AI to each bank's own governance. EU: AI that scores the creditworthiness of individuals is an Annex III high-risk use under the AI Act, with obligations from 2 December 2027 (see the EU AI Act guide for banks and insurers) | Model versions stay pinned and upgrades follow the bank's own change calendar, so validation happens before a new model reaches users |
| Access | Least privilege and segregation of duties under the bank's ICT risk framework | Role-based access through roles, permission groups and per-role tool grants; on-premises sign-in through built-in Microsoft Entra ID single sign-on, with other identity providers behind an SSO-aware reverse proxy |
DORA, Regulation (EU) 2022/2554, has applied since 17 January 2025. Sources: DORA on EUR-Lex, Federal Reserve SR 26-2, Regulation (EU) 2026/1744. The DORA executive brief gives the board-level view, and private GPT for banking covers a staff assistant on its own.
Where it pays back
Banking workflows ready for governed agents
Banking agent registry
Specialized agents for the banking operating model
Each agent has a defined job, inputs, autonomy boundary, outputs, and audit trail. Run one agent for a focused workflow or orchestrate several through VDF AI Networks.
Delegated with review
KYC Onboarding Agent
Coordinates identity, document, registry, beneficial-owner, and screening checks with exception routing.
Explore agent Analyst in the loopAML & Sanctions Agent
Triages alerts, drafts narratives, prepares SAR packages, and keeps analysts in control of filings.
Explore agent Autonomous within thresholdsFraud Operations Agent
Correlates real-time signals, suppresses false positives, and assembles investigator-ready cases.
Explore agent Case-worker assistedDisputes & Chargebacks Agent
Builds evidence packs, tracks network deadlines, and drafts customer-safe dispute responses.
Explore agent Assistive for decisionsCredit Underwriting Agent
Drafts credit memos from application, financial, collateral, policy, and risk context.
Explore agent Policy-bound delegationLoan Servicing Agent
Orchestrates modifications, hardship, collections, covenants, and servicing exceptions with empathy controls.
Explore agent Representative co-pilotCustomer Servicing Agent
Resolves retail, SMB, and wealth inquiries from account, policy, transaction, and case context.
Explore agent Advisor approvedWealth Advisory Agent
Accelerates client research, suitability notes, portfolio commentary, and advisor sign-off workflows.
Explore agent Reviewer controlledRegulatory Reporting Agent
Assembles reports from primary data with provenance, reviewer assignments, and sign-off history.
Explore agent Continuous oversightCompliance Monitoring Agent
Monitors communications, trades, transactions, and policy obligations continuously.
Explore agent Ops delegatedOperations Reconciliation Agent
Explains breaks, matches exceptions, routes unresolved items, and records audit-grade reasoning.
Explore agent Executive briefedTreasury & Risk Agent
Synthesizes liquidity, market, counterparty, and funding signals into daily decision briefs.
Explore agent Under the hood
Technical specifications for banking agent execution
| Requirement | VDF AI Capability |
|---|---|
| On-premise deployment | Full on-premises, private-cloud, sovereign-cloud, or air-gapped deployment options |
| Data sovereignty | Models, embeddings, customer data, transaction data, credit files, and case history remain inside your sovereignty and residency perimeter |
| System posture | Overlay architecture above existing core banking, AML, fraud, CRM, document, case, risk, and reporting systems |
| Private RAG | Policies, procedures, credit manuals, regulator correspondence, filings, disclosures, and operating playbooks stay in governed vector indexes |
| Role-based access | RBAC-scoped agents, tools, knowledge, and workflows aligned to segregation-of-duties and least-privilege controls |
| Model routing | Policy-aware routing by task sensitivity, data class, confidence need, latency, cost, and approved model inventory |
| Autonomy controls | Assistive, delegated, autonomous, and escalated modes configured per workflow, threshold, role, and approval policy |
| Audit logs | Immutable logs for objective, source data, retrieval, tool calls, model route, policy checks, approval, output, and disposition |
| Integration examples | Finacle, Temenos, FIS, Finastra, Salesforce FSC, KYC registries, AML/case APIs, card and payment streams, ISO 20022 and SWIFT-family messaging |
| Encryption | At-rest and in-transit, customer-managed keys |
| Authentication | Microsoft Entra ID SSO built in; Okta, Keycloak, SAML, or LDAP through your SSO proxy; MFA enforced by your IdP |
| Uptime SLA | 99.9% (Enterprise tier) |
ROI Snapshot
What changes after rollout
Days
KYC and onboarding cycles compressed by coordinated evidence gathering
Fewer
False positives through context-rich fraud and AML case preparation
Trace
Every objective, source, policy check, model route, approval, and outcome
Reuse
Institutional knowledge from credit, fraud, compliance, and servicing patterns
EXECUTIVE BRIEF · FINANCIAL SERVICES
On-prem & sovereign cloudDORA third-party risk removalCritical priority
Private AI for banking, without handing your data to a third party
Financial institutions are under pressure to deploy AI while satisfying DORA, GDPR, and internal risk controls. On-premises AI agents let you move fast on high-value workflows while keeping customer data, models, and audit trails inside your perimeter.
For CIOs, CISOs, Heads of Data, and Risk & Compliance leaders in banking and financial services.
- KYC / AML investigation supportAgents assemble case context from internal systems, summarize alerts, and draft investigation notes — with every source and step logged.
- Regulatory and policy Q&APrivate retrieval over internal policy, regulation, and procedure documents so staff get grounded answers with citations, never invented ones.
- Credit and risk memo draftingAgents compile structured data and documents into first-draft memos that analysts review, cutting cycle time without ceding judgment.
A strategic procurement brief for regulated financial services environments.
DORA-ready, on-prem AI for banks and financial institutions.
Private GPT
Private GPT guides for this sector
FAQ
Questions finance teams ask
Does VDF.AI replace our core banking platform?
No. VDF.AI sits above Finacle, Temenos, FIS, Finastra, Salesforce Financial Services Cloud, KYC registries, AML platforms, case-management tools, and reporting systems as an agentic control plane. The bank keeps its systems, data boundaries, approval policies, and cloud or on-premise environment. VDF.AI coordinates the work across them.
How does VDF.AI keep AI decisions explainable for banking regulators?
Every run records the stated objective, data sources consulted, tools called, model route, policy checks, human approvals, outputs, and final disposition. That gives compliance, model risk, audit, and supervisors a decision trail tied to the moment the action was taken, not a reconstructed explanation after the fact.
Can autonomy be limited differently for KYC, fraud, credit, and servicing?
Yes. VDF.AI supports staged autonomy by process. A KYC refresh can be delegated with exception handling, a fraud block can be autonomous within pre-approved thresholds, and credit-adjacent decisions can remain assistive with human sign-off. The autonomy boundary, approval rule, and evidence trail are defined per workflow.
Which banking workflows can be orchestrated first?
Common starting points include KYC onboarding, AML and sanctions alert triage, fraud operations, credit memo drafting, loan servicing, dispute handling, regulatory reporting, reconciliation, customer servicing, compliance monitoring, wealth advisory support, and treasury risk briefs.
What are the best on-premise AI agent platforms for banks?
Shortlist platforms on four bank-specific criteria: full on-premises or sovereign-cloud deployment with no vendor-side inference, per-action audit evidence a supervisor can replay, staged autonomy with human approval on customer-affecting decisions, and DORA-grade exit terms. VDF.AI is built against exactly this profile; IBM watsonx Orchestrate and self-hosted frameworks are the common alternatives, with frameworks shifting the security and audit burden onto the bank's own engineering.
Should a bank deploy agentic AI as SaaS or on-premise?
The deciding factor is data scope, not preference. Workflows touching customer PII, transaction data, or screening results favour on-premise or sovereign deployment — the data never crosses a vendor boundary, sub-processor chains stay short, and DORA third-party risk assessments simplify. SaaS agents remain workable for public-data workflows like regulatory-change monitoring. Most banks land on a hybrid with the sensitive path on-premise.
What should a regulated European bank require from on-prem AI operations?
Four operational guarantees: the full stack (models, retrieval, orchestration, logs) runs inside the bank's perimeter or EU sovereign cloud; every agent action produces immutable, exportable evidence for BaFin/ECB/EBA-style review; model updates follow the bank's own change-management process rather than vendor push; and the platform demonstrates a tested exit path — DORA treats an unexitable AI dependency as a concentration risk.
Can a bank use generative AI if customer data can't leave our data centre?
Yes, as long as the whole stack runs there. Host the language model, the embedding model, the vector index and the agent logs on the bank's own servers or sovereign private cloud, and allow requests only to models your policy approves. Customer, transaction and credit data then stays under the controls you already operate, and no AI vendor needs a copy. The work moves to GPU capacity, model evaluation and change control, which your platform team owns.
Does DORA apply to an AI platform the bank hosts itself?
Yes. DORA has applied since 17 January 2025, and a vendor that licenses and supports a self-hosted platform is usually still an ICT third-party service provider, so it belongs in the register of information. What changes is the risk. Data is processed in the bank's own locations, and models, indexes and logs stay on its hardware, so the exit strategy for a critical or important function is about replacing software rather than recovering data from a provider.
Does US model risk guidance cover LLMs and AI agents?
Not directly. SR 26-2, issued on 17 April 2026 by the Federal Reserve, OCC and FDIC, replaced SR 11-7 and places generative and agentic AI models outside its scope. It says a bank's own risk management and governance should set the controls for tools the guidance does not cover. Model risk teams therefore still expect an inventory, validation evidence, change control and output monitoring for language models, and self-hosting keeps all four on systems the bank controls.
Build the banking control plane above the systems you already trust
Start with one objective: KYC, AML, fraud, credit, servicing, reporting, reconciliation, or treasury risk.