Regulatory scrutiny is rising
DORA, the EU AI Act, and national supervisors expect demonstrable control over AI systems, third-party concentration risk, and operational resilience.
EXECUTIVE BRIEF · FINANCIAL SERVICES
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.
Why it holds up in review
DORA aligned by architecture
In-perimeter data, models, and logs
Auditable every agent action logged
THE PRESSURE
What's forcing the decision?
Data cannot leave the bank
Customer PII, transaction data, and market-sensitive information are subject to residency, secrecy, and contractual constraints that hosted AI cannot satisfy.
Cost and lock-in concerns
Per-token AI pricing is hard to forecast for high-volume workflows, and single-vendor model lock-in is itself a concentration risk.
ICT concentration risk under DORA
External inference providers become ICT third-party dependencies subject to concentration-risk assessment, contractual resilience clauses, and exit planning. On-prem deployment removes that dependency class entirely.
WHY ON-PREM
The case for private deployment
On-Prem Private AI for Banking & Financial Services
On-premises deployment removes external inference as an ICT third-party dependency, keeps regulated data inside your control, and gives risk and audit teams a complete, inspectable trail for every AI action. With flat platform pricing — not per-token metering — high-volume banking workflows stay predictable. It is the most direct path to deploying AI in a way DORA and your supervisors will accept.
COMPLIANCE ANGLE
Mapped to your obligations
Removes third-party inference as an ICT concentration risk; full audit trail supports resilience testing.
AI inventory, risk classification, and human-oversight controls for high-risk use cases.
Data stays in-region and in-perimeter; supports minimization and erasure workflows.
RECOMMENDED FIRST WORKFLOWS
Where to start for fast payback
High-value, low-risk workflows that prove the platform and keep sensitive data inside your perimeter.
KYC / AML investigation support
Agents assemble case context from internal systems, summarize alerts, and draft investigation notes — with every source and step logged.
Regulatory and policy Q&A
Private retrieval over internal policy, regulation, and procedure documents so staff get grounded answers with citations, never invented ones.
Credit and risk memo drafting
Agents compile structured data and documents into first-draft memos that analysts review, cutting cycle time without ceding judgment.
Customer operations assist
Frontline and back-office staff get AI assistance grounded in your own knowledge base, under RBAC, with no customer data leaving the bank.
PROVEN PATTERNS
What similar organizations achieve
40–60% lower AI cost vs. metered cloud
−65% compliance reporting prep time
10× faster document processing
Proven in a Tier-1 retail bank
A European retail bank deployed on-prem KYC/AML investigation agents across 200 analysts. Case assembly time dropped while every retrieval source and model step remained in the audit pack supervisors requested under DORA operational-resilience testing.
Proven in an asset manager
An asset manager replaced hosted policy Q&A with private retrieval over internal compliance manuals. Analysts received cited answers only — no customer or position data left the perimeter — and model-risk teams retained full lineage for SR 11-7 documentation.
QUESTIONS
What leaders ask first
Does this satisfy DORA third-party risk requirements?
Running inference on-premises removes the external model provider as an ICT third-party dependency, which directly addresses concentration-risk concerns under DORA. Combined with full audit logging, it supports operational-resilience testing and reporting.
Can we keep customer data in-region?
Yes. All processing, retrieval, and model inference run inside your perimeter and can be pinned to a specific region or country, satisfying residency and banking-secrecy constraints.
How does on-prem AI compare to Azure OpenAI private endpoints for banking?
Private endpoints reduce public-internet exposure but the model service remains a cloud-managed third party. On-prem inference keeps the full data plane — prompts, embeddings, logs, and models — inside your ICT boundary, which is what DORA concentration-risk reviews increasingly expect for high-volume workflows.
Why is flat pricing important for financial services AI?
Banking workflows — KYC reviews, regulatory Q&A, customer operations — generate high query volumes. Per-token cloud pricing makes forecasting impossible and can itself become a material ICT cost risk. Flat on-prem platform pricing keeps spend predictable as adoption scales.
Which workflow should we start with?
Most banks begin with regulatory and policy Q&A or KYC/AML investigation support: high value, contained data scope, and an audit trail that satisfies risk teams before broader rollout.