Financial Services
Private AI that clears model-risk review.
Bank AI pilots stall where the data can't go to a public model — and where the new model-risk guidance leaves generative AI to govern itself. We put AI on your transaction and customer data inside your perimeter, with the agent identity and audit evidence your risk team needs to stand behind it.
Cloudera Premier Partner · WSO2 Partner · banking & regulated delivery
The governance gate
Why bank AI pilots stall in 2026
The technology works in the demo. It dies in review — for four reasons, one of which only landed this spring.
Public models are off-limits
Customer records, transactions, MNPI — none of it can go to a consumer LLM. Staff route around the rules anyway; shadow AI is already inside the bank.
The model-risk ground just moved
April 2026's revised interagency guidance replaced SR 11-7 — and put generative and agentic AI explicitly outside its scope. There's no examiner template; the bank has to govern it itself.
Agentic risk cascades
One unattributed or compromised agent can amplify across workflows. Without per-agent identity and an audit trail, there’s no containment and no chain of custody.
Shift left, or stall
The new guidance pushes controls to the start of the model lifecycle. Pilots that bolt governance on at the end die in validation.
The approach
Two problems, one perimeter
Keeping the data in is half the job. Governing how AI reaches and acts on it is the other half. We deliver both.
The data never leaves
Model-agnostic AI running on your transaction, customer and risk data inside your own environment — Cloudera AI with NVIDIA NIM in-perimeter. No egress, no public endpoint, residency intact.
Governed access, both directions
The governed front door that gives every agent its own identity and an audit trail of who touched which account — and the governed API layer that controls how AI reaches your core, payments and customer systems.
access points
What we deliver
From stalled pilot to production
Start with the low-risk read; grow into the flagship build — and, only if it's the right fit, optional managed operation. You own the result either way.
Every workload we take toward production is read against three dimensions. That read — not the pilot result — is what separates a demo that impresses from a system you'd actually deploy.
AI governance review
A fast, fixed-scope read of your AI workload — the value it creates, the burden it must demonstrably honor, and whether it holds up in production rather than just passing the pilot. An architecture decision, not a compliance opinion.
Private AI on Cloudera
The flagship build — AI on transaction and customer data, in-perimeter, model-agnostic.
Governed account access
WSO2 agent identity, API governance and audit evidence — answer "which agent touched which account, under whose authority."
AML & KYC intelligence
Investigator copilots and screening intelligence on your own data — alert triage, narrative drafting, network views.
Explore the KYC/AML practicePOC rescue & production readiness
The pilot passes but you wouldn’t approve the deployment. We read it against outcome, compliance and surety and produce the design that says go, no-go, or go-if — and names what production actually requires.
Not every workload is a decision
The heaviest model-risk scrutiny falls on models that drive decisions — credit, fraud, AML disposition. But much of the highest-value bank AI decides nothing. Common patterns we see, grouped by the governance weight they carry:
LOWER SCRUTINY · THE ON-RAMPS
Decide nothing — the clean place to start
- Regulatory & board-reporting drafting from your own filings and data
- Document intelligence — credit memos, loan files, contracts, prospectuses
- Reconciliation & break investigation
- Complaint & surveillance summarization
- Call-center & ops copilots over internal knowledge
HIGHER SCRUTINY · DECISIONING
Full model-risk treatment — designed for the evidence from the start
- AML/KYC alert triage & disposition — our established practice
- Fraud & transaction-risk scoring
- Credit & underwriting assistance
- Trade surveillance & market-abuse detection
Building AI for banks as a vendor? We make yours the product that survives model-risk and vendor review.
Operated by us — owned by you
Plenty of clients run the system themselves once it's built — that's a complete delivery, full stop. For those who'd rather not staff the operation, we can run it inside your perimeter, under your governance. Nothing leaves; nothing changes hands. The data and the system stay yours, and you can bring operations in-house whenever you choose. Sovereignty isn't who staffs the run — it's whose walls it runs inside.
When a managed build is the goal, we architect the application to be operated from the first design decision. The same properties that earn a workload its go — projected quietness, and durability as data, rules and models change — are what make it cleanly operable. Build-to-operate means the Run tier is designed in, not bolted on.
Depth where it's regulated
Compliance and model risk have their owners — your risk, legal and validation teams. We design the architecture that makes their posture demonstrable and durable in production. We're integrators, not counsel.
Start with a governance review.
Fixed scope, low risk, no platform commitment. The fastest way to find out what stands between your pilot and production.