Bank-side governance for generated content.
BankingX40 is independent of the AI model, vendor, or platform that generated the output. It can govern outputs from internal bank AI systems, third-party RegTech providers, compliance copilots, or agentic workflow tools.
The institution defines the workflow boundary. BankingX40 applies the boundary consistently and preserves the evidence trail.
Any output source
Internal model, vendor platform, compliance copilot, agentic workflow, or pilot AI system.
Boundary defined by institution
The reviewing institution defines the governance boundary and receives the decision and evidence trail.
Replayable output governance
Reason codes, replay evidence, audit export, SHA/hash attestation, and governance report.
Assess vendor AI outputs under the institution’s own boundary.
BankingX40 can be used to assess AI-generated outputs from a RegTech platform or third-party vendor before adoption, expansion, or sensitive workflow entry. The assessment does not rank vendors as models. It tests output admissibility under the institution’s governance boundary.
Test generated AML/KYC content
Assess vendor-generated notes, rationales, case summaries, and recommendations.
Test bank AI workflows
Assess internal copilots, LLM pilots, and agentic compliance workflows.
Release, verify, or block
Determine whether outputs are admissible before entering sensitive use.
BankingX40 governs the generated output, not the customer.
It does not decide the customer, the account, the AML case, or the institution’s final policy decision. It determines whether an AI-generated workflow output should be released, verified, or blocked under the active governance contract.
| BankingX40 controls | BankingX40 does not decide |
|---|---|
| AI-generated AML/KYC output | Customer identity judgment |
| Output release state | Customer approval decision |
| Workflow-entry control | Bank policy ownership |
| Evidence for review | External certification claim |
The model generates. BankingX40 governs.
BankingX40 is not another AI model judging the first model. It converts generated content into a canonical governance state, applies deterministic controls, evaluates QEIv18 structural boundary metrics, and returns an output-control decision.
AI output
Generated triage note, rationale, or workflow recommendation.
Canonical state
Output mapped into the governance contract.
Controls
Deterministic contract controls applied.
QEIv18 metrics
Structural boundary metrics evaluated.
Decision
RELEASE / REQUIRE_VERIFICATION / BLOCK.
Evidence
Replay / audit / SHA / governance report.
Each assessment runs against a defined governance contract.
The contract determines how outputs are mapped, evaluated, routed, and evidenced. This makes the assessment repeatable and independent of the output source.
Release, verify, or block
RELEASE / REQUIRE_VERIFICATION / BLOCK under the active governance contract.
Replayable governance report
Reason codes, replay evidence, audit export, SHA / hash attestation, governance report, and QEIv18 decision deltas.