AI governance
AI built for
institutional scrutiny
CORE's AI is designed for the question every examiner will eventually ask: how did you reach that conclusion? Every generation is traceable. Every output is tied to human review. Every decision is reconstructable.
The regulatory reality
Three questions your AI needs to answer.
OCC, FDIC, and state regulators are developing formal AI examination guidance. SR 11-7 model risk principles are being applied to AI-generated analysis. The institutions getting ahead of this share one thing: their AI can explain itself. Most vendor AI cannot.
“Where did this conclusion come from?”
Most AI tools can't answer. The output exists. The reasoning doesn't.
CORE: CORE saves the exact source documents, evidence selection, and assembled prompt context with every generation. The conclusion traces to specific pages of specific files.
“Who reviewed this AI output?”
If AI touched a credit decision, there needs to be a human record — not just a policy that review happens.
CORE: Every AI output requires analyst sign-off before it enters a memo. The reviewer, their edits, and the approval timestamp are logged and immutable.
“What instructions was the AI given?”
Prompt engineering in black-box tools is invisible to institutions and examiners alike.
CORE: Every prompt layer — grounding rules, skills, depth settings, analyst notes — is visible to your team and reconstructable verbatim from the generation snapshot.
Architecture
12 coordinated layers. Not one prompt.
Every CORE generation draws from a structured stack of context — each layer visible to your team, each layer logged. The AI doesn't decide what to include. Your architecture does.
Generation snapshots
Reconstruct any decision.
Years later.
Every time CORE generates a memo section, the complete context is saved: every source document selected, every prompt layer active, the model version used, every edit made after generation, and the approval record.
Two years from now, when an examiner points to a specific paragraph in a credit memo and asks how that conclusion was reached — your team can pull the generation snapshot and show them, verbatim.
Most AI tools produce outputs with no record of how they were produced. That gap is survivable until an examiner finds it.
What every snapshot contains
- Which source documents were selected for this generation
- The complete prompt context, assembled verbatim
- Grounding rules and skills active at generation time
- Model version and generation parameters
- CFR citations used and their validation timestamps
- The analyst who initiated and reviewed the output
- All edits made after generation, with timestamps
- The approval record with role and timestamp
Human oversight
AI that answers to your analysts.
The AI drafts. The analyst decides. That's not a limitation — it's the architecture. Credit decisions are institutional, not automated. CORE gives your team the infrastructure to maintain that line clearly.
Regulatory context
The frameworks shaping what examiners ask.
AI governance in credit decisions isn't a future concern. The regulatory infrastructure is already in place. What's developing is how examiners apply it.
SR 11-7 extended to AI Federal bank regulators apply model risk management principles from SR 11-7 to AI and machine learning models, including credit analysis tools. Explainability and governance are core requirements.
OCC AI guidance The OCC has signaled that AI tools in credit decisions are subject to the same governance expectations as traditional credit models. Examiners are increasingly asking about oversight, testing, and documentation.
FDIC technology supervision FDIC technology supervision principles address AI tools that influence credit underwriting. The focus is on human oversight, documentation of the AI's role, and audit trails.
State-level AI lending rules Several states have enacted or proposed AI-in-lending regulations. The common thread: institutions must be able to explain AI-influenced decisions and demonstrate human accountability.
See how it holds up under scrutiny
30-minute walkthrough focused on the AI governance layer. We'll walk through a generation, a snapshot, and how your team would answer an examiner's questions.