Finance: Compliance & Risk¶
Case study → finance
Compliance by design: decisions that can’t “talk their way around” policy.
Financial decisions are not just predictions. They are governed actions. We make policy enforcement deterministic and produce traces suitable for audit.
The question
Can AI assist credit decisions without violating policy, sector restrictions, or regulatory expectations?
Failure mode to avoid
Footnote exceptions
Important clauses live in appendices, edge cases, and “only if…” conditions.
Cross-document constraints
Policies and risk rules are distributed across multiple sources and versions.
Sector prohibitions
Some decisions are disallowed regardless of narrative quality.
Silent uncertainty
Fluent text can hide missing evidence. The system must be able to refuse.
What changes with governance constraints
Instead of “asking the model to behave”, we enforce constraints at the system level.
Every approval or rejection points to the rule and the evidence that triggered it.
flowchart TB;
Q["Proposed decision"] --> V["Validate constraints"];
V -->|"Pass"| OK["Approve with trace"];
V -->|"Fail"| NO["Reject with rule + evidence"];Diagram: constraint taxonomy (examples)
flowchart LR;
P["Policies"] --> R1["Role restrictions"];
P --> R2["Sector prohibitions"];
P --> R3["Evidence requirements"];
P --> R4["Threshold limits"];
R3 --> C["Citations + provenance"];Outputs
Rule IDs + rationale
Every decision is tied to a specific rule and its evaluated inputs.
Non-bypassable governance
Constraints are enforced in code/data, not in prompt text.
Audit-ready trail
Traces and validation reports suitable for internal review.
Deterministic abstention
When evidence is missing, the system refuses and states what is required.