A practical perspective from Arvelliq Systems.
The credibility problem
Credit decisions are consequential. A useful AI system cannot simply provide an answer; it must make the path to that answer visible. That means preserving the underlying language, the approved data, the financial logic, and the human review state.
Evidence is part of the product
When an extracted covenant is displayed without its source clause, a reviewer must reopen the document and reconstruct the work. Source-linked outputs reduce that gap and make validation part of the ordinary workflow.
Math should not be a narrative
Leverage, coverage, payment schedules, and borrowing-base availability should be calculated by tested, reproducible logic. A language model can explain an output, but it should not be the hidden calculator behind a material credit measure.
Human judgment stays visible
The system should record who approved an input, who overrode an exception, and what evidence informed a decision. AI can compress the path to understanding without erasing accountability.