Credit Memo
Review
Move from reading every page manually to reviewing prioritized, evidence-backed issues.
Key capabilities
What reviewers get
Prioritized issues instead of page-by-page reading
Every flag linked to its source passage
Consistency gaps surfaced across sections
A defensible, audit-ready review trail
Operating principle
AI proposes. Deterministic services calculate. Source documents prove. Humans decide.
How it works
A grounded pipeline that keeps calculation, policy, and human judgment clearly separated.
Ingest source documents
Credit memos, financial statements, and supporting files are parsed into structured data with full source provenance preserved.
Calculate deterministically
A deterministic calculation engine computes figures and comparisons outside the language model, so numbers are reproducible and auditable.
Reason with grounded AI
AI proposes findings only from extracted evidence and the applicable, effective-dated policy — never from unverifiable assumptions.
Route to the reviewer
Every finding arrives with its source citation and severity so a human reviewer can confirm, adjust, or dismiss it — and decide.
An illustrative finding
IllustrativeA fictional example showing how a single issue flows from source data to a reviewer decision. Figures are illustrative only.
Scenario
A credit memo states the borrower maintains a current ratio of 1.4x, while the attached balance sheet shows different figures.
Extracted data
Memo narrative: current ratio 1.4x. Balance sheet: current assets $8.2M, current liabilities $7.1M.
Deterministic check
Calculation engine computes current ratio = 8.2 / 7.1 = 1.15x from the source statement.
Applicable policy
Commercial Lending Policy requires memo figures to reconcile to source financials within tolerance.
AI finding
AI proposes a reconciliation exception: narrative ratio (1.4x) does not match computed ratio (1.15x).
Reviewer action
Reviewer opens the cited balance sheet, confirms the discrepancy, and returns the memo for correction.