Credit Quality
Assurance
Gain visibility into recurring policy issues, reviewer dispositions, and quality trends across the review portfolio.
Key capabilities
What reviewers get
Recurring issue patterns made visible
Disposition trends across reviewers
Findings grouped by category and severity
A portfolio view for QA and oversight
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 QA lead wants to know which policy issues recur most often across recent reviews. (Illustrative figures.)
Extracted data
Findings from recent reviews are categorized by policy area and disposition (confirmed, adjusted, dismissed).
Deterministic check
Aggregation counts findings per category and computes recurrence rates across the sample set.
Applicable policy
Each finding is tied to the specific policy clause it relates to, preserving traceability.
AI finding
AI summarizes that reconciliation exceptions are the most frequent recurring category in the sample.
Reviewer action
QA lead uses the illustrative trend to target reviewer guidance — informed by evidence, not by a black box.