Automating the Credit Memo: A Standardized Borrower Summary for Faster Decisions

I designed and built a working AI-First “North Star” prototype of “MemoMind” to demonstrate how AI can compress a full-day credit-memo workflow into a focused hour.

This prototype paints a clear picture of a near-term future where AI assembles a standardized, decision-ready credit memo in about an hour—so analysts can spend more time on judgment, not paperwork. It improves speed, consistency, and confidence while keeping humans firmly in the loop.

Where we are today

Sarah Chen, a Senior Credit Analyst, had three memos due by EOD with data split across spreadsheets, PDFs, and past memos. Manual extraction, formatting, and risk reviews made speed and consistency tough to achieve.

The “North Star” in one flow

MemoMind ingests the full document set—financial statements, bank statements, credit reports, and prior memos—then:

  1. Standardizes & extracts key ratios and financial signals,

  2. Selects the right industry template to ensure a consistent structure,

  3. Generates a first-draft narrative that explains the borrower story, risks, and recommendation,

  4. Surfaces flags & comparisons (peer benchmarks, anomalies, inconsistencies), and

  5. Hands control to the analyst to add meeting notes, accept/decline suggestions, and finalize.

Outcome from the working prototype: Sarah completed her first memo in just over an hour and finished all three before 3 PM—reducing manual data entry and errors while improving consistency and confidence.

How It’s AI-First

  • Language + Document Intelligence: NLP/LLM-powered extraction from PDFs and spreadsheets; named-entity recognition for counterparties, instruments, and covenants; OCR where needed.

  • Template Learning & Narrative Generation: Model-assisted selection of an industry memo template; auto-drafted sections (Borrower Overview, Financial Summary, Risk Factors, Recommendation) with citations back to source docs.

  • Risk Framework Standardization: Automated ratio calculations, threshold checks, and peer benchmarking to spotlight anomalies and trend breaks.

  • Inconsistency Detection: Cross-checks figures across sources; flags mismatches and missing data before they reach review.

  • Human-in-the-Loop Safeguards: Edit-everywhere workflow, rationale tooltips (“why we flagged this”), and a complete audit trail connecting every claim to its source.