Credit memo / loan underwriting automation

Credit memo / loan underwriting automation takes over the routine part of credit analysis — document collection, metric extraction, writing the memorandum draft. The analyst receives a ready draft with facts, metrics, and source references, and handles verification, adjustment, and decision-making instead of copying data from PDF to Word.

What automation does:

1. Accepts borrower documents — financial statements, tax returns, bank statements, registration documents, collateral documentation — via file storage or upload to the interface.
2. Recognizes and extracts structured data from scans and PDFs: revenue, EBITDA, assets, liabilities, cash flow, credit history data.
3. Normalizes metrics and consolidates them into a single format so that comparable data resides in the data warehouse or BI alongside historical analytics.
4. Calculates core ratios (DSCR, debt-to-equity, liquidity ratios) and flags deviations from internal credit policies.
5. Summarizes the borrower's financial condition: key trends over 2-3 years, risks, strengths, industry context.
6. Generates a credit memo draft based on the bank's internal template with sections «Borrower profile», «Financial analysis», «Risks», «Recommendation».
7. Attaches source references to each fact — document page, table cell, data warehouse entry.
8. Passes the draft to the credit analyst for review, adjustment, and final decision.

What automation does NOT do:

1. Does not make the final decision on loan issuance. The credit memo is a draft for a human, not a signature on a loan agreement.
2. Does not replace compliance, KYC/AML, and fraud checks. These processes remain with separate systems and bank specialists.
3. Does not work blindly with new document types. Non-standard forms, unfamiliar formats, or unsupported languages require additional training or manual fallback.

Credit documents are stored in file storage, where the agent-framework retrieves them for each new application. Data extraction works in conjunction with OCR and table parsing, normalized metrics go to a data warehouse or BI, and the LLM performs summarization of long sections and generates a memorandum draft using the bank's template.

Technical flow:

1. Application files are delivered to file storage (directory structure: application → borrower documents → collateral documentation → correspondence).
2. Agent-framework identifies the document set, distributes them among parsers, and initiates extraction.
3. The parsing layer extracts fields from financial reports and statements: revenue, EBITDA, assets, liabilities, cash flow, credit history data.
4. Normalized metrics are written to a data warehouse or BI — a dedicated mart for underwriting with links to source documents and pages.
5. The summarization module compresses long sections (explanatory notes to financial statements, audit reports, business descriptions) into concise points.
6. The draft generator assembles the credit memo text according to the internal template: borrower profile, financial analysis, risks, recommendation.
7. Each fact in the draft is linked to the source document and page, so the analyst can verify the statement in one click.
8. The draft goes to the review UI: the analyst edits, approves, or rejects, and all actions are logged for audit.

Implementation steps:

1. Define the scope: which credit products (corporate, SMB, mortgage, consumer) fall under automation, which document types, and which credit memo template to use.
2. Compile a library of reference memorandums from past periods so the AI agent understands the bank's structure, tone, and requirements.
3. Configure data extraction for specific borrower document formats and run accuracy tests on a representative sample.
4. Connect the data warehouse: align the table for normalized metrics and the link to existing scoring models and policies.
5. Configure generation: prompts for each section, source citation rules, and tone and length constraints.
6. Embed a human-in-the-loop UI — an interface where the analyst sees the draft, sources, and can edit before approval.
7. Run shadow mode for 2-4 weeks: the AI agent works in parallel with analysts, results are compared, and prompts and extraction are calibrated.
8. Train the credit department, align with risk and compliance, and move to production with a fixed logging policy.

Component diagram:

Data and access:

• File storage with borrower documents and a pre-agreed directory structure organized by applications.
• Data warehouse or BI with historical credit data for context and scoring.
• A library of reference credit memos — documents from prior periods as a style and structure reference.
• Access to the bank's internal credit memo template and credit policy descriptions.
• A list of key ratios and threshold values approved by the credit committee.

Team:

• Senior credit analyst — validates extraction logic, calibrates prompts, reviews drafts in shadow mode.
• AI engineer — configures the agent-framework, extraction, and integrations.
• Product owner from the credit department — maintains scope and documents decisions.
• Risk and compliance representative — approves the human-in-the-loop policy and audit trail.

Organizational requirements:

• Sign-off from compliance and risk before launching shadow mode.
• Human-in-the-loop policy: no credit decision is made without analyst sign-off.
• A logging process for AI agent and analyst actions for internal audit and regulatory purposes.

Timeline:

Standard implementation timeline — 6-10 weeks:

1. Weeks 1-2: scope, collection of reference memos, connecting file storage and data warehouse.
2. Weeks 3-5: field extraction, summarization, draft generation, basic review UI.
3. Weeks 6-7: integration into the analyst workflow, prompt calibration, compliance sign-off.
4. Weeks 8-10: shadow mode, team training, production launch with logging policy.