Credit memo / loan underwriting automation

Credit memo / loan underwriting automation takes over the routine part of credit analysis — collecting documents, extracting metrics, and drafting the memorandum. The analyst receives a ready draft with facts, metrics, and source references, and focuses on review, correction, and decision-making rather than 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 brings them to a unified format so that comparable data sits in the data warehouse or BI alongside historical analytics.
4. Calculates key 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, and industry context.
6. Generates a draft credit memo based on the bank's internal template with sections «Borrower profile», «Financial analysis», «Risks», «Recommendation».
7. Attaches source references to every fact — document page, table cell, data warehouse record.
8. Passes the draft to the credit analyst for review, correction, and final decision.

What automation does NOT do:

1. Does not make the final decision on loan approval. 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 the bank's separate systems and specialists.
3. Does not work blindly with new document types. Non-standard forms, unusual formats, or unsupported languages require additional training or manual fallback.

Credit documents are stored in file storage, from which 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 draft memorandum based on the bank's template.

Technical flow:

1. Application files arrive in file storage (directory structure: application → borrower documents → collateral documentation → correspondence).
2. Agent-framework determines the document composition, distributes them among parsers, and initiates extraction.
3. The parsing layer extracts fields from financial statements and reports: revenue, EBITDA, assets, liabilities, cash flow, credit history data.
4. Normalized metrics are written to a data warehouse or BI — a dedicated data 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 brief summaries.
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 enters 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, what document types and which credit memo template to use.
2. Collect a base of reference memorandums from previous 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 integration with existing scoring models and policies.
5. Configure generation: prompts for each section, source citation rules, tone and length constraints.
6. Integrate a human-in-the-loop UI — an interface where the analyst can see the draft, source documents, and make edits before approval.
7. Run shadow mode for 2-4 weeks: the AI agent operates in parallel with analysts, results are compared, prompts and extraction are calibrated.
8. Train the credit department, align with risk and compliance, move to production with a fixed logging policy.

Component diagram:

Data and access:

• File storage with borrower documents and a pre-agreed directory structure for applications.
• Data warehouse or BI with historical credit data for context and scoring.
• A reference credit memo base — documents from prior periods as a style and structure reference.
• Access to the bank's internal credit memo template and credit policy documentation.
• 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 records decisions.
• Risk and compliance representative — approves the human-in-the-loop policy and audit trail.

Organizational requirements:

• Alignment with compliance and risk before launching shadow mode.
• Human-in-the-loop policy: no credit decision is made without the analyst's 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 alignment.
4. Weeks 8-10: shadow mode, team training, production launch with a logging policy.