LLM engineering Skills for underwriter in investment banking: What to Learn in 2026

AI is already changing underwriting in investment banking by compressing the time it takes to read deal docs, compare covenant language, summarize diligence materials, and flag inconsistencies across models, term sheets, and credit memos. The underwriter who only knows spreadsheet review is getting squeezed; the underwriter who can supervise LLM workflows around risk analysis, document extraction, and memo drafting becomes the person bankers rely on.

The 5 Skills That Matter Most

1. •

   Prompting for controlled financial analysis

   You do not need “creative prompting.” You need prompts that produce consistent outputs from long offering memoranda, credit agreements, and management presentations. Learn how to ask for structured outputs like risk summaries, covenant exceptions, downside cases, and open questions in a fixed template.

   For an underwriter in investment banking, this matters because your job is judgment under uncertainty. If the model can reliably extract the same fields every time, you can spend more time on credit calls and less on reading 80-page PDFs line by line.

2. •

   Document extraction and normalization

   Most underwriting work starts with messy documents: PDFs, scans, tables in images, and inconsistent formatting. You should learn OCR basics, chunking strategies, and how to convert unstructured deal docs into clean JSON or tables that downstream systems can use.

   This is the highest-ROI skill for underwriting because many errors come from bad inputs, not bad analysis. If you can build a pipeline that pulls debt terms, financial covenants, sponsor names, and maturity dates into a standard schema, you become much faster at first-pass review.

3. •

   RAG for deal knowledge and policy retrieval

   Retrieval-Augmented Generation is how you ground an LLM in internal credit policy, prior deal memos, historical precedent transactions, and underwriting guidelines. Learn how vector search works at a practical level and how to retrieve only the relevant sections before generating a response.

   Underwriters live on precedent. A model that can answer “show me similar leveraged finance deals with this leverage profile” or “what does our policy say about this industry exposure?” is far more useful than a generic chatbot.

4. •

   Validation and human-in-the-loop review

   In banking, the model is never the final authority. You need skills in output checking: citation verification, confidence thresholds, exception routing, and red-flag detection for hallucinations or stale sources.

   This matters because underwriting mistakes are expensive and visible. If you can design a workflow where the model drafts and you approve only after validation checks pass, you are building something compliance teams can tolerate.

5. •

   Basic agent workflow design

   Learn how to chain tasks: ingest document → extract terms → compare against policy → draft memo section → flag exceptions for review. You do not need to become a software engineer; you need to understand how tools call models, store state, and hand off tasks safely.

   For an underwriter in investment banking, this skill turns AI from “chat” into process improvement. The people who understand workflow design will automate repetitive parts of underwriting while keeping decision rights with humans.

Where to Learn

• •

  DeepLearning.AI — ChatGPT Prompt Engineering for Developers
  Good starting point for controlled prompting patterns. Spend 1 week here if you are new to LLMs.

• •

  DeepLearning.AI — Building Systems with the ChatGPT API
  Useful for learning structured outputs, retrieval flows, and multi-step pipelines. Pair this with underwriting use cases over 2 weeks.

• •

  Hugging Face Course
  Strong practical foundation for transformers, tokenization, embeddings, and model behavior. You do not need to finish everything; focus on the sections about text classification and embeddings over 2–3 weeks.

• •

  Book: Hands-On Large Language Models by Jay Alammar and Maarten Grootendorst
  Better than theory-heavy books if you want practical intuition on RAG, embeddings, evaluation, and applications. Read selectively over 2 weeks.

• •

  Tooling: LangChain or LlamaIndex documentation
  Use one of these to understand document ingestion and retrieval pipelines. Do not try both at once; pick one stack and build a small underwriting prototype in 1–2 weeks.

How to Prove It

• •

  Deal memo summarizer with citations
  Build a tool that ingests an offering memorandum or credit memo PDF and returns: business overview, key risks, leverage metrics, covenant issues, sponsor background, and open questions. Every answer should cite the source page or section so a banker can verify it quickly.

• •

  Covenant comparison engine
  Create a small app that compares covenants across three deals in the same sector and highlights differences in leverage tests, liquidity requirements, baskets, or restricted payments language. This shows you understand what actually matters in underwriting rather than just text generation.

• •

  Precedent transaction lookup assistant
  Load a folder of past underwriting memos or deal summaries and let users ask questions like: “Which deals had similar EBITDA margins but higher leverage?” or “What exceptions were approved for this industry?” This demonstrates RAG plus practical credit judgment.

• •

  Exception tracker for diligence checklists
  Build a workflow that reads diligence notes or Q&A logs and flags missing items such as audited statements not received, legal opinions pending, or inconsistencies between management presentation and model assumptions. That maps directly to real underwriting pain points.

A realistic timeline:

• •Weeks 1–2: Prompting basics + structured output
• •Weeks 3–4: Document extraction + citation-based summarization
• •Weeks 5–6: RAG over internal-style materials
• •Weeks 7–8: Build one portfolio project end-to-end

What NOT to Learn

• •

  Training large models from scratch
  That is not your job as an underwriter in investment banking. You need applied systems around existing models, not GPU research projects.

• •

  Generic chatbot demos with no bank context
  A “hello world” support bot does nothing for your credibility in underwriting interviews or internal mobility conversations. Focus on documents, covenants, memos: the actual work product of your role.

• •

  Over-indexing on coding depth before business depth
  You do not need to become a full-time ML engineer first. If you cannot explain leverage ratios, covenant packages, sponsor risk, or why certain exceptions matter operationally then your AI work will be irrelevant.

If you want to stay relevant through 2026 as an underwriter in investment banking/underwriting in investment banking role changes fast enough that manual-only workflows will keep shrinking./

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.