AI agents Skills for underwriter in banking: What to Learn in 2026

AI is already changing underwriting in banking in very specific ways: document intake is getting automated, financial spreading is being assisted by extraction models, and credit memos are being drafted from structured data instead of blank pages. The underwriter who stays relevant in 2026 will not be the one who “knows AI”; it will be the one who can review model outputs, challenge bad assumptions, and turn messy borrower data into defensible credit decisions.

The 5 Skills That Matter Most

1. •

   Reading and validating AI outputs

   You do not need to build models from scratch, but you do need to know how to spot when a model is wrong. In underwriting, that means checking extracted revenue, debt service coverage ratios, covenant flags, industry codes, and borrower names against source documents.

   Learn how to question confidence scores, missing fields, and hallucinated summaries. A good underwriter in 2026 treats AI output like a junior analyst draft: useful, fast, and always reviewable.

2. •

   Structured financial analysis with Python or SQL

   Underwriters spend too much time rekeying data that should already be structured. Basic Python or SQL lets you clean borrower datasets, compare multiple periods of financials, and flag anomalies across portfolios.

   You are not trying to become a software engineer. You are trying to automate repetitive analysis so you can spend more time on credit judgment, exceptions, and risk discussion.

3. •

   Prompting for credit work

   Prompting is not about writing clever instructions. For an underwriter, it is about getting consistent outputs for tasks like summarizing tax returns, comparing management projections to historical performance, or drafting covenant commentary from a credit package.

   Good prompting also means knowing what not to ask the model to do. If you can define input format, required fields, and output structure, you can make AI useful without letting it wander into unsupported conclusions.

4. •

   Data quality and document understanding

   Most underwriting problems start with bad input data: scanned PDFs, inconsistent borrower names, incomplete schedules, or stale financials. In 2026, underwriters need enough skill to understand OCR errors, extraction failures, and document classification logic.

   This matters because bad data drives bad decisions faster when AI is involved. If you can identify where the source data breaks down, you become the person who prevents automation from creating risk.

5. •

   Model governance and explainability

   Banks will keep tightening controls around AI use in credit workflows. Underwriters who understand audit trails, approval thresholds, human-in-the-loop review, and model limitations will be more valuable than people who just use tools blindly.

   You should be able to explain why a recommendation was accepted or rejected. That includes knowing how to document exceptions, preserve decision rationale, and align with policy and regulatory expectations.

Where to Learn

• •

  Coursera — "Python for Everybody" by University of Michigan

  Good starting point if you have never used Python. Spend 3-4 weeks on this if your goal is basic data handling for underwriting analysis.

• •

  DataCamp — "Introduction to SQL"

  Useful for pulling loan book data, filtering portfolios by risk grade or industry code, and building simple reports. Plan 2-3 weeks of part-time learning.

• •

  DeepLearning.AI — "ChatGPT Prompt Engineering for Developers"

  Short course that teaches structured prompting patterns you can apply to credit memo drafting and document summarization. Use it as a 1-week sprint.

• •

  Book: The Handbook of Credit Risk Management by Sylvain Bouteille and Diane Coogan-Pushner

  Not an AI book, but still relevant because AI does not replace core credit judgment. Read it alongside your AI learning so you do not lose underwriting discipline.

• •

  Microsoft Power Automate + Azure AI Document Intelligence documentation

  Practical tools if your bank already lives in Microsoft ecosystems. These are useful for building document intake workflows over 2-4 weeks without needing a full engineering team.

How to Prove It

1. •

   Build a loan package summarizer

   Take a sample set of anonymized financial statements and management commentary. Use an LLM to produce a standardized summary covering borrower profile, key risks, leverage trends, liquidity position, and covenant concerns.

   Then compare the output against your own manual summary. The point is not perfection; it is showing that you can control structure and validate results.

2. •

   Create a covenant monitoring dashboard

   Use Excel + Power Query or Python + Streamlit to track covenant dates, DSCR trends, leverage thresholds, and exception status across a small portfolio sample. Add alerts for missing statements or approaching test dates.

   This shows that you understand how underwriting connects to ongoing monitoring. It also proves that you can work with structured data instead of living inside PDFs.

3. •

   Document extraction error checker

   Build a simple workflow that compares OCR-extracted fields from scanned borrower documents against expected values from the loan file. Flag mismatches in revenue figures, legal entity names, maturity dates, or guarantor details.

   This is highly relevant because many underwriting errors come from bad extraction rather than bad analysis. A project like this demonstrates practical risk control thinking.

4. •

   Credit memo first-draft generator

   Feed structured inputs such as financial ratios, industry notes, collateral details, and policy exceptions into a prompt template that drafts the first version of a credit memo section-by-section.

   Keep it constrained: no final recommendation without human review. That makes the project realistic for banking environments where auditability matters.

What NOT to Learn

• •

  Generic “AI strategy” content with no underwriting use case

  Slide decks about transformation are not helping you approve better loans or catch weaker credits faster. Stay close to workflows: spreading financials, memo writing, covenant tracking, exception handling.

• •

  Deep machine learning theory before basic data skills

  You do not need neural network math before you can use Python or SQL effectively in underwriting. Start with extracting value from real loan data first.

• •

  Consumer chatbot tricks unrelated to regulated work

  Building funny prompts or image generators will not move your career forward in banking credit roles. Focus on controlled outputs, traceability, and decision support inside policy boundaries.

If you want a realistic timeline: spend the first 2 weeks on prompt basics and document workflows; weeks 3-6 on Python or SQL fundamentals; weeks 7-10 building one portfolio project tied directly to loan review; then keep iterating with real underwriting examples from your desk.

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.