Best AI for Credit Analysts

Credit analysis is writing-intensive work. The credit memo, the approval memo, the annual review, the watch list write-up, these documents take real time to produce, and much of the time goes to things that don't require the analyst's specific credit judgment: finding the right words to describe an industry's competitive dynamics, structuring the debt analysis narrative, writing the boilerplate sections of a standard credit memo.

AI tools are well-suited to exactly that work. They don't assess credit risk. They don't know whether a borrower's free cash flow projection is credible. They don't have the experience to know which covenant protections matter for a specific credit profile. What they do is take the written work that surrounds the analyst's judgment and produce better first drafts faster.

This guide covers three tools that credit analysts are using effectively in 2026. The focus is on where each tool adds real value in a credit workflow, not just where it can produce an impressive-looking output.

Where AI fits in credit analysis

It helps to be specific about which parts of credit analysis AI can assist and which parts it can't.

AI can help with: Credit memo drafting. Industry overview sections. Financial statement narrative interpretation. Covenant summary sections. Risk factor descriptions. Comparable transaction research on public sources. Answering questions about financial concepts or loan structure mechanics.

AI cannot replace: The analyst's judgment about credit quality. The model that actually calculates the numbers. The professional assessment of management quality, industry position, and repayment capacity. The institutional knowledge of the credit portfolio and how this credit fits within it.

The tools that work best for credit analysts are the ones that slot into the writing and research work without pretending to do the credit judgment.

1. Claude (claude.ai)

Claude is the most useful general-purpose AI tool for credit analysts who want help with memo drafting, analytical writing, and working through complex structural questions.

For credit memo production, Claude's value is in the drafting speed for sections that have consistent structure but variable content. The business description section of a credit memo follows a pattern: nature of the business, primary products or services, key customers and revenue concentration, competitive position. A good description of a borrower takes time to write well. Claude, given the key facts about the borrower, produces a solid draft that the analyst edits rather than writes from scratch. The same applies to the industry overview, the financial analysis narrative, and the deal structure summary.

The debt analysis narrative is a specific use case worth noting. Credit analysts regularly write sections that walk through a borrower's debt metrics, coverage ratios, and the trend in those metrics over multiple years. The structure is consistent, the calculations are already in the model, and the writing task is to explain what the numbers mean and why the trend matters for credit quality. Claude handles this well when you give it the relevant metrics and the context of the deal.

For covenant analysis write-ups, Claude is useful for describing how specific covenants work, what the trigger levels mean in the context of the borrower's financial position, and how the covenant package compares to market terms. Covenant descriptions are templatable writing that AI handles efficiently.

For questions about loan structure mechanics, Claude is strong on the common structures: term loans, revolvers, unitranche, senior/junior structures, PIK mechanics. When an analyst is working on an unfamiliar structure and needs to understand how a specific feature works, Claude explains it accurately and can walk through the mechanics with a hypothetical example.

The data caveat is important for credit analysts: real borrower financial statements, credit applications, and identifying information about specific borrowers should not go into the standard Claude.ai consumer plan. Use Claude for your own analysis and drafting without pasting actual borrower data.

Best for: Credit memo drafting, financial analysis narrative, covenant descriptions, industry overviews, and loan structure explanations. Pricing: Free tier available; Claude Pro at $20/month.

2. Perplexity

Perplexity covers the external research work that comes up in credit analysis, the parts where you need current, cited information about an industry, a borrower's public profile, or recent transactions in a sector.

For industry overview sections in credit memos, Perplexity is faster than manual research. Ask about the competitive dynamics in a specific sector, the recent performance trends in an industry, the key risks that lenders face in a particular market, and it returns cited summaries that you can verify and incorporate. The citation structure is important: anything that goes into a credit memo needs a verifiable source, and Perplexity shows you exactly where each claim comes from.

For borrower background research using public information, Perplexity handles news coverage, press releases, public financial filings, and industry trade publication coverage. For private company borrowers where public information is limited, it at least gives you what's available and flags where the information is thin.

For transaction comps research based on publicly announced deals, Perplexity helps orient you on deal terms, debt multiples, and pricing from publicly disclosed transactions in a sector. It's not a replacement for a full comps database, but for getting a quick sense of where market terms have been for a specific type of credit, it's a useful starting point.

At $20/month for Perplexity Pro, credit analysts who write industry-heavy memos or work with borrowers in less familiar sectors find it worth having.

Best for: Industry background research, borrower public profile research, recent deal and transaction research using public sources. Pricing: Free tier available; Perplexity Pro at $20/month.

3. Glean

Glean addresses the institutional knowledge problem in credit departments. For a credit analyst at a bank or credit fund that has been operating for several years, the relevant prior credit work, the last underwriting of a borrower, the research memo on a comparable credit situation, the industry analysis from two years ago, is usually in the system somewhere but hard to find quickly.

Glean connects to enterprise document storage, email, and knowledge management tools. It indexes everything with access permissions intact and makes it searchable in plain language. A credit analyst working on a new loan to a company in a sector they don't know well can search for prior credits in that sector and find the relevant underwriting materials and research in seconds.

The permissions layer is important in credit. Borrower financial information is confidential, and access controls on credit files aren't optional. Glean's retrieval respects existing file permissions so analysts see only what they're authorized to access.

For credit teams at community banks, regional banks, or credit funds that have built up years of institutional knowledge, Glean reduces the friction of finding prior work and makes that knowledge accessible to analysts who weren't there when the original work was done.

This is enterprise-only with custom pricing. For a small credit team with straightforward document storage, it's overkill. For a larger institution with years of accumulated credit documentation that's genuinely hard to find, it's worth evaluating.

Best for: Credit teams at larger banks and credit funds where finding prior underwriting, research, and portfolio documentation is a recurring bottleneck. Pricing: Enterprise only; custom pricing.

A practical credit memo workflow with AI

Here's how these tools fit together for a credit analyst writing a new loan underwriting memo.

Start with Perplexity to research the industry and any public information on the borrower. Get the industry overview built from public sources with citations you can verify. Get current on any recent news or developments about the borrower or its sector.

Build the financial model and analysis in your spreadsheet as usual. The AI tools don't touch the model.

Use Claude to draft the memo sections based on the analysis you've done. Give Claude the industry facts from your Perplexity research, the financial metrics from your model, the deal structure terms from the term sheet, and the key risk factors you've identified. Claude drafts the written sections from that structured input. You review, verify every fact, apply your credit judgment to the risk assessment, and refine the language.

For prior credit work at your institution, use Glean to find relevant precedents before you start the memo. Knowing what terms were used for similar credits, how prior analysts framed a similar risk, and what the portfolio history looks like for a sector saves time and improves consistency.

The credit judgment is yours throughout. AI handles the writing support and the research acceleration.

Frequently asked questions

Can AI tools help with the financial spreading process?

Not directly. Financial spreading, entering financial statement data into a standardized analysis template, is a manual data entry and calculation task that AI conversational tools don't perform. Purpose-built spreading software or Excel models handle the actual spreading. AI is useful for interpreting the spread results, identifying patterns, and drafting the narrative interpretation.

Is AI useful for portfolio monitoring and annual reviews?

Yes, particularly for the written sections of annual reviews that follow a consistent structure. Claude handles the update narrative for a portfolio review well when given the current financial metrics and the key developments since the last review. The time savings on annual reviews can be significant for analysts who manage large portfolios.

What's the best way to use AI without violating confidentiality policies?

The safest approach: use AI for the parts of your work that are based on public information (industry research, transaction comps) and for drafting language based on analysis you've already done in your model, without pasting actual borrower financial data or identifying information into consumer AI tools. For anything that requires putting actual borrower data into an AI tool, get your institution's approval and use an enterprise tool with appropriate data contracts first.