ChatGPT Prompts for Credit Analysts: Risk Assessment, Portfolio, Covenants

Published: July 2026 | 8 min read

Credit analysts evaluate risk every day — assessing borrowers, setting credit limits, monitoring portfolio health, and recommending approvals or denials. The analysis requires synthesizing financial statements, credit reports, industry data, and qualitative factors into a risk rating. AI prompts can accelerate the document-heavy parts of credit analysis: financial spreading, ratio computation, risk memo drafting, and portfolio reporting.

Below are production-ready ChatGPT prompts for credit analysts covering credit assessment, portfolio monitoring, industry analysis, and loan documentation. These are adapted from Skillent's Finance AI Prompt Library.

Individual Credit Assessment

1. Consumer Credit Risk Assessment

Role: You are a credit analyst evaluating a consumer credit application.
Applicant: [age placeholder, income, employment, housing cost, existing debts, credit score range]
Requested credit: [type — personal loan/auto/mortgage/credit card], [amount], [term]
Task: Perform credit risk assessment.
Evaluate using 5 C's:
1. Capacity — DTI ratio, payment-to-income, income stability
2. Character — credit score, payment history, delinquencies, bankruptcies
3. Capital — savings, assets, down payment (if applicable)
4. Collateral — loan-to-value (if secured), asset condition
5. Conditions — economic context, loan purpose, industry risk
Compute:
- DTI ratio (should be <36% typically, <43% for mortgage QM)
- Front-end ratio (housing/income)
- Credit utilization rate
- Payment shock (if rate adjusts)
Format: Credit assessment memo with risk rating and recommendation (Approve / Conditional / Decline).
Include: Recommended credit limit, interest rate tier, and conditions.

2. Small Business Credit Analysis

Role: Credit analyst evaluating a small business loan application.
Business: [type], [years in operation], [revenue], [entity type]
Input: [3 years of financial statements, owner credit summary, industry, collateral offered]
Task: Perform small business credit analysis.
Analysis sections:
1. Business overview and industry risk
2. Financial statement spreading:
   - Revenue trend (3-year CAGR)
   - Gross margin trend
   - Net margin trend
   - Current ratio, quick ratio
   - Debt-to-equity, debt service coverage ratio (DSCR)
   - Days sales outstanding, inventory turns, days payable
3. Cash flow analysis:
   - Operating cash flow trend
   - Free cash flow
   - Ability to service proposed debt payment
4. Owner personal credit (for guarantees)
5. Collateral evaluation (LTV on assets pledged)
6. Industry risk rating
Recommend: Approval, approval with conditions, or decline with rationale
Suggest: Loan structure (amount, term, rate, covenants, collateral)
Format: Credit memo, 2-3 pages.
Include: Risk rating assignment per bank's rating scale.

3. Financial Statement Spreading Worksheet

Role: Credit analyst spreading financial statements for analysis.
Input: [Paste 3 years of balance sheet, income statement, cash flow statement data]
Task: Create a spread financial statement with ratios.
Spread into:
1. Balance sheet — year over year, $ change, % change
2. Income statement — year over year, $ change, % change, common size (% of revenue)
3. Cash flow statement — year over year trend
Calculate ratios:
- Liquidity: Current, quick, cash ratio
- Leverage: Debt-to-equity, debt-to-assets, interest coverage, DSCR
- Profitability: Gross margin, operating margin, net margin, ROA, ROE
- Efficiency: Asset turnover, inventory turnover, AR turnover, AP turnover
- Cash flow: OCF/sales, FCF/sales, cash conversion cycle
Format: Spreading template with 3-year columns and ratio summary.
Include: Industry benchmark comparison where data is available.
Flag: Any ratio that changed >25% year over year for investigation.

Portfolio Monitoring

4. Portfolio Risk Rating Migration Report

Role: Credit analyst preparing portfolio risk rating migration report.
Input: [List of borrowers with: prior risk rating, current risk rating, exposure, past due status]
Task: Create a risk rating migration matrix.
Matrix:
Rows = prior risk rating, Columns = current risk rating
For each cell: count of borrowers and total exposure
Compute:
1. Migration rate (what % moved up/down/stayed)
2. Downgrade exposure ($ and % of portfolio)
3. Upgrade exposure ($ and %)
4. Stable exposure ($ and %)
5. Net migration trend (improving/deteriorating)
6. Watch list additions/removals
Format: Migration matrix with summary statistics.
Include: Top 5 downgrades by exposure with explanation column.
Flag: Any borrowers that dropped 2+ rating notches (potential problem loans).

5. Loan Portfolio Concentration Analysis

Role: Credit analyst performing portfolio concentration analysis.
Input: [Portfolio summary — borrower, exposure, industry, geography, collateral type, loan type]
Task: Analyze portfolio concentrations.
Concentration dimensions:
1. By industry (top 10 with % of total portfolio)
2. By geography (top 10 with %)
3. By loan type (term, revolver, mortgage, etc.)
4. By collateral type (real estate, equipment, AR, unsecured)
5. By borrower size (small/medium/large)
6. Single-name concentration (top 20 borrowers)
7. Related-party concentration (common ownership groups)
For each dimension:
- Current concentration %
- Internal limit %
- Regulatory limit (if applicable)
- Headroom or breach
Format: Concentration analysis dashboard.
Include: Recommendations for rebalancing if any concentration exceeds limits.

6. Early Warning Indicator Dashboard

Role: Credit analyst building an early warning indicator system.
Task: Create an early warning indicator (EWI) dashboard specification.
Indicators:
1. Financial: DSCR < 1.2x, current ratio < 1.0, debt-to-equity increasing >20%, margin compression >3pp
2. Behavioral: Late payments, covenant breaches, overdraft activity, line utilization increasing
3. External: Industry downturn, regulatory changes, management changes, legal filings
4. Portfolio: Risk rating downgrade, watch list addition, past due migration
For each indicator:
- Definition and threshold
- Data source
- Monitoring frequency
- Alert level (Yellow/Red)
- Action required (monitor, contact borrower, restructure, classify)
Format: EWI dashboard specification with alert levels.
Include: Escalation protocol — who is notified at each alert level.

Industry & Macro Analysis

7. Industry Risk Assessment

Role: Credit analyst assessing industry risk for lending decisions.
Industry: [name], economic context: [current conditions — recession/expansion/recovery]
Task: Create an industry risk assessment.
Include:
1. Industry size and growth trajectory
2. Cyclicality (highly cyclical / counter-cyclical / stable)
3. Barriers to entry
4. Competitive landscape (fragmented / consolidating / oligopolistic)
5. Regulatory environment (increasing / stable / deregulating)
6. Key risks:
   - Demand drivers and sensitivity
   - Input cost volatility
   - Labor availability
   - Technology disruption
   - Regulatory risk
   - Environmental/ESG risk
7. Industry default rate trend (vs. overall portfolio)
8. Risk rating recommendation for this industry
Format: Industry risk memo, 2 pages.
Include: Recommended exposure limits and monitoring focus areas.

8. Borrower Competitive Position Analysis

Role: Credit analyst evaluating a borrower's competitive position.
Borrower: [company], industry: [name], market position: [leader/challenger/niche]
Input: [Company financials, market share estimate, competitor names]
Task: Analyze competitive position.
Evaluate:
1. Market share and trend (gaining/stable/losing)
2. Differentiation (price, quality, service, technology, location)
3. Customer concentration (top 5 customers as % of revenue)
4. Supplier concentration and dependency
5. Pricing power (can they raise prices?)
6. Switching costs for customers (high = sticky = lower risk)
7. Competitive moats (patents, brand, network effect, scale, regulatory)
8. Threat of substitutes
9. Digital transformation readiness
Format: Competitive analysis memo with risk implications for credit.
Include: How competitive weakness affects repayment ability and what covenants might protect the lender.

Loan Documentation & Covenants

9. Covenant Package Recommendation

Role: Credit analyst recommending loan covenants for a credit facility.
Borrower: [company], facility: [type — term/revolver/asset-based], amount: [amount]
Risk rating: [rating], industry: [type]
Task: Recommend a covenant package.
Financial covenants:
1. Minimum DSCR (typically 1.15-1.25x based on risk)
2. Maximum debt-to-EBITDA (typically 3.0-4.0x)
3. Minimum current ratio (1.0-1.2)
4. Minimum tangible net worth
5. Maximum capital expenditures
6. Minimum interest coverage
7. Maximum dividend/distribution (springing or hard)
Affirmative covenants:
- Financial reporting (quarterly, annual, within X days)
- Insurance maintenance
- Tax compliance
- Property maintenance
Negative covenants:
- Additional debt restrictions
- Asset sale restrictions
- Change of control
- Related party transaction limits
Format: Covenant recommendation memo with rationale for each.
Include: Cure periods and waiver process for each financial covenant.

10. Loan Structuring Recommendation

Role: Credit analyst recommending loan structure.
Borrower: [company], purpose: [acquisition/working capital/expansion/refinance]
Input: [Financial summary, collateral available, repayment source, cash flow projections]
Task: Recommend loan structure.
Options:
1. Term loan (amortizing) — for equipment, acquisition
2. Revolving credit — for working capital
3. Asset-based lending — for AR/inventory financing
4. Mezzanine/subordinated — for gaps in capital structure
For recommended structure:
- Amount and sublimits
- Term and amortization schedule
- Interest rate structure (fixed/variable, index, margin)
- Fees (upfront, commitment, unused)
- Collateral and LTV
- Guarantees (corporate, personal)
- Conditions precedent
Format: Loan structuring memo with term sheet outline.
Include: Sensitivity of repayment ability under stress scenarios.

Special Situations

11. Problem Loan Action Plan

Role: Credit analyst creating an action plan for a deteriorating credit.
Borrower: [name], current risk rating: [rating], exposure: [amount]
Issues: [DSCR breach, margin compression, management change, customer loss, covenant breach]
Task: Create a problem loan action plan.
Sections:
1. Situation summary (what went wrong and when)
2. Current exposure and loss given default (LGD) estimate
3. Recovery options:
   a. Workout — restructuring terms, extending maturity, PIK interest
   b. Additional collateral or guarantees
   c. Refinance with another lender
   d. Foreclosure/enforcement
4. Recommended strategy (with rationale)
5. Timeline and milestones
6. Required approvals (credit committee level)
7. Monitoring plan (weekly/monthly)
8. Provision recommendation (specific reserve calculation)
9. Reporting requirements (special mention / substandard / doubtful)
Format: Problem loan action memo.
Include: Exit strategy if workout fails — timeline and process.

12. Credit Committee Presentation Template

Role: Credit analyst preparing a credit committee presentation.
Borrower: [name], request: [new loan/renewal/modification], amount: [amount]
Task: Create a credit committee presentation outline.
Slides:
1. Executive Summary (borrower, request, amount, risk rating, recommendation)
2. Borrower Overview (history, ownership, management, business model)
3. Financial Analysis (3-year trends, ratios, spreading highlights)
4. Cash Flow & Repayment (primary source, secondary source, DSCR, stress test)
5. Collateral Analysis (what's pledged, LTV, liquidation analysis)
6. Industry & Competitive Position (risk factors, mitigants)
7. Structure (amount, term, rate, covenants, guarantees)
8. Risk Assessment (key risks, mitigants, residual risk)
9. Recommendation (approve/decline, conditions, risk rating)
For each slide: Key message, data needed, talking points
Format: Presentation outline with slide-by-slide notes.
Length: Suitable for 15-20 minute presentation with Q&A.

Best Practices for Credit Analyst AI Prompts

1. Never input actual borrower SSNs or EINs — use placeholder names and replace after generation

2. Always specify the institution type — bank, credit union, non-bank lender, or factor. Different regulations apply

3. Include the regulatory framework — OCC, FDIC, NCUA, or state banking regulator. This affects covenant requirements

4. Specify risk rating scale — every bank has its own scale. AI needs to know yours to produce compatible output

5. Review all financial spreading — AI can miscalculate ratios. Every ratio must be manually verified against the source financials

For more credit-related resources, see our AI prompts for insurance underwriters and AI prompts for cash flow analysis.

How to Use These Prompts Effectively

Getting the most out of AI prompts for financial work requires a structured approach. Here's how to integrate these prompts into your workflow:

1. Start with Clean Data

Before running any prompt, ensure your source data is organized and accurate. AI output is only as good as the input. Gather your trial balances, general ledger exports, prior-period statements, and any supporting documentation. The more structured your input data, the more useful the AI output will be.

2. Iterate and Refine

Don't expect perfect output on the first run. Start with the prompt as written, review the output, then refine. Add context about your specific industry, company size, accounting software, or reporting requirements. The prompts above are templates — adapt them to your exact situation.

3. Always Verify Against Source Documents

AI can make calculation errors, cite outdated tax rates, or miss nuances in your chart of accounts. Every piece of output that contains a number, a tax rate, a deadline, or a regulatory citation must be verified against primary sources. This is not optional — it's a professional responsibility.

4. Build a Prompt Library for Your Organization

Save the prompts that work best for your team. Document what you changed, what input data you provided, and what output format worked best. Over time, you'll develop a customized prompt library that reflects your organization's specific accounting practices, reporting requirements, and industry context.

5. Use AI for Drafts, Not Final Products

The most effective pattern is to use AI prompts to generate first drafts — whether that's a budget model, a variance commentary, a reconciliation summary, or a tax memo. Then apply your professional judgment to review, correct, and finalize. AI accelerates the draft phase; it doesn't replace the review phase.

Why These Prompts Matter for Finance Teams

Finance and accounting teams are under increasing pressure to do more with less. Month-end close cycles are compressing. Reporting requirements are expanding. Audit expectations are rising. And the demand for real-time financial insight — not just historical reporting — is growing across every organization.

AI prompts like these address that pressure by automating the repetitive, pattern-based work that consumes hours of professional time each week. A variance analysis that used to take three hours can be drafted in twenty minutes. A budget model that used to start from a blank spreadsheet can begin with a structured framework. A reconciliation narrative that used to be written line by line can be generated from structured input.

The goal isn't to replace financial professionals — it's to free them from mechanical work so they can focus on analysis, strategy, and advisory roles. The most successful finance teams using AI are the ones that treat it as a junior analyst: capable of producing solid first drafts quickly, but requiring review and guidance from senior professionals before the work is finalized.

For more finance prompts across specialties, explore the full Skillent Finance Prompt Library.

Common Mistakes to Avoid with Finance AI Prompts

After working with hundreds of finance teams adopting AI tools, we've identified the most common — and most costly — mistakes professionals make when starting to use AI prompts in their workflow.

Mistake 1: Treating AI Output as Final

The single most dangerous mistake is treating AI-generated financial analysis, tax calculations, or reporting narratives as final without review. AI can produce output that looks correct — proper formatting, confident tone, plausible numbers — while containing subtle errors. A misplaced decimal, an outdated tax rate, a misapplied formula, or a misinterpreted accounting standard can turn a useful draft into a professional liability. Always treat AI output as a first draft that requires professional review.

Mistake 2: Inputting Sensitive Client Data

Entering client names, social security numbers, full financial statements, or proprietary business data into an AI tool without understanding the tool's data handling policy is a serious risk. Some AI tools retain input data for training purposes. Some may expose data through API responses. Before using any AI tool with real client data, verify the tool's privacy policy, data retention practices, and whether you can disable training on your inputs. When in doubt, use anonymized or placeholder data.

Mistake 3: Using Generic Prompts for Specialized Tasks

A prompt designed for general financial analysis won't produce good output for a specialized task like transfer pricing documentation or ASC 842 lease accounting. The prompts in this guide are tailored to specific finance functions. Using a generic "analyze this financial data" prompt when you need a specific deliverable — like a variance commentary for a board meeting — will produce generic, unhelpful output. Match the prompt to the task.

Mistake 4: Ignoring Context

AI tools don't know your company, your industry, your accounting policies, or your reporting requirements unless you tell them. A prompt that says "analyze this balance sheet" without context will produce generic analysis. A prompt that says "analyze this balance sheet for a SaaS company using ASC 606 revenue recognition, with a fiscal year ending June 30, reporting to a Board of Directors" will produce targeted, useful analysis. Context is the difference between a parlor trick and a professional tool.

Mistake 5: Not Iterating

The first run of a prompt rarely produces the best possible output. The most effective users run a prompt, review the output, refine the prompt based on what was missing or wrong, and run it again. This iterative process — two or three rounds of refinement — typically produces dramatically better results than the first run. Build time for iteration into your workflow.

Mistake 6: Forgetting the Human Review Layer

AI is a tool, not a team member. Every piece of output that will be seen by a client, an auditor, a regulator, or a board member must be reviewed by a qualified professional who understands the context and takes responsibility for the final product. This isn't just best practice — in many financial roles, it's a regulatory requirement. The human review layer is non-negotiable.

Quick Start Guide: Your First Week with Finance AI Prompts

If you're new to using AI prompts in your finance workflow, here's a practical week-one plan:

Day 1-2: Experiment with Low-Stakes Prompts

Start with prompts that produce output you can immediately verify. Try the variance analysis prompt with last month's actual vs. budget data. Try the reconciliation summary prompt with a small subset of accounts. The goal is to get comfortable with the prompt structure, see the quality of output, and calibrate your expectations.

Day 3-4: Integrate into a Real Workflow

Pick one recurring task — maybe the monthly variance commentary, or the weekly cash flow summary — and use the relevant prompt to produce a first draft. Then complete the task as you normally would, comparing the AI-assisted version to your traditional approach. This will show you where AI helps, where it falls short, and how to integrate it effectively.

Day 5: Customize and Save

Take the prompt you used during the week and customize it for your specific situation. Add your industry, your accounting software, your reporting format, your typical account structure. Save the customized prompt. This becomes the starting point for next week's work — and the beginning of your personal prompt library.

Ready to access the full library of 190,000+ professional AI prompts? Get Skillent Pro for $9/month and unlock prompts for every finance function.

Advanced Pro Tips for Finance AI Prompts

Chain Prompts for Complex Analysis

For complex deliverables, chain multiple prompts together. Start with a data extraction prompt to pull key figures from source documents. Feed that output into an analysis prompt. Then feed the analysis into a formatting prompt that structures the output for your final deliverable. This three-step chain — extract, analyze, format — produces better results than trying to do everything in one prompt. Each step can be refined independently, and intermediate output can be reviewed before proceeding.

Build a Prompt Version History

As you refine prompts over time, keep a version history. Save the original prompt, note what changes you made and why, and track which versions produced the best output. This creates an institutional knowledge base that your entire team can benefit from. When a new team member joins, they can start with your refined prompts rather than the generic originals.

Create Industry-Specific Variants

The prompts in this guide are written for general finance and accounting use. But a CPA serving manufacturing clients needs different prompts than one serving SaaS startups. Create industry-specific variants of each prompt — add industry-specific tax considerations, accounting standards, reporting formats, and common transaction types. Industry-specific prompts produce dramatically better output than generic ones.

Pair AI with Spreadsheet Skills

The most powerful workflow combines AI prompts with advanced spreadsheet skills. Use AI to generate the analysis framework, the commentary, and the narrative. Use Excel or Google Sheets for the actual calculations — where formulas are auditable and verifiable. Copy AI-generated commentary into spreadsheet cells alongside the numbers. This hybrid approach leverages the strengths of both tools: AI for language, spreadsheets for math.

Document Your Review Process

For audit and compliance purposes, document your AI review process. Note which AI tool was used, which prompt was used, what the output was, and what changes you made during review. This documentation demonstrates professional diligence and provides a clear audit trail. Some firms now require this documentation as part of their quality control procedures.