AI for credit analysis: Automated financial analysis, risk flagging, and borrower evaluation

Private credit analysts need to form a fast, accurate, and defensible view of a borrower's creditworthiness and deliver a term sheet quickly. 

The information analysts use to draft a memo is buried deep within a data room of CIMs, Excel models, presentations, contracts, and filings—hundreds of documents across various formats, often with missing or contradictory information.

But there’s hope. With advances in AI credit analysis, modern tools can extract financials from any format, normalize spreads, surface risk flags, design covenants, and assemble investment committee memos based on their findings. Analysts regain time to evaluate borrowers with full context, leaving tedious manual work to automation and enabling them to prep IC materials in record time. 

This guide explains what AI credit analysis means for private credit teams, how AI financial analysis interprets complex financial models, and how teams are accelerating their underwriting processes and winning bids. 

Where private credit analysts lose time

Rushing the underwriting process can lead to overlooking borrower risks, extending a term sheet based on an ill-informed evaluation, and investing in a portfolio company that falls outside your investment thesis. But for an analyst to conduct their screening with the expected thoroughness, it can take hours of tedious, low-leverage work. 

Each step of the traditional workflow introduces friction.

• Collecting borrower files: Borrowers submit mixed-format reports, like PDFs, Excel files, screenshots, scanned tax returns, bank statements, and ad hoc internal reports, requiring analysts to categorize documents just to make sense of what’s missing.
• Spreading borrower financials: Analysts must rebuild the P&L, balance sheet, and cash flow statements from scratch, ensuring every line item is mapped consistently.
• Normalizing line items: Borrowers often label expenses, revenue components, and COGS differently. Analysts must standardize their chart of accounts before analysis can begin.
• Adjusting EBITDA: EBITDA adjustments are never a one-size-fits-all approach, so analysts must scour footnotes, trace supporting documents, and validate management explanations to understand the company’s profitability.
• Running coverage, leverage, and liquidity calculations: DSCR, FCCR, LLCR, senior leverage, total leverage, and liquidity coverage all depend on the accuracy of the financials. Analysts recalculate these over varying time periods. 
• Designing the covenant framework: Analysts have to create the covenant structure, including which financial requirements to include, how to define them, and where to set thresholds based on the borrower’s history and downside sensitivities.
• Drafting the credit memo: Once the analysis is finished, analysts spend hours drafting borrower overviews, financial summaries, risk sections, and preliminary recommendations.

Each of these manual tasks represents a trade-off between detailed analysis and time-to-decision. With AI tools, analysts can accelerate the underwriting process with high accuracy, freeing up time to make contextual decisions about an applicant's creditworthiness.

What AI credit analysis means in private credit

AI credit analysis leverages an agentic architecture used by underwriting teams to ingest unstructured borrower materials, reconstruct financials with audit-grade accuracy, identify the most relevant risks, and generate screening and IC-ready outputs — all with the precision investment committees require.

Credit teams upload a borrower package, and the system begins identifying, executing, and verifying tasks using specialized agents.

• A specialized Excel-analysis layer designed for multi-sheet, formula-heavy files that ordinary LLMs cannot reliably interpret. The system encodes spreadsheet structure, formulas, ranges, and dependencies into a machine-readable representation, enabling precise cell-level extraction and tracing.
• PDF and unstructured-document engines trained on messy private market reporting — management decks, tax returns, scanned statements, CIMs — with the ability to surface every extracted number’s source page for auditability.
• Document-type classification and retrieval systems that detect missing bank statements, aging reports, or financial schedules, and automatically compile every relevant source for each analytical step.
• Financial normalization and mapping logic that aligns borrower statements to consistent schemas, enabling precise recomputation of DSCR, FCCR, liquidity coverage, and margin trends across inconsistent formats.
• Synthesis layers that convert these structured financials into borrower summaries, preliminary risk assessments, and draft IC-ready materials — all editable in natural language and tied back to precise source data.

The traditional underwriting workflow is completely transformed, giving credit teams a significant speed advantage while maintaining the same rigorous analytical rigor of a seasoned analyst.

How AI automates financial spreading and analysis 

Borrower documents are often received misclassified, disorganized, and in various file formats. Before analysts can evaluate a company’s financial health, they must first structure the data into a usable format. It’s not particularly necessary for humans to execute — but it’s critical to the underwriting process. 

AI automates this workflow end-to-end, replacing one of the least strategic tasks with reliable, insight-ready outputs.

Here’s how it works.

Step 1: Document Ingestion

Analysts begin by uploading borrower materials. The platform identifies each file type — P&Ls, balance sheets, cash flow statements, footnotes, bank statements, tax returns — regardless of formatting.

Step 2: Automated classification

AI distinguishes financial statements from operational or supplemental documents, organizing the borrower package into defined categories. At any point in the workflow, an analyst can ask the chatbot to surface any information in the data room, and the system can retrieve it because it can reason over the entire borrower package at once. 

Step 3: Extraction of financials

AI extracts line items from income statements, balance sheets, and cash flow statements, and understands complex Excel formulas, merged cells, and cross-sheet references. It reads footnotes and supplemental schedules to capture necessary adjustments and context.

Step 4: Normalization into a standardized financial model

Once extracted, financials are mapped to a normalized structure. AI performs this mapping consistently across the portfolio — not just within a single deal — enabling lenders to derive insights from it. 

Step 5: Automated financial analysis

Once financials are normalized, the platform calculates key metrics using the lender’s exact definitions. F2 interprets multi-sheet Excel models the same way an experienced analyst would — following formula chains, resolving irregular cell structures, and tracing cell references across tabs — allowing it to recompute DSCR, FCCR, leverage, liquidity, and working-capital cycles with near-perfect accuracy. Each metric links back to its originating cell or page.

Automate your multi-year financial spreading with F2.

Step 6: Private-credit-specific KPIs

F2’s platform interprets and generates deep financial insights through its contextual understanding across a borrower’s application. When an analyst needs to incorporate factors such as working capital, capex, taxes, and non-operating items to calculate recurring KPIs like cash flow, free cash flow coverage, liquidity runway, and downside capacity, F2’s purpose-built architecture can reference this information to complete the task.

Key opportunities for AI risk analysis 

The inherent challenge credit teams face when assessing borrower risk is that the data they need is spread across different reporting periods, disclosures, and Excel tabs. They’re not apparent at first glance.

But because the platform has a comprehensive understanding of the entire data room, it can quickly surface potential risk flags and link them back to the source of the information, including:

• Flagging margin compression, sudden expense spikes, growth volatility, and unexplained shifts in operating income as soon as they appear in the financials.
• Detecting shrinking liquidity cushions, falling interest coverage, and weakening cash conversion directly from the normalized spread.
• Uncovering abnormal AR aging, inventory buildup, and payables compression the moment these working-capital pressures emerge.

How analysts assess borrower quality using AI credit analysis findings 

AI can turn a messy data room into investment insights in minutes, enabling analysts to focus on a borrower's quality.

Instead of manually assembling IC materials, analysts begin with a complete analytical baseline and focus on formulating opinions based on the results. 

1. Reviewing financial summaries and qualitative findings: Analysts use the generated summaries as the first “map” of the borrower; they verify the underlying sources through the platform, adjust the language for nuance, and ask the system to drill deeper into specific areas. 
2. Validating addbacks and adjusted earnings: The platform identifies all addbacks across reporting periods, links each adjustment to the underlying footnotes or supporting schedules, and flags items with weak or inconsistent justification. Analysts then confirm valid adjustments, removing unsupported ones, or asking the system to recalculate adjusted EBITDA using the updated addbacks. 
3. Evaluating borrower quality and stability: Analysts can ask the platform to benchmark core outputs against comparable portfolio deals or external datasets, such as FactSet. Underwriting teams can also compare the platform’s results to other external datasets by connecting their API keys to the platform. 
4. Reviewing sensitivities and downside scenarios: The platform will generate downside and covenant-stress scenarios by adjusting revenue, margin, or working capital assumptions based on historical patterns. Analysts review these cases in context and can ask the system to modify assumptions and re-calculate scenarios. This enables underwriting teams to test and design covenant structures with greater confidence.
5. Refining draft memos: The platform drafts IC-ready materials, and analysts can revise any section using natural language — rewriting risk sections, adjusting charts, adding references to management conversations, or incorporating deal-specific nuances. The platform automatically updates all downstream charts and tables when analysts modify assumptions or values, eliminating manual rework.

Throughout the underwriting process, analysts can “interview” the AI chatbot at any time with any inquiry. 

Here are some examples: 

• Show me how DSCR changes under a 200 bps rate increase.
• Highlight which customers drive 80% of revenue.
• Rewrite the margin analysis as a three-bullet summary.
• Explain why liquidity dropped in Q2 and highlight the source pages.

Analysts working hand in hand with the platform now assume investigative and review roles, while tedious manual work is automated. 

Next steps for private credit teams implementing AI credit analysis

Forward-thinking credit teams can adopt AI underwriting by following these steps: The most effective private credit teams approach AI adoption with precision and intentionality:

1. Audit the current underwriting workflow: Document the most time-consuming steps and identify low-leverage, repeatable tasks that could be automated.
2. See the full underwriting workflow inside F2: Schedule a demo with F2 to walk you through how AI handles borrower ingestion, Excel interpretation, scenario analysis, and memo generation end-to-end. You’ll see how the platform reconstructs financials, surfaces risk, and produces auditable outputs before an analyst even begins their review.
3. Share your past deal materials for training: Provide examples of prior screening memos and IC materials. F2’s deployment team will train your workspace on your firm’s formatting, analysis structure, and investment theses, ensuring all outputs align with your existing workflow and compliance requirements.
4. Pilot with real borrower materials and measure the impact: Run an initial deal through F2 and compare the time savings and quality of IC materials to your current process. Many teams see up to a 75% reduction in underwriting time with greater accuracy and defensible insights than existing underwriting workflows. 

Conclusion

AI credit analysis opens the door for private credit teams to close deals faster and underwrite with better insights into the applicant’s financial health.  

By removing bottlenecks throughout the underwriting process, teams can focus on high-impact work, build a stronger portfolio, and reduce risk exposure across investments. 

Schedule an F2 credit analysis walkthrough.