The complete guide to AI underwriting for middle market lenders

Deal teams spend hours spreading financials, sifting through data rooms, and putting meticulous finishing touches on investment committee reports. 

These have been the realities of private market investors for far too long. That is, until recently, as AI has entered the chat. 

Today, investors use AI to automate their analysis, cite and source their outputs with precision, and deliver IC-ready reports in minutes. The best applications of AI are designed to improve human judgment by removing repetitive tasks that analysts face, enabling them to focus on nuance and context and make better decisions. 

This guide outlines how AI fits into modern underwriting workflows, the impact of AI integration on deal teams' speed and precision, and the future of AI in underwriting and credit analysis. 

What AI in underwriting means

When private deal teams leverage advanced technologies such as LLMs to process large volumes of information, perform complex financial analysis, and generate insights, this is referred to as “AI underwriting.”

Modern systems often use an “agentic” architecture: one layer determines the job to be done (e.g., spreading financials), and then sub-agents independently execute those tasks before synthesizing the results. This enables AI to break down underwriting into smaller, incremental steps that traditionally consumed large amounts of an analyst’s time.

As a result, analysts can begin their process with structured inputs and foundational insights, enabling them to focus on second-order analysis and preparing materials for their VP.

Why AI underwriting matters: The bottlenecks AI eliminates

AI underwriting is designed to enable teams to make faster, more informed investment decisions. Firms of all sizes and across all deal velocities struggle with common-denominator challenges — typically stemming from volume, variability, and the complexity of borrower data — in their underwriting processes. AI fixes these bottlenecks:

• Unstructured data: Private markets are notorious for lacking consistent reporting structures found in public markets — mismatched PDFs, missing tax returns, and data rooms with no folder hierarchy or naming conventions. AI structures incoming data rooms by ingesting, classifying, and organizing documents in real time.
• Manual financial spreading: Analysts rebuild financial statements line by line, mapping time-series trends, and calculating ratios across inconsistent statements — a process that is slow, error-prone, and varies significantly across analysts. AI performs extraction, mapping, and dissemination in seconds, enabling analysts to begin interpreting the outputs. Some platforms even handle multi-tab Excel models directly, referencing specific cell ranges and consolidating data across sheets. 
• Institutional knowledge scattered across the portfolio: Much of underwriting depends on pattern recognition — how similar borrowers performed, what covenant sets worked for comparable companies, and which risk factors historically led to losses. But this memory is scattered across old memos, spreadsheets, and the personal experience of a few senior team members. AI solves this by indexing a firm’s entire corpus of historical work across its portfolio — past deals, memos, covenant packages, and performance outcomes — and surfacing the most relevant information for each new borrower, enabling more consistent covenant design, better-justified credit decisions, and faster alignment across the underwriting team. 
• Dense documents that slow down risk evaluation: Contracts, quality of earnings (QoE) reports, customer lists, and HR files contain critical information, but analysts don’t have the time to read them line by line — leading to missed risk factors that can ultimately delay IC decisions. AI summarizes these documents instantly, extracts risk-relevant details, and links each insight back to the exact page or clause it came from, giving underwriters fast, defensible visibility into exposures without sacrificing auditability or trust.
• IC materials take too long to prepare: Most investment committee memos repeat the same sections — company overview, financial summary, covenant reasoning, and risk profile — forcing analysts to rebuild familiar content from scratch each time. AI generates the full first draft instantly, using templates trained on past memos to match internal tone, structure, and formatting, so analysts can focus on judgment rather than assembly.

The end-to-end AI workflow for underwriting teams 

Below is what an AI-powered underwriting workflow looks like for underwriting teams today. 

Step 1. Document intake and classification

When a borrower’s files are uploaded to the platform, the platform classifies, groups, and organizes them into a workspace. The AI can parse nested data room structures, map file types to specific underwriting tasks, and quickly surface relevant information while flagging extraneous items.

Step 2. Data extraction and normalization

AI identifies the key financial and operational line items in each document — revenue, COGS, operating expenses, EBITDA adjustments, working capital changes, and key contractual terms — and converts them into structured data.

Once the data is extracted from various documents, it’s normalized to enable accurate financial comparisons, trend analysis, and credit analysis. 

Step 3. Automated financial spreading

AI produces clean, structured formatting for financial analysis in seconds:

• Multi-year P&Ls
• Balance sheets
• Cash flows
• Working-capital schedules
• Margin trends
• Year-over-year changes
• Ratio analysis (DSCR, FCCR, leverage, liquidity)

Files are cross-referenced to check for and reconcile any discrepancies. 

Analysts can then validate the outputs rather than building them from scratch. For teams handling high deal volume, this eliminates one of the most time-intensive parts of the underwriting process.

Experience the depth of F2's Excel native reasoning and financial logic architecture. 

Step 4. Document summarization and risk flagging

Many of the risks that matter most to committees are buried inside long, unstructured documents. Traditionally, surfacing the right risks would take an analyst hours. Today, AI reads everything — every line of every contract and every file — and surfaces risk-relevant insights like:

• Margin compression
• Liquidity deterioration
• Customer concentration
• Negative churn patterns
• Contract dependencies
• Tenant rollover risk
• Add back inconsistencies
• Seasonal volatility
• Litigation exposures

These risk flags often incorporate both quantitative signals and qualitative cues derived from documents (contract expirations, non-standard terms, unusual liability structures).

Importantly, the best AI underwriting platforms ensure every single output generated by the AI is cited with the exact page of the source document — or, for financial data, down to the exact cell in a spreadsheet — so that analysts can quickly verify a source’s accuracy and the complete underwriting process can maintain an audit trail.  

Step 5. Covenant design support 

The platform provides credit teams with the analytical foundation necessary to design the right covenant set for a borrower. Beyond automated financial analysis, the platform will draft copy that provides the narrative for analysts to explain the reasoning behind the suggested covenants. The AI-powered rationale may include insight into the borrower’s financial stability and risk areas.    

Analysts spend their time applying judgment to possible covenant designs, rather than manually calculating financial ratios and drafting v1 of the memo copy. 

Step 6. Drafting memos and deal materials

Once the borrower’s data room has been fully classified, extracted, and summarized, the AI can immediately draft:

• Borrower profiles
• Business descriptions
• Financial summaries
• Covenant packages
• Risk and mitigant sections
• Questions for management
• Recommendation options

Analysts can take the first draft of the memo and use AI to begin revisions and further analysis — including rewriting risk summaries, restructuring tables, or adjusting the narrative tone — using simple natural-language prompts.

Step 7. Approval and collaboration

As deal materials are prepared for their next round of review, team members across an organization can collaborate seamlessly in a shared workspace. Partners, IC members, and credit leaders can review the materials and even prompt the AI agent to provide further context or to surface additional information from the data room. 

With version history and redlining capabilities, the platform helps reviewers understand how assumptions, inputs, or conclusions have changed over time, improving investment decision-making. 

AI use cases across credit, banking, and private equity underwriting

While different teams underwrite risk specific to their bespoke security structures, core themes across private markets offer consistent use cases for AI in investment underwriting: fragmented data rooms, speed-to-decision, and analytical rigor. 

Here’s how different teams leverage AI underwriting to make faster, better decisions:

Private credit

Private credit deals often involve bespoke structures, detailed EBITDA adjustments, and recurring reporting. 

AI helps teams standardize financials, evaluate addbacks, design covenants accurately, and monitor ongoing borrower compliance more consistently.

Commercial banking

Commercial banks manage a high volume of borrower applicants across commercial and industrial (C&I) term loans, commercial real estate (CRE) mortgages, asset-based lending (ABL) facilities, and corporate revolvers. 

AI accelerates preliminary underwriting, spreads guarantor and borrower financials, digests appraisals, extracts rent roll data, and prepares credit memos quickly.

Private equity

PE analysts must make sense of massive data rooms under tight timelines. 

AI analyzes contracts, customer files, HR documents, litigation paperwork, and QoE reports, surfacing issues early and reducing time-to-decision.

Key benefits of AI underwriting 

Underwriting teams no longer have to do their jobs alone. With AI-powered underwriting software, teams can deliver higher-quality work and refocus their time on better investment decisions. 

• Up to 75% faster workflows: Teams reclaim hours previously spent on spreading, covenant testing, and document review.
• Improved risk coverage: AI reads every document thoroughly, with the depth that analysts often miss.
• Cleaner, more consistent outputs: Standardized spreads, contextual extraction, and memo structures create credibility with committees and regulators.
• Better internal communication: Teams benefit from clarity on final deal materials, rather than having to double-check terminology or resolve discrepancies across documents. 
• More confident decisions: With cleaner inputs and earlier risk visibility, committees can make stronger, more defensible investment decisions.

Challenges and limitations of AI underwriting

As new AI-powered underwriting software emerges, teams are tasked with vetting the reliability of different tools. 

Here are some limitations you might encounter while diligencing new platforms:

• Data quality varies: While F2 targets high accuracy in the categories tested, other AI platforms may produce inconsistent reports. Before confirming the platform provider's reliability, your analysis should incorporate a human-in-the-loop. 
• Models evolve: Virtual data rooms include evolving document sets. AI systems require ongoing training and quality assurance to extract context across new formats accurately. Prospects should seek out multi-model platforms that can leverage different models based on which are most performant for each task type.  
• Governance is critical: Auditability remains a top concern for underwriting teams. Stakeholders expect transparency into how credit decisions are made — including those powered by AI.

Additionally, AI cannot independently make judgment calls about market dynamics or economic impacts — areas that remain fundamentally human-driven in the underwriting process.

How to choose an AI underwriting platform 

While the promise of accelerated deal reviews and robust underwriting analysis is compelling, there are critical differences among AI platform providers.  

Before you book a demo with a potential platform provider, consider these top factors that can make or break your experience in adopting an AI underwriting solution. 

Source traceability and auditability

AI outputs must be tied directly back to their source:

• Page-level and clause-level citations
• Ability to jump from any insight to the exact document snippet
• Transparent extraction logic

This ensures underwriting decisions can withstand IC review and stakeholder scrutiny.

Data privacy and access control

Data room files contain tax returns, bank statements, customer data, and sensitive contracts.

Your platform provider must implement:

• Strong document permissions
• Encrypted storage and processing
• Strict separation of customer data
• Zero-day data retention and training policies with LLM providers (i.e., platforms that do not allow LLMs to store and use data for training). 

Version history and collaboration

Underwriting is fundamentally an iterative process. Teams need:

• In-platform commenting
• Redlining and change tracking
• Version history that shows when assumptions or inputs were updated
• Shared workspaces reviewers can access before the memo is finalized

These capabilities should replace back-and-forth emails with a single, reviewable source of truth.

Financial analysis depth

Private market investors have a near-zero margin for error in financial analysis. Your platform must be able to extract and calculate metrics with the rigor of an experienced analyst. 

Key capabilities:

• Multi-year spreading across P&L, balance statement, and cash flow
• Automated ratio calculations (DSCR, leverage, FCCR, margins, liquidity)
• Trend detection (declines, volatility, concentration)
• Ability to read and incorporate Excel models directly
• Normalization across inconsistent statements

These are the foundations underwriters rely on to size loans and structure protections.

Contextual understanding beyond financials

Underwriting also depends on dense non-financial documents. 

Your platform should be capable of:

• Summaries of long PDFs
• Extraction of key risks from contracts, customer lists, and bank statements
• Identification of anomalies or inconsistencies
• Context links back to the original source

The system should surface insights that analysts would otherwise miss under tight timelines.

Where AI underwriting is headed in the future

The world of AI underwriting is evolving fast — and the next wave will reshape credit workflows even more deeply.

Agentic underwriting assistants are already helping underwriting teams with end-to-end tasks with extreme precision. 

As models improve, analysts will rely more heavily on AI for portfolio analysis, sensitivity scenarios, peer comparisons, and macro-level risk reasoning — tasks that historically required significant analyst hours.

The shift isn’t toward automation replacing analysts — it’s toward analysts leveraging advanced tools to perform deeper analysis with quicker turnaround times, positioning investment committees to maintain a competitive advantage across bid cycles. 

Conclusion

Underwriting and due diligence have long been defined by manual, tedious work — work that serves more as a distraction to top investors than a core competency.

AI removes that friction.

With a trusted AI underwriting platform that structures information, accelerates analysis, and surfaces risk earlier, teams can focus on what matters most — judgment.

See how F2's agentic architecture handles everything from document intake to IC-ready reports. Book a demo today.