AI for commercial loan underwriting: How underwriting teams can accelerate decisions with greater conviction

In commercial banking, underwriting teams have their hands full — toggling between borrower and guarantor financials, performing collateral analysis, and identifying the risks across all three. 

The initial screening process is messy, particularly when applicants submit Excel files built by their intern or screenshots of their QuickBooks account. But technology has advanced, and the commercial loan underwriting process no longer requires analysts to spend hours reconstructing financials, calculating global DSCR, or reconciling debt schedules.

AI for commercial banking automates mundane manual tasks and provides analysts and underwriting teams with the insights they need to focus on borrower quality, guarantor strength, and investment decisions with greater conviction — while adhering to strict compliance and reporting requirements.

Below, we’ll explain how AI fits into the commercial loan underwriting process, how it changes day-to-day workflows, and how leading banks use it to accelerate credit decisions. 

The manual work of commercial loan underwriting 

The current loan underwriting process involves as much investigative work as financial acumen — analysts spending hours combing through multiple entities to uncover risks and reconciling missing or incomplete documents with duplicate Excel sheets and upside-down PDFs. 

Before reaching a refined output where decisions can be made thoughtfully, underwriting involves hours of low-value work just to make sense of an application. 

• Document collection: Borrowers upload tax returns, financial statements, rent rolls, guarantor statements, and appraisals. Documents arrive in inconsistent formats, often requiring analysts to clean, reorganize, and rename files.
• Financial spreading: Analysts map revenue, expenses, balance sheet line items, cash flow adjustments, and EBITDA addbacks into a standardized chart of accounts. This is one of the most time-consuming steps in commercial loan underwriting.
• Guarantor analysis: Guarantor statements require manual extraction of income, liquidity, contingent liabilities, and entity ownership, often buried inside multi-page PDFs and tax schedules.
• Ratio testing (DSCR, FCCR, LLCR): Analysts calculate project-level and global DSCR, FCCR, and LLCR, leverage and liquidity metrics, and stress-case scenarios that inform risk ratings and covenant packages.
• Collateral evaluation: For C&I credits, collateral schedules need to be normalized. For CRE loans, analysts must pull data from rent rolls, T-12, and appraisals.
• Risk rating calculation: Risk ratings require multiple underwriting inputs scattered across spreadsheets.
• Credit committee materials: Memo drafting requires analysts to repeatedly rewrite the same sections, rebuild charts and tables manually, and cite the original source data.

Each of these steps delays the decision-making process and introduces human error. AI enables teams to process borrower applications in a fraction of the time with full accuracy and deeper insights that would be out of reach with manual due diligence. 

What AI means for the commercial loan underwriting process

AI commercial loan underwriting is a system designed to automate the analytical and administrative tasks that slow analysts: consolidating borrower and guarantor financials, reconstructing global cash flow, parsing multi-entity ownership structures, reviewing collateral documents, and drafting credit materials with source-level accuracy.

Importantly, AI underwriting helps banks stay compliant by assisting with the creation of standardized, auditable documents that meet applicable rules.  

The system operates through a coordinated, agentic workflow that first identifies the tasks to be done and then instructs specialized sub-agents to execute them using tools designed to extract messy documents and perform complex financial analysis.

These capabilities include:

• Advanced spreadsheet-intelligence models that interpret complex Excel workbooks — following formula chains, resolving merged cells, handling multi-entity tabs, and rebuilding borrower and guarantor cash flows with cell-level precision.
• Document-extraction engines that pull quantitative and qualitative signals from tax returns, financial statements, rent rolls, appraisals, and guarantor packages — automatically tying each value back to the exact page, cell, or line item.
• Full-context retrieval layers that reason across the entire borrower submission — allowing the system to connect guarantor liquidity to borrower DSCR, link rent-roll results to T-12 NOI, or align owner K-1 income with global cash-flow calculations.
• Summarization and trend-analysis modules that generate borrower profiles, margin and liquidity trends, guarantor financial summaries, collateral observations, and preliminary risk narratives — all source-linked for verification.

The result is an underwriting system that turns fragmented borrower and guarantor packages into clean, structured, analysis-ready outputs — allowing analysts to spend their time reviewing the credit story, challenging the outputs, and designing the right loan structure, rather than assembling these foundational insights from scratch.

How AI accelerates the commercial loan underwriting workflow

AI accelerates the commercial loan underwriting process by reconstructing financials, extracting guarantor and collateral data, surfacing risks, testing covenant structures, and generating credit committee-ready outputs. Analysts are freed from tedious, low-impact work to focus more on the application's merits and the borrower’s creditworthiness. 

Automating borrower and guarantor financial spreading

AI handles the full construction of borrower and guarantor financials — including multi-entity structures and personal guarantees — with audit-ready precision.

The system automatically:

• Identifies every document type (1065, 1120, 1040, Schedule C/E/K-1, internally prepared financials, liquidity statements, bank statements) the moment files are uploaded.
• Extracts line items from PDFs, scans, and Excel using F2’s purpose-built financial analysis engine.
• Normalizes borrower and guarantor financials into the bank’s exact chart of accounts, even when accountants use inconsistent labeling.
• Reconstructs global cash flow using F2's Excel-native reasoning engine by linking guarantor income, liquidity, distributions, contingent liabilities, and Schedule E pass-through income directly into a consolidated global DSCR model.
• Understands multi-entity ownership chains by interpreting K-1s, tracing flow-through income, and automatically identifying cross-ownership.

Analysts use this foundation to validate outputs in F2’s Audit Mode, adjust assumptions, immediately recalculate core metrics, and request alternative financial summaries (e.g., rebuild global cash flow using only recurring income).

Accelerating collateral analysis for commercial and industrial (C&I) loans and commercial real estate (CRE)

Collateral analysis is time-consuming because analysts must surface rent rolls, accounts receivable, and inventory schedules, and verify the bank’s eligibility rules before they can assess the collateral's strength and its impact on the loan structure. 

AI transforms collateral review from a multi-hour task into a structured, verifiable output.

For C&I loans, F2 automatically:

• Extracts AR aging schedules and identifies concentrations, stale receivables, and potential ineligibles.
• Pulls inventory balances and flags valuation swings, turnover slowdowns, or obsolescence patterns.
• Builds borrowing-base-ready summaries using lender-defined eligibility rules.

For CRE loans, the system:

• Extracts rent rolls from PDF or Excel (tenants, lease terms, expirations, escalations, CAM, occupancy).
• Normalizes T-12 operating statements into clean NOI schedules.
• Computes DSCR, debt yield, and stabilized vs. in-place NOI using bank-specific definitions.
• Flags collateral inconsistencies such as mismatches between the rent roll, appraisal, and T-12.

Once collateral data is automatically extracted, normalized, and reconciled, analysts can focus on evaluating collateral quality, adjusting assumptions, and deciding how advance rates, covenants, and structure should reflect the true risk of the asset base.

Surfacing borrower, guarantor, and collateral risk signals

Risk signals are buried in documents and footnotes, often too nuanced for analysts to contextualize, given the limited time they have to synthesize their findings for review. 

AI serves as an early-warning system that sees deterioration trends before analysts would typically catch them manually.

The platform identifies a variety of risk indicators for borrowers (margin compression, liquidity decline, or working capital strain), guarantors (inconsistent recurring income, diminishing liquidity, or escalating contingent liabilities), and collateral (tenant rollover spikes, valuation discrepancies, or repair and maintenance spending).

Every risk flag is source-linked in F2’s Audit Mode, allowing analysts to validate the signal in seconds rather than digging through 200+ pages of documents.

Drafting credit committee-ready memos and risk ratings

The platform uses its full context across the borrower’s entire application to generate a thorough credit memo and risk ratings, allowing teams to interact with the generated drafts using version control, collaboration tools, natural language prompting for iterative analysis, and export capabilities. 

Here’s what you can expect in your draft materials:

• Borrower overview and business description
• Guarantor strength analysis
• Collateral schedules (C&I & CRE)
• Global cash flow & DSCR analysis
• Covenant recommendations
• Risks and mitigants
• Policy exceptions
• Final recommendation section

Analysts can interact with the generated materials, adjust risk and mitigant language to align with credit policy, verify sensitive claims through source-level auditability, and instruct the system to regenerate charts or tables when assumptions change. Instead of manually making these changes, analysts spend their time synthesizing the most important aspects of the borrower’s profile, enabling the firm to make better-informed credit decisions. 

How commercial banking teams should choose an AI underwriting platform

Not all AI underwriting platforms are created equal — some systems rely on generic LLMs that lack the analytical and reasoning abilities of vertically trained models, while others are built to automate only specific tasks. The tools that you need should be able to execute any task that your underwriting team would perform while screening an applicant with the speed and rigor that both accelerates your workflows and generates the insights you need to make informed investment decisions. 

When evaluating platforms, commercial banking teams should prioritize systems that can:

Handle borrower and guarantor packages across all formats

Underwriting files rarely arrive clean — tax returns, internally prepared financials, personal statements, rent rolls, appraisals, and guarantor K-1s come in mixed formats. The platform must ingest all of them, classify automatically, and reconcile inconsistencies across reporting periods.

Reconstruct global financials and bank-specific ratios

Banks rely on project DSCR, global DSCR, FCCR, liquidity metrics, leverage, debt yield, and NOI, all defined internally. The platform must follow spreadsheet logic, extract footnotes, and calculate these ratios with audit-grade precision.

Automate collateral analysis for C&I and CRE

For C&I, the system should extract AR aging and inventory listings, and build borrowing-base schedules in accordance with the bank’s eligibility rules. For CRE, it must parse rent rolls, normalize T-12s, compute in-place and stabilized NOI, and reconcile appraisals.

Draft credit memos and risk ratings

The platform should assemble borrower overviews, guarantor summaries, collateral sections, risks/mitigants, covenant recommendations, and final approval rationale — all source-linked for internal review and regulatory examiners.

Provide complete auditability

Every number, paragraph, ratio, and statement must be tied back to the exact cell or page from which it came. Without granular audit trails, AI outputs cannot support OCC, FDIC, or internal credit policy requirements.

Next steps for commercial banking teams adopting AI underwriting

If you’re ready to bring automation into your underwriting process, here’s what you can do:

1. Audit your workflow: Identify where analysts spend the most time on manual tasks.
2. Select 2–3 high-volume, low-judgment tasks to automate: spreading, document extraction, collateral scheduling, and memo drafting are ideal starting points.
3. Book a workflow demo with F2: See how borrower files, guarantor packages, rent rolls, T-12s, and tax returns are extracted, synthesized, and used in analysis before generating high-grade outputs. 
4. Provide sample memos and risk-rating formats: F2 can configure outputs to match your bank’s templates, policies, ratio definitions, and style guidelines.
5. Pilot on a live deal: Compare the AI-augmented workflow to your manual process — most banks see up to 75% underwriting-time reduction with improved accuracy and fully sourced documentation.

Conclusion

AI enables commercial loan underwriting teams to stop rebuilding spreadsheets and start evaluating applicants with a strong baseline and expert-level insights that otherwise are often missed. Banks using AI today reduce workflow times, flag risks earlier, and produce more consistent credit committee materials —  giving lending teams the time and clarity they need to make stronger, faster credit decisions.

Book an F2 workflow demo.