Solving the messy data problem: How AI normalizes private company financials

Public market investors live in a world of order — SEC filings are standardized, data is structured, and XBRL tags ensure that performance metrics mean the same thing, every time. The data is audit-ready before it ever hits your desk.

But in the world of private credit or commercial banking, the story is not quite the same. 

A borrower application is rarely submitted as a neat data room. More often, you’re looking through a ZIP file containing mismatched PDFs, screenshots of bank statements, scanned tax returns, and Excel models broken by circular references.

For lenders, this lack of standardization is the single biggest barrier to scale and rigor. You cannot automate the commercial loan underwriting process if you cannot first normalize the data. Before a risk rating can be assigned, an analyst must first make sense of the data room. 

This article explores how AI is solving this input problem by acting as an intelligent translation layer between a borrower’s application and the firm’s underwriting workflow requirements.

The anatomy of an unstructured data room

In a typical middle-market deal, the data room is a fragmented, disorganized repository of anything the borrower chooses to submit. It often contains master folders and subfolders with files that may be hundreds of megabytes in size, and lacks any consistent hierarchy.

The volume and naming problem

Borrowers rarely follow a naming convention. An analyst might open a folder to find files named Scan_123.pdf, Financials_Final_v3.xlsx, and Q3_Update_OLD.pdf. As the underwriting process progresses, the data room grows, and files are often shared with the lender across different channels.

To make sense of this, the analyst must open each file, determine its type, rename it, and move it to the appropriate internal folder. This manual triage creates friction that significantly slows the underwriting process — and is far from the high-impact tasks for which investment teams are hired. 

The format mix

The second hurdle is the file format itself, which is usually a combination of static PDFs, complex Excel files, and a slew of ancillary scans, decks, and image files. 
 

An analyst must mentally stitch these disparate sources together to form a cohesive view of the company’s health, a tedious process that can take hours of analysis, before an analyst can begin making sense of the borrower. 

Step 1: Automating ingestion and classification

The first step in modern financial spreading is organizing the incoming applicant materials. When a data room is uploaded to a platform like F2, the system's first job is to create a structured workspace.

Beyond OCR: Identifying context within the data room

Traditional OCR tools fail here because they try to read text without context. An AI-powered ingestion engine, however, scans the entire directory structure. It opens files, reads their contents, and automatically classifies them based on context.

The system instantly distinguishes between, for example, an old tax return and a fundraising deck, regardless of the file's actual name. It groups related documents and resolves duplicative versioning issues. This eliminates the administrative burden of organizing folders, allowing the analyst to start with a structured view of the borrower immediately.

Interviewing the data room for missing materials 

One of the most powerful capabilities of an intelligent system is knowing what’s missing for your analysis. In a manual workflow, an analyst might spend three hours building a model only to realize they are missing the T-12 financials for the current period.

Because the AI has indexed the entire data room, the analyst can quickly review the data and ask F2’s chatbot questions like, "What materials are missing for me to perform an LBO?”

If any files are missing, the deal team can pause the clock and go back to the borrower immediately, rather than discovering the gap two days later. It prevents the team from wasting time on a deal that isn't ready for underwriting.

Step 2: Translating borrower logic via normalization agents 

Once the files are identified, the data must be spread — a task that would be simple if all data were uniformly classified and formatted. Unfortunately, we know that is rarely the case. The raw borrower data must be normalized to your firm’s standardized chart of accounts. 

Every borrower has their own internal accounting logic. For example, one might group marketing, sales, and travel expenses under a single SG&A line item, while another lists them as three separate lines. 

For a lender to perform portfolio-wide analysis, this data must be translated into a common language.

The agentic approach to financial mapping

F2 uses specialized normalization agents to handle this mapping.
 

1. Extraction: The agent extracts the raw line items from the source document.
2. Contextual understanding: It analyzes the line item's context, surfacing the financial intent. 
3. Standardization: It maps these raw items into your specific chart of accounts. This capability is critical for lenders in middle-market deals, where financials may be poorly formatted or not CPA-prepared. Moreover, F2 identifies gaps where the data might be wrong and rebuilds an accurate spread rather than relying on the borrower’s potentially flawed presentation.

This normalization layer enables financial spreading software for lenders to deliver consistent outputs across thousands of borrowers with inconsistent data.

Cross-portfolio querying with normalized data 

Normalized data enables portfolio-wide analysis at a scale and speed that manual workflows cannot match. Because every borrower’s financials have been mapped to the same standard template, analysts can run cross-portfolio queries instantly, with the help of F2.

This also allows analysts to start their diligence one step ahead of what they’re used to — by comparing normalized data from past deals, analysts can triage new opportunities quickly. 

In a manual environment, a question as simple as, “Create a table showing EBITDA over time for these three companies," would require opening three different Excel files, checking how each one defines EBITDA, locating the right tabs, and manually copying the data into a new summary sheet. With normalization agents, the data is already structured, allowing for instant benchmarking and trend analysis.

This capability is particularly useful for monitoring macro risks. For example, an analyst could ask: "Based on all the companies in my portfolio, what will happen if the US puts a 100% tariff on Chinese steel next week?" The system can analyze normalized data to identify which companies are exposed to those specific cost inputs.

How deal teams use normalization agents to reclaim their time and make decisions with greater conviction 

A primary bottleneck in the underwriting process is the misallocation of talent toward low-leverage tasks — specifically, the manual translation of messy borrower accounting into a standardized, usable format. Whether managing a high-volume commercial funnel or a concentrated private credit portfolio, the goal of normalization is to ensure analysts spend their time on judgment rather than data entry.

High-velocity review for commercial lenders

For commercial banks processing hundreds of applications per month, AI-driven normalization reduces the cost of rejecting deals.

• Standardized filtering: Normalization enables banks to instantly apply internal risk hurdles to non-standard borrower reports, immediately identifying whether a prospect meets minimum debt service coverage ratio (DSCR) or liquidity requirements.
• Operational efficiency: By automating the translation layer, banks can generate a screening memo in minutes. This ensures credit teams focus only on deals with a high probability of fitting the bank's specific credit box, rather than manually mapping data for a loan they will ultimately decline.

Deep-conviction underwriting for private credit

For private credit teams, the priority is executing robust analysis and gaining a competitive edge in bid cycles.

• Benchmarking and peer analysis: Because the AI maps every borrower to the same standardized chart of accounts, analysts can instantly compare a target company's performance against the existing portfolio. 
• Macro risk sensitivity: Normalization transforms a static data room into a queryable asset. Analysts can perform second-order analysis by asking how a change in the economy would affect borrowers in the portfolio with those specific exposures.

Conclusion

Private market data has long been a barrier to high-impact, thoughtful decision-making. Analysts have resigned themselves to being data administrators, spending their days renaming PDF files and manually mapping line items.

But with the emergence of agentic AI, we can finally decouple the quality of the borrower's reporting from the underwriting speed and rigor. By automating ingestion, classification, and normalization, lenders can turn a raw data room into a credit memo in minutes.