Improving Efficiency in the M&A Process

Last updated: September 2026

What Is M&A Financial Due Diligence?

M&A financial due diligence (FDD) is the process of analyzing a target company's financial records to confirm accuracy, identify risks, and help buyers determine enterprise value. For CPA firms, it's also a race against the clock. The firms that close deals faster, with cleaner data, win more of them.

Key Takeaways

• FDD is one of the most data-intensive, deadline-driven workflows in accounting. Manual data gathering is the single biggest bottleneck.
• FDD teams rely on a core tech stack: virtual data rooms, financial modeling platforms (or simply Excel), and financial data extraction tools.
• Data extraction is the first and most time-sensitive software need in any FDD engagement. Clean data is what every other analysis depends on.
• Crunchafi Data Extraction connects directly to a target's accounting system, pulls and normalizes the full general ledger, and delivers a structured spreadsheet analysts can use immediately.

The State of M&A

Source: Institute for Mergers, Acquisitions & Alliances (IMAA)

While the graph from IMAA shows that M&A deals in the US have declined over recent years, the outlook is positive.

According to EY-Parthenon's 2026 Deal Barometer, corporate M&A deal volume in the US is projected to grow 11% in 2026. In short, there's work out there if a firm can find the bandwidth to take it on. For most firms, that means bringing on more staff. Unfortunately, hiring has been more difficult than ever.

How, then, can M&A financial due diligence firms rise to this challenge? How can they decrease deal time and improve results? How can an already maxed workforce increase throughput and manage more deals as the market continues to expand?

Technology is starting to rise to the complexity of today's deals. With smart tools that can assist with the manual, time-consuming tasks, firms can improve their efficiency for M&A due diligence, increasing both the number of deals they can work on and the margins on those deals. And what firm doesn't want a better bottom line?

Let's look at the M&A process, where the biggest opportunities exist for technological assistance, and what those tools for financial due diligence are.

An Overview of the M&A Process

For a company looking to sell, the M&A process starts with planning and marketing. The company prepares financial documents and other materials to present to a list of prospective buyers.

If a prospective buyer is interested and both sides agree to explore a transaction, due diligence begins. Financial due diligence firms get involved to analyze the target's financial situation, confirming financial details, identifying risks, and helping the buyer determine enterprise value.

M&A financial due diligence involves working with the seller to obtain financial reports and documents, preparing a databook where analysts can identify issues and perform analysis, and presenting findings to the buyer.

The two parties then negotiate, with the FDD report shaping deal terms. When both sides agree, the purchase agreement is signed and the transaction closes.

In practice, this process is a complicated, high-pressure back-and-forth with tight timelines, incomplete data, and a lot riding on the accuracy of the analysis.

Here’s a quick, high-level view of the M&A process:

1. Determine the deal scope with the client, usually the buyer (called the “buy-side” of the transaction).
2. Send a document and data request list to the seller (called the “sell-side” of the transaction).
3. Review documents as they are received, update the request list for missing or incomplete items, and repeat until critical requests are fulfilled.
4. Convert data and documents into an Excel databook to use for analysis, highlighting any questions or items that need to be clarified by the sell-side’s management team.
5. Multiple levels of review of the databook to ensure accuracy and consistency.
6. Meet with the management team on the sell-side.
7. Complete analysis on key scope items, like quality of earnings or net working capital.
8. Convert the databook into a report for the client.
9. Write and send the final report to the client.

Where Does the M&A Process Break Down?

The M&A process is full of potential friction points, but a few consistently slow deals down and compress firm margins:

• Data Gathering From the Target: Getting clean, usable financial data from the target company is the first bottleneck. Analysts spend days, sometimes weeks, going back and forth with the target to collect the right reports in the right format. Every delay here pushes back the entire engagement.
• Manual Data Cleanup and Reformatting: Even when data arrives, it rarely arrives ready to use. Analysts spend significant time reformatting, normalizing, and validating information before any real analysis can begin.
• Inconsistency Across Deals: When data gathering and preparation are manual processes, output quality varies by analyst and by engagement. That inconsistency creates additional review time and increases risk of errors.
• Headcount Constraints: Taking on more deals without adding headcount requires either longer hours or smarter processes. Most firms are still relying on the former. For a deeper look at how staffing pressures are affecting accounting firms, see how technology is helping firms address the talent crisis.

In all of these areas, analysts can spend days, if not weeks, worth of effort, drastically slowing the pace of the deal. Firms should focus on ways technology can help improve in these areas.

What Software Do Financial Due Diligence Teams Use?

FDD is a data-intensive discipline, and the right tech stack makes a measurable difference in deal speed, output quality, and firm margins. Most FDD teams rely on a combination of three software categories:

• Virtual Data Rooms (VDRs): VDRs are secure document-sharing platforms where the target company uploads financial records, contracts, and other deal materials. They're the standard for document management in M&A transactions. Documents in a VDR still need to be extracted, cleaned, and structured before analysis can begin.
• Financial Modeling Platforms: Spreadsheets remain the dominant tool for FDD analysis and databook preparation. Some teams supplement with purpose-built financial modeling software, but the workbook is still the deliverable. The quality of that workbook depends entirely on the quality of the data going into it.
• Financial Data Extraction Tools: This is the first and most time-sensitive software needed in any FDD engagement. Before analysts can model, analyze, or report, they need clean data from the target's accounting system. Data extraction software connects directly to that system, pulls the general ledger and transaction history, and normalizes it into a structured format that analysts can immediately work with.

Of the three categories, data extraction has the highest direct impact on deal velocity. It eliminates the manual request-and-reformat cycle that consumes the early weeks of most engagements.

Financial Data Extraction Tools for M&A Deals

Financial data extraction software does one thing exceptionally well: it gets clean, structured data out of a target's accounting system and into the hands of analysts quickly.

Here's how it works in a typical FDD engagement:

1. The analyst sends the target a secure connection link.
2. The target authorizes access to their accounting system.
3. The software connects, pulls the full general ledger and transaction history, and normalizes the data automatically.
4. The analyst receives a structured Excel workbook
5. What would otherwise take days of back-and-forth and hours of manual cleanup happens in minutes.

This gives FDD teams:

• Shorter Request Lists: When you can pull financial data directly from the source, you're not chasing the target for 40 line items on a document request list.
• Faster Databook Preparation: Normalized data means analysts move directly into analysis. The hours previously spent reformatting spreadsheets get redirected to the work that requires their expertise.
• Better Margins per Deal: Fewer hours on data prep means lower cost per engagement.
• Consistency: Every pull produces the same structured output. That consistency reduces managerial review time, minimizes human error, and makes it easier to onboard junior analysts without sacrificing output quality. Teams also are able to build their due diligence process around a standard data starting point.
• Higher Deal Throughput: When each engagement moves faster, firms can take on more deals with the same headcount.

How Technology Improves the FDD Workflow

The right technology changes the economics of the entire engagement by:

• Reducing analyst burnout. Time-consuming, repetitive data work is a primary driver of analyst burnout in FDD practices. Automating the data gathering and normalization process frees analysts to focus on higher-value analysis.
• Improving output consistency. Manual processes produce variable outputs. Automated data extraction produces the same clean, structured workbook every time.
• Scaling deal capacity without scaling headcount. Automation reduces the hours required per deal, which means existing teams can handle more engagements per quarter.

How Crunchafi Data Extraction Fits Into the FDD Process

Crunchafi Data Extraction is purpose-built for FDD engagements. It connects directly to the target's accounting system, pulls and normalizes the full general ledger and transaction history, and delivers a structured Excel workbook that analysts can use immediately.

No manual data entry. No reformatting. No chasing the target for reports they're not sure how to run.

The databook components are automatically produced with each pull. That consistency reduces review time, minimizes errors, and makes it easier to scale deal volume without adding headcount.

For FDD teams evaluating data extraction tools, Crunchafi's financial due diligence solution is built specifically for this workflow, not adapted from a general-purpose data tool.

If you want to reduce burnout for your team, grow your deal capacity without adding headcount, improve consistency in your output across the team, and reduce time spent on each deal, leading to better margins, Crunchafi’s Data Extraction software is the answer. Our customers report reduced deal time and hours saved on mundane, manual data manipulation tasks.

Interested in incorporating leading technology into your process? Reach out to book a demo today!

Frequently Asked Questions

What is M&A financial due diligence?

M&A financial due diligence is the process of analyzing a target company's financial records to verify accuracy, identify risks, and help the buyer determine enterprise value. It typically involves data gathering from the target, preparation of an Excel databook, and a findings report presented to the buyer.

What does an FDD team do?

An FDD team analyzes the target's financial statements, transaction history, and accounting records. They identify quality of earnings adjustments, working capital trends, and financial risks that inform deal terms and valuation.

What is the biggest bottleneck in M&A financial due diligence?

Data gathering from the target is consistently the first and most significant bottleneck. Analysts often spend days or weeks collecting financial reports in the right format before any analysis can begin. Financial data extraction software eliminates most of this friction by connecting directly to the target's accounting system.

What is financial data extraction software?

Financial data extraction software connects to a target company's accounting system or ERP, pulls the general ledger and transaction history, and normalizes the data into a structured format analysts can immediately work with.

How does data extraction software speed up M&A deals?

By automating the data gathering and normalization process, data extraction software compresses the early stages of an FDD engagement from days or weeks to minutes. Analysts receive a clean, structured workbook immediately and can move directly into analysis without reformatting raw data.

Can data extraction software reduce analyst burnout?

Yes. Manual data gathering and reformatting are primary contributors to analyst burnout in FDD practices. Automating these tasks frees analysts to focus on higher-value analysis work.