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How Innovative Machine Learning Makes M&A Better

Kison Patel

Kison Patel is the Founder and CEO of DealRoom, a Chicago-based diligence management software that uses Agile principles to innovate and modernize the finance industry. As a former M&A advisor with over a decade of experience, Kison developed DealRoom after seeing first hand a number of deep-seated, industry-wide structural issues and inefficiencies.

CEO and Founder of DealRoom

Machine Learning for M&A

If you work in M&A, you know due diligence is a costly and time-consuming process.

Traditional due diligence is completed through data rooms, spreadsheets, and emails. These outdated and clunky processes waste thousands of hours because each tool works independently. The lack of cohesion and absence of automation leads to poor diligence. This can be disastrous. 

Good news.

There’s a solution to these inefficiencies.


virtual data room machine learning

After discovering how inefficient traditional due diligence is, we developed a lexical analyzer, meaning extractor, and document classifier all-in-one. In short, our scientists created machine learning for M&A.

In this article, we’ll focus on what this supervised machine learning can do for M&A professionals.


At the core of our machine learning is an M&A language database. This database was created by acquiring enormous amounts of M&A-related words and phrases. We used to scrape EDGAR and access a huge database of M&A cases.

In turn, our scientists developed an algorithm that analyzes this database, extracts meaning, and categorizes documents. The results produced by the algorithm are then translated into recommendations for you: buyers, sellers, and advisors.


To give you a better feel for what our AI can do, let’s explore a single feature. The project management tool includes a ‘Recall’ feature. With ‘Recall’, the machine learning suggests documents that are relevant to your specific requests. Rather than mining through data rooms for the right information, you put in a request or question. In response, our machine says, “Hey, here’s what you’re looking for!”. The more you use this feature, the more accurate the suggestions become. Essentially, the software does the administrative tasks so you can focus on value-add activities.


Here’s the nitty gritty.

After collecting enormous datasets from different contexts, we extracted all possible meanings. “Meanings” is key here. By analyzing different meanings, rather than solely focusing on common words, our algorithm is capable of finding the most appropriate documents. If we only analyzed common words, the search results would include irrelevant recommendations. Instead, the natural language processing algorithm finds documents that actually make sense given your request.

Moral of the story: you will be directed to the most relevant responses and documents.


buyer due diligence requests

Let’s look at an example.

Say you are a seller working with multiple bidders. In March, human resources from Company A sends you a question. You reply with a detailed answer. In April, the marketing department from Company B sends the same question. Instead of preparing separate responses for each request, our machine learning recommends a response based on your previous activity. This saves you from doing the same work twice. 

Let’s take this one step further.

Company A sends you a question and you answer with one response. Company B sends the same question and you fail to provide the same exact information. After the sale, Company B claims you misrepresented information. Recommended responses and recent files help prevent these legal implications. Every player is on the same page, sharing a uniform message with each bidder.

Now, let’s say you’re on the buy side. You work in the marketing department at Company A. You don’t normally collaborate with human resources and are unsure of the role they play in due diligence. Human resources from Company A already sent the seller a request. Typically, you would unknowingly send a duplicate request and wait for an answer (you already have). This inefficiency is eliminated. Since your requests are kept in one centralized location, you are aware of HR’s request and can easily access the response they already received.


AI makes M&A easier. It automates inefficient and outdated processes. On the buy side, AI minimizes duplicate work and makes sharing information across functions easy. On the sell side, it reduces the workload for sellers and advisors by helping them respond more efficiently to requests.

With an M&A Project Management Tool, the entire deal process is centralized into a single workflow. This advances the virtual data room by replacing spreadsheets with a centralized information hub. Slow response times, duplicate work, unsecure documents, and ultimately, inefficient and risky due diligence have no place in M&A. Our machine learning solves these common problems. When it comes down to it, we’re not changing anything about how you work.

We're giving you the tools to work faster, better, and more efficiently. We created machine learning to advance M&A. 

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DealRoom is a secure, cloud-based M&A due diligence management platform that replaces overpriced data rooms, email clutter and manual spreadsheet trackers.

Contact M&A Science to learn more

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