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M&A due diligence with AI: from months to weeks?

Updated: 2 days ago

Buying a company or engaging in a significant investment entails a thorough investigation of the target’s financial health, operational efficiency, legal compliance, and potential risks. Bankers, consultants and lawyers traditionally spend up to 70% of their time on due diligence with most of that time allocation going to digging into the data room. This makes the process a labor-intensive, time-consuming and a costly effort for many people involved in the deal.

 

Using AI and more specifically Large Language Models (LLMs) are transforming this process by making it more efficient, accurate, and insightful. The way-of-working during an M&A due diligence is also likely to change significantly. Where the emphasis previously was on the thorough examination of as many documents as possible in as much detail as doable in as little time as available, it will now shift to more emphasis on asking the right questions. We have referred to this phenomenon as ‘The Einstein transformation of due diligence’ following the famous quote: knowledge is more important than knowledge


NYC skyline financial district

 

below, we explore the various phases of the process where AI enhances due diligence in M&A transactions.


Phase 1: Preparation - Data collection and selection by the Seller

 

One of the most significant challenges in due diligence is collecting and processing large amounts of data from various sources before they enter the data room. AI is extremely good at indexing and summarizing information from a vast amount of data sources. This significantly reduces the time and effort required to get to initial insights. Typically, this process happens outside of the Virtual Data Room (VDR) and then the final selection of information is being uploaded by the seller.

 

Phase 2: Populate - The Virtual Data Room (VDR) by the Seller

 

In our recent post about VDRs, we explained how these secure online repositories for document storage and exchange can facilitate the deal process. VDRs balance the need for information openness with restricted access on a need-to-know basis. AI enhances the VDR functionality through automated document categorization & privacy redaction once the documents are uploaded into the VDR.

 

Phase 3: Analysis - Deep diving into the provided information by the buyer

 

Now that the information is in the VDR, the Due Diligence process starts with the review of the thousands of pages of contracts, financial statements, compliance reports and other documents by the buy-side of the transaction. Often lawyers and M&A consultants appointed by the buyer break their heads plowing through the many documents in a virtual data room.

 

To make things even more complex, very often data from the data room needs to be compared to information from somewhere else. Think about comparable financial data from comparable players in the industry, verification of legal filled documents, fiscal documentation and many, many more.

 

Interpreting large volume of information can be easily done by AI both in and outside the data room. Analyzing contracts and legal documents to identify key clause like: change of control clauses, unfavorable obligations, and potential liabilities. A popular technology that is being adopted is RAG (Retrieval Augmented Generation), a subfield of Large Language Models (LLMs), which makes such contextualized search possible near real time which saves lots of time. This allows lawyers and analysts to focus on top insights rather than getting bogged down in document review.

 

Besides AI Search, LLMs are good at distilling and summarizing financial statements, legal documents, market reports, and other relevant sources. These models then consolidate this information into comprehensive summaries or even into a database. This is lifesaver when comparing data in the data room with data from outside.

 

Phase 4: Valuation - Financial due diligence & price estimate by the buyer

 

Traditionally countless hours are spent sifting through financial statements, verifying data, and identifying potential financial risks.

 

Automated data extraction and processing by AI makes a big difference. But that’s not all. In the end, financial due diligence is all about carefully analyzing and validating a company's financial health. AI-boosted financial analysis can include things such as: predictive modeling and cash flow forecasting, scenario analysis, trend and ratio analysis, benchmarking, and ultimately help with building and validating the valuation model. Here again RAGs, LLMs can extract, compare and contextualize datapoints in minutes instead of days.

 

Another area where AI can be leveraged is enhanced financial risk identification. AI can detect unusual patterns or anomalies in financial data, such as irregular revenue recognition or unexpected expense spikes, which may indicate potential risks.


 

Conclusion

 

The integration of AI, particularly Large Language Models (LLMs) and Retrieval Augmented Generation (RAG), is revolutionizing the due diligence process in M&A transactions by significantly improving efficiency, accuracy, and insight generation in all phases of dealmaking. While the human aspect of M&A will always remain key, the addition of Artificial Intelligence will enhance the process significantly.


Are you ready for the future of due diligence with AI?


If you want to learn more about how your firm can leverage AI for a faster and quality due diligence, feel free to get in touch.


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