Earlier this year, we discussed the increasing use of technology in the M&A deal process. To recap, a recent Mergermarket study revealed that the use of technology and big data were likely factors in the increasing frequency of unsolicited bids and corresponding decrease in frequency of broad auctions. Building on our earlier discussion, we now consider below the ways in which technology is used to facilitate deals.
Due diligence process
Artificial intelligence has already had significant influence on the due diligence process. For example, Kira Systems, an artificial intelligence contract analysis company, claims that their software has been used on over $100 billion worth of transactions. In a recent article, Bill Stoffel, US Private Equity Leader at EY commented that the amount of competition in the market has made the speed of completing diligence critical, with many diligence periods lasting two or three weeks.
Noah Waisberg, co-founder of Kira, claims that his company’s Diligence Engine can reduce 6½ hours of work by two junior lawyers to 2½ hours of work by one junior lawyer. Kira’s Diligence Engine uses machine learning algorithms to analyze the language within a collection of contracts or other documents to identify provisions dealing with a specific issue.
More recently, firms have started to deploy artificial intelligence as a way to identify potential acquisition targets. This application of artificial intelligence involves running collections of data through machine learning algorithms to identify factors that increase the likelihood of a successful deal. The Aingel platform claims to do just that. Founded after over a year of research at NYU, the platform scores potential targets on their likelihood of a successful exit by analyzing data from a database of past deals and their outcomes.
In a recent interview with McKinsey & Co, Managing Partner of Hone Capital Veronica Wu described how her firm developed a machine learning model to identify potentially successful targets on AngelList. In particular, their model found that start-ups with an average seed investment of $0.5 million generally failed. Interestingly, the model also uncovered that start-ups whose founders’ university degrees came from different schools were more likely to advance to a series A round of financing.
While it may be unlikely that deals are won or lost based on a founder’s alma mater, artificial intelligence can be used to provide insight into companies that is not always apparent to the naked eye.
The author would like to thank Daniel Weiss, Summer Student, for his assistance in preparing this legal update.
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