Bank M&A: Data-Driven Insights

Over the last several years, the possible consequences of deploying artificial intelligence (AI) or AI-based methods in the legal industry, including consequences on the employment of human lawyers, have received a fair amount of “air time.” Yet, with the post-Covid-19-driven, frenzied bid for lawyers as evidenced by pandemic bonuses and increased associate salaries, among other perquisites customary of tight labor markets, we can have confidence that the deployment of AI-based methods in the legal industry will not yield negative consequences, or dire outcomes for lawyers.

In fact, given the evolution of AI-based techniques and the proliferation of potential use cases for those techniques in the legal industry, clients, lawyers and other legal professionals alike can, and should, genuinely be enthusiastic about how the nature of our work has already changed and about the promise of how our work will continue to change across all practice areas – from litigation to transactional practices and all practice areas in between.

As we look at the AI-based techniques that are available to address or solve data-driven questions or problems, we see that some techniques are designed to enhance productivity, and some techniques are designed to provide intelligence.

Productivity-Based Applications.

How can we sift through a terabyte worth of various types of documents to find those documents that meet certain criteria that are important to our client and our team?

Many AI-based programs can produce an indication of how one clause is similar to another according to a user-defined set of characteristics.?For example, some of these productivity-enhancing applications are now routinely used to conduct diligence in M&A transactions or to find LIBOR-based clauses in the years-long, mandated shift away from LIBOR in the wake of British Bankers Association’s LIBOR-fixing scandal.

Intelligence-Enhancing Applications.? What actionable insights can we draw from the data inherently present within a set of documents, all to help our clients and teams make better, more-informed decisions??

Can we, for example, derive insights that can help our client negotiate a particular type of agreement?

Bank M&A

As an example of intelligence-enhancing applications, here’s just one set of insights, taken from our 2019 database, that have been gleaned from Attune’s “What’s Market” transaction analytics system.

The deal volume and aggregate dollar value of Bank M&A has been, and continues to be, consistently sizeable on a year-over-year basis. According to S&P Global: In 2019, 258 deals with an aggregate deal value of $55.05 billion were announced. In 2020, 112 deals with an aggregate deal value of $27.67 billion were announced. The aggregate deal value in 2019, for sure, was a deviation from the longer-term trend as the 2019 aggregate deal value reflects an extremely sizeable transaction announced in February 2019 – the acquisition of SunTrust Banks Inc. by BB&T Corp. for approximately $28.3 billion, which was and continues to be the largest Bank M&A deal in over a decade.

Let’s drill down and take a closer look at the Bank M&A in 2019 given that it was a banner year.?Specifically, let’s consider “What’s Market?” for the Material Adverse Change (MAC) clause for Bank M&A deals in 2019 using the AI-driven methods built into Attune’s “What’s Market” transaction analytics system. ?What Attune’s analytics reveal is that, on a deal-value-weighted basis, the typical MAC clause looks substantially similar to MAC clauses used in the following two transaction agreements.

• TCF Financial – Chemical Financial Merger Agreement (drafted and negotiated by (I) Lee Meyerson and Sebastian Tiller of Simpson Thacher and J.P. Morgan Securities, on behalf of TCF Financial, and (II) John M. Jennings and Aileen L. Nagy of Nelson Mullins and Keefe, Bruyette & Woods, on behalf of Chemical Financial), which is available here https://www.sec.gov/Archives/edgar/data/19612/000119312519092935/d694529ds4.htm#toc694529_116. [Deal Value, $3.6 Billion; Announced, January 28, 2019]
• §?BB&T Corp.-SunTrust Merger Agreement (drafted and negotiated by (I) Edward D. Herlihy and Matthew M. Guest of Wachtell, Lipton, Rosen & Katz and RBC Capital Markets, on behalf of BB&T, and (II) H. Rodgin Cohen and Mitchell S. Eitel of Sullivan & Cromwell and Goldman Sachs, on behalf of SunTrust), which is available at https://www.sec.gov/Archives/edgar/data/92230/000119312519069529/d688076ds4.htm#rom688076_143. [Deal Value, $28.3 Billion; Announced, February 7, 2019]

The TCF-Chemical deal was announced on January 28, 2019, and the BB&T Corp.-SunTrust deal was announced on February 7, 2019. And, perhaps, not surprisingly, the MAC clauses included in the respective merger agreements for these deals are substantively similar, or nearly identical.?See below for each of the MAC clauses as well as a blackline comparing the two MAC clauses.

None of us knows the factors that may have affected the deal making process or decision-making of the business and legal dealmakers, who must have been acting independently, for these two deals. Yet, the data available through Attune’s analytics system, suggest several questions and observations, including those below, among other questions and observations:

1. Understanding “What’s Market?” for deal terms used within specific industries can be helpful as there are likely to be more commonalities among deal terms in a particular industry than among deal terms across industries, taken together, particularly considering the above example.
2. In 2019, how did two separate groups of veteran dealmakers in the Bank M&A space get to nearly the same point on two completely separate, material deals (with an aggregate deal value of almost $32 billion), with both deals being announced within about two weeks of each other? Market forces shape not only deal values but deal terms.?Or, could it be that, in 2019, the TCF- Chemical dealmakers set the market for the MAC clause in the Bank M&A space and the dealmakers on the BB&T-SunTrust deal took the TCF-Chemical deal as precedent?
3. How prevalent are standard clauses and standard agreements in negotiated M&A agreements within industries or subsets of industries, and how can clients – even clients of sophisticated M&A advisory services – benefit from the use of such standard clauses and agreements when engaging in M&A deals, whether those clients are strategics in the Bank sector like BBT, SunTrust, TCF and Chemical, or those clients are private equity firms aiming to minimize transaction costs, for the benefit of upstream L.P.’s, as the PE firms buy and sell companies as part of the funds they establish and build?

This is just one example of the many data-driven insights that can be extracted from the rich sources of data that exist on M&A across industries. If you are interested in learning more about how we combine AI-based techniques and “big data” for use cases in the legal and information industries or about how we glean insights from “big data” across the legal industry and other industries, please reach out to us. We love to combine AI-based techniques and “big data,” and we love to puzzle through the insights those combinations reveal!

Karen M. Suber, Esq. and John Giraldi, Ph.D., New York, New York

A special thank you to Zuhaib Gull and Ali Shayan Sikander of S&P Global, who have maintained, and continue to maintain, the Bank M&A Deal Tracker. See, e.g., https://www.spglobal.com/marketintelligence/en/news-insights/latest-news-headlines/bank-m-a-2020-deal-tracker-10-deals-announced-in-december-2020-57037182.