Tag: risk

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The Clause Tracking Conundrum

I have to admit that “clause tracking” sounds good. Let’s analyze how many deals contain our preferred limitation of liability clause. Let’s track how many times we had to use fallback #2. It feels like these would be really useful things to know. Which is probably why so many contract managers ask for it.

Not Quite a Limitation of Liability Clause…

But as you peer behind the shiny facade of clause tracking, things start to look a little grimy. To explain why, I’m going to use a real life example that I recently plucked from the EDGAR database:

10.  Limitation of Liability. IN NO EVENT SHALL EITHER PARTY BE LIABLE TO THE OTHER FOR EXEMPLARY, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES, ARISING FROM OR RELATING TO THE AGREEMENT, WHETHER IN CONTRACT, TORT (INCLUDING NEGLIGENCE) OR OTHERWISE, EVEN IF SUCH PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES; PROVIDED, HOWEVER, THAT THIS LIMITATION SHALL NOT APPLY TO EACH PARTY’S (A) BREACH OF SECTION 4 (CONFIDENTIALITY); (B) BREACH OF SECTION 7 (OWNERSHIP AND INVENTIONS); (C) INDEMNIFICATION OBLIGATIONS; OR (C) RECKLESSNESS, INTENTIONAL MISCONDUCT OR FRAUD. THE PARTIES FURTHER AGREE THAT THE MAXIMUM LIABILITY UNDER THIS AGREEMENT, REGARDLESS OF THE TYPE OF CLAIM OR NATURE OF DAMAGES, SHALL EXCEED THE TOTAL VALUE OF SERVICES PROVIDED BY DAVA TO AROG HEREUNDER.

The most glaring problem with this clause should be clear to an experienced contract negotiator fairly quickly. The word “NOT” is missing. Rather than saying “SHALL NOT EXCEED” it says “SHALL EXCEED”. And so what was probably intended as an aggregate cap on the supplier’s liability, instead becomes a strange declaration of liability exposure. Clearly, someone made a mistake. Quite a big one.

Which brings me to clause tracking. How do I build a machine that can spot this problem and flag it for remediation?

If my clause unit is the whole numbered paragraph 10 above (which seems reasonable on its face), then a clause tracking tool might use a statistical bag of words method to tell me that (because of the missing 3 letters) this is a 99% match to my preferred limitation of liability clause. But given the difficulty of perfectly matching in the grubby world of scanning errors, I am tempted to ignore 1% errors as immaterial. So this clause probably gets a green light.

This is the “perfect match” conundrum. In the real world of imperfect scans and OCR errors, I am almost never going to get a perfect match, so I treat 99% as OK. But in many cases, including the example above, a 99% match could actually contain a very high risk error.

If the other party produces the first draft (“third party paper”), your chances of clause matching go from bad to worse. Even if their template includes an acceptable version of the clauses you require, there’s a vanishingly small chance they used the same words as your clause library. So even the “matched” clauses will be well below 100% and you’ll have to double-check everything.

To be fair, a clause matching tool might redline the missing words and help a human negotiator to catch any mistakes. But redlining isn’t exactly ground-breaking. All it does it augment a skilled human process. We can unlock far more value by eliminating human tasks rather than augmenting them.

Another (arguably better) approach is to train a machine to understand the meaning of a “clause” and to define your risk playbook semantically, rather than by reference to precise word combinations. In this approach, you might split Section 10 into two semantic concepts. Sentence #1 is an exclusion of indirect loss, which is good. Sentence #2 is (or would be, but for the error) an aggregate cap on liability, which is also good (for the Supplier).

By training a machine with many examples of these two concepts, we can produce an AI service that assigns the correct semantic meaning to any sentence/clause processed, regardless of the precise words used. This offers far more value than precise word matching because it works not just for deals on our paper, but also for deals on third party paper.

We still need to solve the problem of training our machine to understand mistakes. Sentence #2 looks extremely similar to a liability cap. But it isn’t. So I need a “typo” training class to teach my AI that sloppy drafting like this should not be confused with the real thing.

Don’t get me wrong. There is some value to “perfect match” clause tracking if you are working with high fidelity documents that don’t suffer from OCR noise. But semantic machine learning analysis has far greater potential to automate contract review, especially if a large chunk of your deals are on third party paper.

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Contracts are weak link assets

Contracts are extremely valuable to your business.  But contracts are weak link assets.  It is your weakest contract that will cause you pain. It is the weakest terms of that contract that will do the same.  And your contract negotiators are like a soccer team.

Wait.  What?  Let me explain.

About a year ago, I was in Las Vegas, as a freshly-inducted member of the Coupa village (Coupa had just acquired the CLM software company I co-founded roughly 20 years before).  It was Inspire time – the annual Coupa conference – with Malcolm Gladwell as one of the featured speakers.  And Malcolm gave a truly inspiring talk about strong and weak links.

As with much of Malcolm’s work, he took an interesting idea from somewhere else, and wove it into a compelling story of his own.  The original idea comes from Chris Anderson and David Sally’s book, The Numbers Game: Why Everything You Know About Soccer Is Wrong.  Malcolm’s adaptation was to apply the strong vs weak link analysis to educational philanthropy, and to organizational theory more generally.

Here’s the idea.  Sometimes you get the most bang for buck by fixing weak links.  Sometimes you get the most bang for buck by investing in strong links.  Soccer, it turns out, is a weak link game.  You’re much better off building a team with no weak links, rather than splashing all your cash on superstars like Messi or Neymar.  Why?  Because in a low scoring game like soccer, mistakes lose games more often than miraculous goals win them.  Basketball, on the other hand, is a strong link game.  With Michael Jordan, Scottie Pippin and Dennis Rodman, the ‘96 Bulls were destined for greatness, notwithstanding the relative weakness of their Australian team mate, Luc Longley.  Basketball is a high scoring game where the odd mistake won’t kill you, but you need your strong players to get points on the board.

Reflecting on this idea of strong and weak links, it occurred to me that contracts are totally weak link assets.  When times are good, and business is rolling along, contracts tend to stay in the bottom drawer.  Plenty of contracts never see daylight after they’re signed, sealed and delivered.  Strong protection?  Weak protection?  Who cares?  But in times of crisis, when bad things happen, many contracts are going to be scrutinized under a bright, shiny light.  Those deals better not have any weak links, because if they do, a not-so-friendly litigator is going to tear right through them.

This is quite the frustrating predicament for contract management professionals.  When the pressure is on to wrap up negotiations and close a deal, the commercial decision-makers will focus on the big ticket issues (liability caps, etc.), with little patience for fighting over dozens of “rats and mice” clauses that could be future weak links.  And they have a point.  It’s often not worth blowing a deal up over an impasse on any one weak link issue.  And so they linger on.  Hundreds, sometimes thousands of weak links.  Waiting for the moment to rear their ugly heads.

This problem is compounded by the fact that corporate and financial due diligence efforts typically adopt strong link analysis techniques to contract portfolios.  Assessments of legal risk often apply materiality thresholds that limit review to major or high value contracts, and limit clause analysis to issues likely to have a large impact on the balance sheet or P&L.  These keep everyone focused on strong link risks.  This would be fine if contracting was basketball.  But it isn’t.  It’s soccer.

Force majeure is just the latest example of a potential weak link clause that was often overlooked.  Most force majeure events rarely happen.  If they do happen, they don’t last very long.  But when these events happen, the pain can be very high, and everyone will be looking closely at the details, perhaps for the first time ever.

With the COVID-19 pandemic and government shutdowns triggering billions of dollars of losses across the globe, the humble force majeure clause may be the weak link that makes those losses fall disproportionately hard on your business.  Maybe you had a handle on where the clause offered protection and where it did not.  Your playbook ensured a mutual right to suspend performance for the duration of any force majeure event.  But what about termination?  Pre-COVID, many negotiators viewed force majeure as short, isolated blips, rather than extended, global, recession-inducing game-changers.  The inclusion of a force majeure termination trigger, and the timing of that trigger, is thus much more random than we might hope.  And if this weak link results in the unexpected early termination of multiple critical supply agreements, the disruption and costs will be substantial.

What’s the solution to this dilemma?  The obvious starting point is to get a handle on the problem.  Sure, everyone has weak links in their contracts, but not all weak links are the same.  If you start by tracking and measuring where you have weak or risky terms, then you’ve got some chance of understanding the scale of pain that might be coming your way.  Armed with that insight, you can take action to mitigate the risks.  If you don’t even know where the weak links are lurking, you’ll feel a full load of pain.  You can’t mitigate after the fact.

One of the keys to fixing contractual weak links is being able to translate all the legal mumbo-jumbo into data you can measure, and data you can trust.  I will save my thoughts on the trusted data challenge for another time.