Prodigal Data

Prodigal Data

Corporates assign an incredible amount of effort to getting key contracts in place; senior business stakeholders invest time into forging relationships and agreeing important business and risk understandings, which need to be safely transitioned into a legal construct. Most often, senior lawyers manage this transition, which is then negotiated with other senior lawyers. Contract execution, when it happens, is a significant event, celebrated by both sides and often kicks off years of good business.

Yet, in 99% of instances, these fine contracts go into either a storage box or document repository and are never seen again, unless there is a business incident and then a mad scramble ensues to try and fish the contract out of wherever its been slumbering. At best, header page information has been harvested from the contract and entered into core systems, but this is usually only enough to operationally manage a loan or derivative or money market account, or indeed set up a supplier on a procurement system. All those hard-fought business and risk understandings are mostly lost to organizations and it has been, and is, a genuine tragedy.

The world has though, over the last five years, changed significantly. Prior to the COVID pandemic, we were already noting the incremental rise in ferocious marketplace competition, driven largely by social media and other online user data being commercialized via advanced analytics to get product in front of buyers in a way never contemplated before. The COVID pandemic itself stressed global balance sheets as well as corporate and sovereign resilience – the Ukraine situation has obviously, only exacerbated this trend. And against this backdrop of rampant competition and market uncertainty, regulators across sectors have, if anything, upped the ante in terms of their scope of expectations and also the level of scrutiny deployed over these expectations.

Buffeted by these various existential headwinds, organizations now have to deliver more value with fewer inputs. This inexorable wave of pressure has surged over traditional challenges and shoe-horned 10 years’ worth of digital transformation into 18 months. Companies are now critically assessing every shred of corporate data, across functions, and are attempting to utilize it to create value. Within this environment, legal can no longer afford to squander a rich data inheritance without consequences and with the rise of mature data extraction technology, they no longer have to. Liberating and effectively re-deploying this “lost” legal data is now entirely possible.

We have already discussed methodologies to extract legal data in other versions of Insights, so we won’t replicate that here. Rather, we will focus on how corporates can use legal data, once it has been extracted, to “deliver more value with fewer inputs”.

It’s worthwhile pointing out though, that what enables the type of analysis I will walk through shortly is the fact that once you are through the digitization and data modelling process, you are now in a position where you can do two things. First, you can access, interrogate and manipulate a structured set of your contract data and deploy it for use in downstream systems. Secondly, and critically, you can generate valuable risk and commercial insights by comparing, across your entire contract estate, key language, clauses or provisions between different vintages and versions of a contract template and note the variation. That’s where the real goodness lies. A couple of topical use cases below both from financial services and further afield highlight this:


Financial Services - locating the poison pills

Even with embedded policies and controls, getting legal contracts in place, under time and cost pressures, alongside complex review processes (often involving multiple actors and competing interests), inevitably puts firms at risk of entering into ‘out of policy’ or close to ‘out of policy’ language. For a classic example, one only has to think back to the 2008 financial crisis, where bank trading floor sales functions pushed, in a higher than comfortable number of transactions under ISDA agreements, to have margin calls linked to both underlying asset prices and overly-complex default provisions. Now, at the time of drafting, the markets realistically activating these defaulting provisions seemed incredibly unlikely, requiring many standard deviation shifts in asset prices to come into play. Regardless of the probability though, they became almost daily occurrences as the sub-prime “black swan” event unfolded. Many of us in industry actually know people who got a “billion dollar telephone call” where, out of the blue, a counterparty was demanding margin based on a provision buried deep in a contract which nobody had looked at for years. Banks were largely unprepared and this compounded their woes at a time of crisis and made survival far more difficult. Core functionality in mature data extraction tools is the ability to upload policy points into the system and then score critical provisions against these policy points. This can give you both an idea of where the danger lies in the legacy book (for action to be taken) and also alert business stakeholders to risk as new agreements are negotiated.


Financial Services - getting better risk metrics and removing the proxy mist

All contracts contemplate what happens if something goes wrong and in many contract areas both the “something” and “what happens” are defined in ways that can directly engage with risk and pricing models. However, as mentioned previously, often this rich data set never makes it out of the contract and organizations end up using aging proxy or conservative placeholder data in their live risk management processes and pricing calculations, because going back to source has never been considered a practical option or has involved too much perceived manual effort. Classic examples of this are a default schedule or collateral detail in an ISDA agreement or indeed provisioning data or interest rate fallbacks in an LMA loan agreement. In fact, within the Basel-governed world of financial services, mining legal data and updating proxies can quickly lead to, amongst other things, better data-driven collateral management, increased hedge effectiveness and ultimately, direct risk-weighted capital reductions, thus freeing up financial resources to attack more complex deals, with higher margin potential.


Corporate and Procurement contracts - rolling with the Schrems II punches

Part of the knock-on impact of the ECJ’s Schrems II decision is that all EU/UK based firms that have either supplier or customer contracts with entities based outside of the EU/UK need to understand (and take various actions) if personal data is moving outside of the UK/EU as a part of the services which these contracts underpin. The problem is, many mid to large size corporates now have thousands of contracts to review and data to map and whether or not personal data is moving across borders is not always explicitly referenced, but rather has to be inferred by contractual indicators at multiple locations in the contracts. Again, using technology we can input likely language, clauses or indicators that suggest the presence of personal data flows and the system will then score contracts on how much risk they run; this should allow firms to optimise their budgets and triage which contracts to review urgently and where firms can, on a risk-based approach, discount or deprioritize other contracts.

It is paradoxical that we are generating new risk management insights and optimizing risk and financial calculations by liberating and deploying old data, in some cases, very old. Many firms struggle with this at a visceral level. The truth though, is that this data never should have been disinherited in the first place and we are only now in an environment where this data can safely and cost-effectively return home.

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