The short-order collapse of Silicon Valley Bank, Signature Bank, and First Republic Bank earlier this year sent shockwaves across the entire financial industry, triggering calls for fresh regulation and new measures aimed at improving liquidity and recovery planning.
The general sense is that banks need to be ready. But for what? Answers to this question can remain vague for many executives. One of causes of this situation is that the risk allocation tends to take place only at a client or transactional level when it should be happening at a portfolio level. While there is a reasonable degree of visibility into the risk nexus for a particular product line or a specific client engagement, portfolio risk across product silos can be much more difficult to assess given the myriad contracts between any given institution and its counterparties.
Contractual data modelling and a comprehensive contract review can help executives collect this information and put it to use. The benefit is not only more effective risk mitigation amid market turmoil, but time and cost savings that can translate into vital competitive advantages.
Contractual data models help preemptively collect information about key contract risks, enabling stakeholders to quicky make informed decisions instead of losing valuable time evaluating each contract during an impromptu review exercise.
In practice, a "data model" is simply an abstract of known core questions covering any contractual portfolio. For example, what are the key termination rights? Are such rights triggered by a recent credit ratings downgrade? What are the limits for the transfer of the contract portfolio?
The model might be configured to contain specific queries, like the ones above, or more complex queries answered by a review of several clauses – for example, profiling the eligible collateral within a product or client portfolio.
Creating a tailored data model from scratch may seem daunting, particularly in terms of finding the required bandwidth to put the building blocks in place and populate the model. And, of course, time and capacity are in shorter supply than ever in times of market stress. Therefore, each institution must decide whether to proactively develop its data model internally or partner with a specialised third-party provider.
The best data model in the world is worthless unless it is deployed against a body of contracts. However, a contract review at scale is a significant undertaking which has historically involved a laborious manual trawl of contract data.
Fortunately, new technology tools make this process much easier. A financial institution might now tackle this challenge with machine learning technology used to extract information from contracts based on a preconfigured data model. However, relying exclusively on this approach is unlikely to be sufficient. Results produced by a trained machine learning tool can, occasionally, be unreliable. This means that the cure can be effectively worse than the disease for a given financial institution. Only adding another component i.e. review carried out by subject matter experts, can mitigate this risk. Human involvement in the process ensures that results of contract review powered by machine learning technology are reliable. Additionally, verification undertaken by lawyers can support training process of the machine learning tool and ensure that results delivered by the technology are presented consistently in a way useful for the decision-makers.
Once a financial institution aligns its existing contracts against a data model it can relatively easy apply this solution for new contractual engagements.
Today, many institutions review contracts ad hoc at an individual transaction level against specific risk factors. There are numerous events that can trigger an urgent need to review a contractual portfolio, from market turbulence to counterparty distress, a reorganisation or merger proposal.
This piecemeal approach is not only resource intensive and time-consuming, but also yields few reusable insights capable of supporting effective decision-making and risk management beyond the specific trigger for the contract review.
The need for a comprehensive contract review is therefore not a question of "why," but a matter of "when." A pre-emptive, portfolio-level risk review enables organizations to act quickly and decisively on accurate information in the moment, but this requires a proactive plan to implement a modelling framework and contract review. As this period of market turmoil has shown, it’s often a worthwhile investment.
