Sirion Acquires AI Pioneer Eigen Technologies to Accelerate Document AI Capabilities.

Gray swan events and what they mean for analyzing your documents

It would be easy to assume that an event as cataclysmic as COVID-19 would qualify as a “black swan”, a term which has become shorthand for an event that catches markets unawares. The term “black swan” was popularized by author, professor and ex-Wall Street trader Nassim Nicholas Taleb in his 2007 book by the same name.

Technically, however, based on Taleb’s definition (and indeed he predicted a global pandemic back in 2007 in his book), the impact of COVID-19 on the world economy is a low-probability, high-consequence event qualifying it as a “gray swan”. The critical difference between a gray and black swan event is the ability to predict its occurrence.

Whether black or gray, it’s fair to say that very few people anticipated and planned for the ripple-effect the crisis has had on the financial system. This giant swan irrespective of colour crash-landed into our lives and our economy in 2020. And it’s not the only unexpected challenge financial institutions are having to contend with currently.

Brexit, COVID, LIBOR and now Negative Interest Rates

2020 has been awash with challenges for banks and lenders, and with each new issue comes more risk, more work and more expense. Negative interest rates are the latest in a growing list of issues that firms in financial services, including Eigen’s clients, have had to handle in recent months.

At the start of October, the Bank of England reached out to financial institutions in the UK with a voluntary survey to gauge operational readiness for a move to zero or negative interest rates after the Bank cut rates to a record low of 0.1% at the start of the coronavirus crisis . The prospect of negative interest rates has huge implications for banks from both an interest income and cost of funds perspective. For firms whose primary source of operating income is interest and cost of funding is heavily skewed towards deposits, this would be a major concern and would threaten their profitability at a time when they are being called upon to help those in greatest financial need as a result of the pandemic.

At the same time, these firms are also working through the impact of Brexit and COVID and the cessation of LIBOR. The cumulative effect is putting pressure on firms as they have to analyze their documents through different lenses to verify, collate and interpret information to fully understand and mitigate for the various economic and conduct risks these issues raise individually as well as collectively.

What this means for banks and lenders

Big banks and lenders now have a considerable challenge to overcome in the form of reviewing their back books. The effort required to identify LIBOR-linked instruments and investments alone is significant, but circumstances now dictate that they need to understand and mitigate for other risks.

This means potentially thousands of documents need to be examined and re-examined at a level of granularity previously unrequired. A single loan agreement could be subject to a review of IBOR-related terms, including maturity date and fallback provisions, passporting rights for Brexit, pandemic clauses, equity cure rights and transfer restrictions as a result of the market downturn created by COVID. The same agreement now potentially needs to be reviewed again, almost entirely from scratch, to understand the requirements and language relating to negative interest rates.

The administrative burden is huge when you consider the volume of credit agreements a large organization will have and that different subject matter experts may need to review the same documents. The risks and requirements relative to individual situations and scenarios need to be understood while they are also fluctuating and evolving. Reviewing these documents manually is likely to be a slow, cumbersome and costly process at a time when these firms need to arrive at answers and decisions quickly.

Solving the problem using Document AI technology

The option often pursued by these firms is to hire in a specialist team of consultants to manage the workload and to manually and meticulously review each document. While this is a tried and tested approach, it is also incredibly time-consuming. It, therefore, can be hugely expensive, especially when documents need to be analyzed for various separate but interlinked risks and scenarios.

Using a document AI platform, like Eigen, is a significantly more cost-effective and scalable way of approaching the problem. Firstly, the review process itself becomes substantially quicker when it comes to analyzing documents en masse. Users of Eigen have been able to make their document review process up to ten times faster and reduce manual processing by up to 80% while achieving high levels of data accuracy. Additionally, Eigen has enabled them to systematize the knowledge of experts to turn a one-off document review exercise into a repeatable and automated process able to handle flow books and future projects.

Document AI technology puts the power of natural language processing and machine learning in the hands of non-technical users, enabling them to transform documents into data at the push of a button. If circumstances change, it’s easy to adapt and update the machine learning model to produce additional or different data. The threat of negative interest rates is yet another reminder that we can’t always accurately predict the future, but it’s crucial to be able to react and make informed decisions quickly. Document AI makes managing such risks faster and more efficient.

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