Published by Gbaf News
Posted on December 10, 2019
5 min readLast updated: January 21, 2026
By Stephanie Vaughan, Global Legal Practice Director, iManage RAVN
The transition from LIBOR to new reference rates like SONIA and SOFR has meant that all financial institutions are currently in the process of evaluating their universe of contracts across the suite of asset classes. Financial institutions are worried that they will not have assessed and, where necessary, re-papered all relevant contracts by 2021, producing an “all hands on deck” situation. One can understand the concern given that financial products linked to LIBOR are valued at more than USD 350 trillion. Among other things, the upcoming transition has required a contract re‐papering exercise covering both such a wide geographical region and a large number of affected asset classes with real financial impact at a scale that has seldom been undertaken.
Nevertheless, every cloud has a silver lining.The transition has provided an opportunity for the financial and legal markets to not only further their understanding of artificial intelligence (AI), but has also triggered a practical adoption of AI technology to facilitate the LIBOR repapering. AI is able to quickly review and categorise huge volumes of digitised documents, and then extract key clauses or other pieces of information, helping institutions understand where there are potential liabilities around LIBOR and which documents need to be updated.
Using AI to turn unstructured data into structured data, however, does more than allow financial institutions to address current business challenges like the LIBOR transition – it also provides a foundation that they can use for future challenges, opportunities, and other business purposes; positioning them for success.
Getting Focused
AI has been a buzz word in the financial services industry for quite some time, but it’s taken sweeping changes like LIBOR to actually focus people’s minds on it. It’s true that multiple financial services institutions have set up AI‐focused centers of excellence, but there has not as yet been a real impetus to put AI into practice in understanding their contractual universe. LIBOR has provided that impetus.
In the process of using AI to tackle the LIBOR repapering, there’s been a recognition among financial services institutions that AI is not a tool that magically solves all your problems as soon as you buy it.
Rather, it’s a technology you train in order to provide the most benefit. In this way, the talk around AI has shifted from just being about “innovation” to focusing on “practical use” – it’s now extending out from the innovation hubs to the beating heart of operations, tackling real world problems like the LIBOR repapering.
So, moving forward, what other practical uses can AI assist financial services institutions with, now that it has assumed a prominent role in daily operations?
For starters, it can be of assistance with any future compliance efforts that are undertaken in response to new regulatory frameworks or other industry‐wide changes. In the last few years, financial services institutions have had to revisit their contracts and paperwork in light of GDPR, Brexit, and the LIBOR transition, to name just a few industry headaches. Periodically having to review the same set of documentation for different regulations is a cumbersome process, not to mention a huge drain on resources.
Having used AI for the LIBOR exercise, however, financial services institutions will be able to move more quickly and have a leg up on any future compliance challenges, because they will have already identified, and extracted and stored certain key pieces of information from their documents as structured data. Having that “base” of information means only having to extract a small amount of information on top of it, rather than starting from scratch each time a new regulation arrives on the scene. Furthermore, AI is now being implemented as ‘business as usual’ (BAU) and many financial institutions are using AI for anti-money laundering compliance.
This same base of information can also help financial services institutions optimise their dealings with their legal service providers as large-scale review or due diligence projects will no longer be required since the banks will have data at their fingertips. This is not only relevant for due diligence exercises but is also relevant for more traditional legal advice. As clients begin to understand their data, the advice they are able to receive can become not only more tailored but also more firm and begins to change the scope of what they need their legal advisors to do. Law firms, in turn, will need to evolve the delivery of their legal services to reflect this new reality where financial services institutions have recognised the value of the data contained within their documents.
Additionally,there are benefits to be gained simply by having a thorough understanding not just of loans, bonds, and other main financial contracts, but also the entire raft of supporting documentation – which can also include NDAs and signing authorities. Having a detailed accounting of all this data will put any financial services institution at a powerful advantage.
The Power of Combining Data
Financial services institutions have always recognised the importance of commercial data, but after going through the LIBOR exercise, they are realising the value of combining the commercial data with the data in their documents – a different type of data. Imagine not only being able to know what your exposure is to a particular counterparty is at any time, but also, in the same system, being able to understand how quickly you can terminate all of your contracts with that counterparty and the steps you need to take to do so?It’s time for this data to be systematised and placed at the forefront of operations.
Properly harnessing that data with AI technology – turning unstructured “words on a page” into structured data points – allows financial services institutions to better leverage that information in the future, for both foreseen and unforeseen purposes. Although initially embraced out of necessity to deal with today’s urgent challenges like the LIBOR transition, practical AI will continue to benefit financial services institutions for years to come.
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