The transition away from LIBOR poses a huge challenge for financial services firms who may have hundreds if not thousands of contracts referencing the benchmark that’s due to be phased out from the end of 2021. NLP-powered Document AI technology can play a crucial role in automating your review process so you can fully understand your risk exposure and identify the appropriate remediation actions for LIBOR-linked contracts.
As well as saving firms time and money on their LIBOR efforts, one of the benefits of using Document AI technology is the longer-term effect. LIBOR necessitates a wholesale review of documents on a scale previously unknown. Which means, those choosing to use NLP to automate their in-house document review for LIBOR, have also taken a giant leap forward with their digital transformation efforts. The technology can solve your LIBOR legacy contract analysis challenge and create opportunities to tackle other projects and improve internal processes.
Goodbye LIBOR, Hello Risk-Free Reference Rates
The London Interbank Offered Rate (LIBOR) was introduced in the 1980s and has been used by loan issuers as a benchmark for the interest rates they charge on their financial products since then. It underpins hundreds of trillions of dollars’ worth of financial contracts globally, including derivatives, bonds, loans and mortgages. But because of the way LIBOR is set, using data submitted by a consortium of international banks based on estimated rather than actual rates, it’s seen more than its fair share of controversy.
In the last 10-12 years, it’s been beset with rigging scandals and claims of manipulation. As a result, the regulators have called time on LIBOR, and it will be replaced by alternative risk-free rates (RFRs) from 2021 onwards.
Regulators, official administrators and trade associations have come together in working groups formed worldwide to develop currency-specific replacement rates and guidance for impacted businesses. The recommended alternative reference rates are consistent in terms of when (overnight) and how (transaction-based) they are set. The central banks will calculate these currency-specific reference rates based on transactional market data to mitigate the risks associated with using estimates submitted by market participants. The key risk-free rates replacing LIBOR will be:
- US Dollar: Secured Overnight Financing Rate (SOFR)
- UK Sterling: Reformed Sterling Overnight Index Average (SONIA)
- Euro: Euro Short-Term Rate (€STR)
- Swiss Franc: Swiss Average Rate Overnight (SARON)
- JP Yen: Tokyo Overnight Average Rate (TONA)
Solving the Legacy Document Analysis Challenge
LIBOR transition has created economic, conduct and operational risks for those in capital markets, commercial lending, retail banking, wealth management, investment management and insurance. To mitigate these risks, firms must assess and understand the impact on their products and agreements and transition those affected by the change to alternative rates. This requires remediation of legacy contracts and communication, and potentially renegotiation, with clients.
Additionally, internal systems, models and processes need to be updated to reflect the changes. The amount of effort and time required to complete the end-to-end exercise cannot be underestimated, and the document analysis step is particularly onerous. A thorough review is necessary to avoid operational risks, which is time-consuming and costly if it’s not automated.
Before the contract remediation process can get underway, the right documents need to be identified and then sorted based on the type of remediation required. Remediating contracts for the LIBOR transition is essentially a three-step process:
- Step 1: Find and flag all the contracts that require remediation.
- Step 2: Identify the type of remediation required and bucket the contracts accordingly – for example, repapering, renegotiation, wind-down etc.
- Step 3: Carry out the remediation work.
Using Document AI, it’s possible to automate and accelerate the first two steps of the process and train the platform to extract the specific information and answer the relevant questions with minimal human intervention. Using NLP and machine learning techniques, the platform can analyze large volumes of unstructured, siloed text, such as contracts, rapidly and more accurately than human review alone. The diagram below shows the steps in a LIBOR transition project that Document AI can automate to save firms’ time and money.
Document AI Provides Value Long After LIBOR
The technology is flexible enough to handle virtually any text-heavy document. It can gather data points and provide insights to support many use cases and automate multiple processes. And once the technology has been successfully implemented for one project or use case, non-technical users can quickly train it to answer new questions.
This gives firms the flexibility to handle other regulatory, compliance and reporting requirements, automate loan operations processes and address internal workflow pain points. With so many document types being subject to review for LIBOR, the opportunities to quickly solve other business challenges are plentiful.
Many financial services firms are embedding Document AI technology across their organisations to scale operations, automate processes and drive cross-functional efficiencies. Firms can achieve straight-through processing by using APIs to connect the platform to upstream and downstream systems and custom code in the form of plugins can enrich or transform the data output.
With the flexibility to train the platform on new document sets and data requirements, those firms using Document AI for LIBOR legacy contract analysis will reap the business benefits long after it has been replaced.
For More Information
To find out how Document AI can help you with your LIBOR transition, please visit the Eigen Technologies website.
Or request a demo with one of Eigen’s Document AI specialists who can show you how clients like Goldman Sachs, ING, BlackRock and Allen & Overy benefit from the technology.
[ Artificial Lawyer is proud to bring you this sponsored thought leadership article by Eigen Technologies. ]