In July 2017, the U.K.’s Financial Conduct Authority (FCA) announced that the London Interbank Offered Rate (LIBOR) would cease to exist by the end of 2021.
Although set in the U.K., banks and other financial institutions worldwide use the LIBOR protocol for deciding the average interest rate at which they will lend to each other. Today, LIBOR has become a global benchmark for banking transactions. More importantly, banks use LIBOR to decide rates for a whole range of financial products and services ranging from personal, home, and car loans to complex derivatives.
LIBOR is the average interest rate at which major global banks borrow from one another. It is based on five currencies, including the U.S. Dollar, the Euro, the British Pound, the Japanese Yen, and the Swiss Franc, and serves seven different maturities – overnight/spot next, one week, and one, two, three, six, and 12 months .
LIBOR has been tainted since the 2012 scandal when it emerged that derivatives traders, in collusion, had attempted to rig this key rate to make better returns. Regulators want LIBOR phased out, and by December 2021, banks and financial institutes must pivot to risk-free alternative rates.
LIBOR has been around for so long that the decision to withdraw has set panic among global financial institutions that are now calling for a transition plan. As soon as the FCA announced plans to sunset the LIBOR, the U.S. Federal Housing Finance Agency came out with new and comprehensive LIBOR transition plans for national mortgage loan companies like Fannie Mae, Federal Home Loan Bank, and Freddie Mac.
According to some estimates, trillions of dollars of LIBOR-linked financial products are circulating in the market today. With too much money out there determined by a single protocol that will soon cease to exist, there is an immediate need to simplify interest rate decision-making.
These contracts need to be analyzed for key contractual terms, including “fallbacks” – which define what happens if LIBOR is discontinued. Furthermore, Fallback and related clauses are buried deep into the unstructured text, making its extraction and analysis difficult.
Consider this, for example: sifting through reams and reams of documents would require the services of hundreds of trained legal and financial specialists, lawyers, and regulators, which can cost effort, time, and money. According to global banking estimates, 14 of the world’s top banks usually pay anywhere up to $1.2 billion annually in remediation. In contrast, the costs for the financial and banking industry as a whole will be much more than that amount, and much of that estimate is linked to the challenging and difficult task of changing the terms of contracts whose duration extends beyond 2021 when LIBOR ends.
However, non-compliance is not an option and will open banks to lawsuits and fines from regulators. As Michael Held, General Counsel at the New York Federal Reserve publicly described this as a “Defcon 1 litigation event.”
With trillions of dollars at stake, banks and law firms are exploring technology and Artificial Intelligence to find a solution to this problem.
By using machines to do the manual task of analyzing the documents – some of them still in scanned or hard copies – companies can save millions of dollars in labor costs and shorten the timeframes exponentially.
Machine Learning and Natural Language Processing can be leveraged to look at a large number of documents and identify the relevant legal clauses and obligations. These clauses can then be analyzed using appropriate data science models to help the law firms, investment companies, and banks identify the action required on these documents – from none at all if there is sufficient fall-back to multi-party negotiation and re-papering.
With advances in artificial intelligence and data sciences – companies can expect anywhere from 70% to 95% automation to identify and flag the contracts for re-papering. Furthermore, these contracts will now be fully digitized, fully searchable, and enable better search and analytics.
Artificial intelligence has drastically improved the accuracy of dealing with vast volumes of complex data, which helps mitigate the risks of moving away from LIBOR and implementing a reliable AI capability as the first step.
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