For decades, the LIBOR – or London Inter-Bank Offered Rate – has been practically the most important numerical figure in existence. It’s been used as the global interest rate benchmark by mortgage lenders, student loan officers and credit card agencies, among others, all of which set their own interest rates relative to what LIBOR dictates.
From 2021, LIBOR will be scrapped, amid recent rigging scandals that resulted in billions of dollars in fines for major global banks. The financial industry will have to make friends with new ‘risk-free’ alternatives to LIBOR including SONIA (Sterling Overnight Index Average) or SOFR (Secured Overnight Financing Rate).
All of which means that in less than two years, banks must pull off the existentially daunting feat of transitioning all of their contracts to alternative interest rates.
Robotic process automation (RPA) and natural language processing (NLP) can do the “grunt work” of analysing documents and pulling out keywords, but when we get to the meaty part – i.e deciding what to do with each contract – the process becomes all about multi-factored decision-making, which is better suited to knowledge-first, rules-based automation. Identifying the best fall-back provision in the contract for situations when LIBOR is unavailable, or which are expiring before 2021 and can therefore be disregarded, or how much additional risk to price in – these are the types of judgements that typically require human oversight. They’re also dependent on thinking processes that can be encoded in rules-based technology. Tools that can make this thinking readily available – such as an automated triage tool linked to a centralised knowledge base – can vastly improve the rate and accuracy of contract issuance and revision, under post-LIBOR terms.
In a world of curveballs like Brexit, amending large numbers of contracts could become something of a recurring theme for businesses. For large banks, getting the armoury in place now will not only appease the FCA’s urge to have a “robust written plan” in advance of the change – it’ll save millions in the transition period, when that mountain of contracts beckons.
Transparency in a post-LIBOR world
Despite the short-term operational challenges, a post-LIBOR improvement of the market structure is a real possibility. Among many, many other things, what the financial crisis showed us was the vulnerability of IBORs to manipulation. To calculate LIBOR, each morning from the 1980s to the 2010s, the British Bankers’ Association (BBA) collected interbank-offered rate quotes from a panel of banks, reflecting the rates at which banks said they could borrow funds from other banks. These were hypothetical numbers that didn’t necessarily have to match up to reality, leaving obvious room for foul play. The transition to rates grounded in actual transactions and liquid markets, rather than derived from speculative polls of selected banks (as IBORs were), should be viewed as an ethical step forward.
But in the midst of an FCA crackdown, firms will have to do more to execute this new trend toward total transparency. Each trade or loan will need to be backed up by comprehensive contextual information about each trade, including how a certain price was reached and how a firm measured the liquidity profile of a trade, right down to intricate details such as how long an employee has been trading for.
Failure to invest in transparent technology to properly audit trades could see firms and traders incurring the same regulatory retribution and unhealthy headlines as ex-UBS trader Arif Hussein, banned by the FCA for attempting to manipulate sterling rates.
Opportunities of risk-free rates
The various aforementioned ‘risk-free’ rates, including SONIA and SOFR, are currently having varying degrees of takeup and liquidity. But the likelihood is that firms and traders will soon have to fully commit to a world in which there are multiple rates to choose from to best serve specific segments of the market. This could mean a wealth of new opportunities to find the perfect match of rate with product – if firms can identify them. Again, this sounds like a large-scale task but it would be a breeze for knowledge-first automation to aid in the assessment process of defining risks, deadlines and term structures, per rate and product.
This touches on another major element of the transition – education and knowledge sharing. In insurance, for example, the widespread use of new benchmarks will require the education of loan officers, staff and customers. Transparent automation technology that can make information on each rate widely accessible will go some way to achieving this.
The end of LIBOR is going to be a perfect storm for the financial services industry – one of those tide-turning moments that has the potential to leave everyone adrift and lost at sea. But organisations should be implementing transparent intelligent automation now to ensure they reach safe harbour.
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