Advanced Data Mining Technologies and AI Can Aid Firms’ Transition from LIBOR to Alternative Risk-Free Rates

Advanced Data Mining Technologies and AI Can Aid Firms’ Transition from LIBOR to Alternative Risk-Free Rates
Advanced Data Mining Technologies and AI Can Aid Firms’ Transition from LIBOR to Alternative Risk-Free Rates

As the financial markets prepare for potential disruptions from the phasing out of the London Interbank Offered Rate (LIBOR) and selection and implementation of alternative risk-free rates (RFR) by the end of 2021, financial institutions are seeking creative solutions to help manage the anticipated workload and transition risks.

On a macro level, institutions currently have myriad concerns over the transition to a LIBOR replacement and how legacy contracts that reference LIBOR would be altered to reference the new rate. On an operational level, substantial document management challenges are anticipated.

Converting outstanding LIBOR contracts may necessitate extensive data processing, system updates and documentation review. Contract conversion will require a complete understanding of the existing contract population, relevant document types, contractual triggers, if-then scenarios, and the ability to link amendments and applicable clauses. This means banks that are party to millions of individual loans, securities and derivatives that reference LIBOR face a herculean task. For those institutions, having the right technology will be crucial during this transition phase.

Institutions with access to natural language processing (NLP), a technique that employs computational linguistics and artificial intelligence to understand language, will be better positioned competitively compared to peers that do not. With NLP technology, firms can review extensive documents quickly and extract relevant LIBOR clauses from both structured and unstructured data. NLP can be used to pinpoint specific contractual language such as names of counterparties, effective dates, applicable terms and jurisdictions.

Since NLP accelerates unstructured data processing in an automatic fashion, with the ability to categorize and translate documents, institutions that leverage its capabilities effectively can expect to save significant time and cost, while obtaining more accurate extraction of data points needed to complete the RFR transition.

Also, given many institutions do not store contracts in a single location on their networks, a NLP tool can be programmed to search for relevant LIBOR clauses and extract corresponding documents across different networks or assigned folders within a network. The data extracted can be sent to a repository or user-selected output for storage and reporting.

In addition to the document management concerns above, the transition to RFR is expected to have many key implications for firms and the financial market, such as::

  • Litigation risks: Legacy LIBOR contracts that do not have an effective replacement may trigger lawsuits especially if they result in losses to investors or counterparties.
  • Valuation and risk management: Transition of legacy contracts will affect hedging and introduce market valuation issues, particularly if duration and hedge effectiveness are not examined closely.
  • Infrastructure: Technology for compliance, risk modeling and portfolio tracking must be updated and tested to capture transactions that reference the new RFR accurately.
  • Accounting and tax implications: Fair value changes may impact taxation of firms. Also, hedge accounting and inter-affiliate accounting structures could be affected.

The LIBOR transition will require a disciplined and focused approach by management across the organization. Follow Protiviti’s Insights blog to learn more about how replacing the LIBOR rate will affect your business and other creative ways to mitigate exposures and risks.