LIBOR Transition: Resolution to Data Challenges

Introduction/Problem Statement

Since 1968, the London Interbank Offered Rate (LIBOR) has served as a primary benchmark rate for an estimated $300 trillion of loans [1] , securities, and derivatives worldwide. Given the volatility of the economic market over the previous decade, global regulators have declared that the current construct of the LIBOR calculation is unsound and a veritable threat to international financial stability. This concern has culminated in the Financial Conduct Authority’s March 2021 [2] announcement to cease virtually all USD LIBOR product issuances after December 31, 2021, and USD LIBOR publication after June 30, 2023.

Given the volatility of the economic market over the previous decade, global regulators have declared that the current construct of the LIBOR calculation is unsound and a veritable threat to international financial stability.

With the formal decommission of LIBOR as a rate option, financial services organizations along with capital-intensive industries such as manufacturing, energy and real-estate are tasked with identifying and amending the millions of contracts referencing LIBOR-based products. With the volume and complexity of such an undertaking, firms are challenged to develop effective solutions surrounding production, monitoring and reporting on cessation activities. As such, a lack of overall insight exists regarding completion status, outstanding remediation population, credit officer actions and client responses.

Industry Viewpoint

LIBOR-impacted industries have a to action extending over numerous decades of responding to regulatory overhauls designed to increase economic stability and mitigate future financial crises. As such, organizations should initially reflect on clear-win and clear-loss actions which previously instilled confidence in skeptical regulators. After performing this act of internal assessment, businesses should focus on detecting existing gaps such as silos, legacy architecture, and data quality inconsistencies. Acknowledgment of potential disparities allows for proactive remediation efforts rather than retrospective discovery after new services are implemented. Such preemptive measures may pave a smooth path for solutioning loan and lease transformation centered on data harmonization and robustness across an organization.

Solution

Positive success in remediating such gaps has been observed by addressing the scenario from a ‘hub-and-spoke’ structure. Given the complexity of impacted organizations, many require a centralized governance structure to allow for data quality across the lines of business. With senior management at the ‘hub’ defining the key actions required, business units are effectively enabled to isolate deficiencies and unify fragmented data.

In conjunction with a foundational framework, organizations require resolutions derived from a multi-faceted approach rooted in data integrity. Doing so requires execution of:

  • Eliminating software silos,
  • Integrating data flow between systems of record
  • Pinpointing necessary enrichments to ensure standardization of key field presentation, and
  • Ensuring the current state tech stack is sufficient to avoid retrofitting during remediation

Upon completion of such actions, preservation of data integrity should occur for both content and metadata through reliability-based analysis centered on production, system integrity and probability distribution. Implementing such an approach may produce tangible benefits such as an established end-to-end audit trail, reduction of human error, strong recoverability, searchability and traceability functionalities. These actions, in turn, will help instill confidence in data integrity and completeness along with mitigating fear of potential deficiencies within the portfolios.

In conjunction with data capture, organizations need to successfully define their approach regarding formal presentation of such data to credit officers, executive leadership and external regulators. Key factors for data presentation include:

  • Appropriate level of context,
  • Minimization of data noise and complexity,
  • Synchronized information depicting a singular story, and
  • Presentation of details through a combination of numbers, language and visuals

This combination of factors helps to drive internal success through user empowerment of available data to extract necessary details and effectively converse with impacted clients. This approach also may help establish trust with external clients and compliance with regulatory agencies through the systematic and comprehensive insight brought to cessation activities. Such data availability in conjunction with the proposed, robust strategies have become a powerful force towards LIBOR remediation from which teams can extract smarter predictions for leadership to derive more timely and productive decisions.

For more information about DHG's LIBOR Transaction Advisory capabilities, reach out to us at DHGAdvisory@dhg.com

References
[1] Kiff, John. “International Monetary Fund: What Is LIBOR?” Back to Basics: What Is Libor? - Finance & Development, December 2012
[2] Financial Conduct Authority. “FCA Announcement on Future Cessation and Loss of ...” FCA Financial Conduct Authority, 5 Mar. 2021

ABOUT THE AUTHORS

Lauren Ronge
Senior Consultant, Risk Advisory

Daniel Miller
Lead Consultant, Risk Advisory

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