At the Heart of the LIBOR Nightmare Is a Data Problem

The transition from LIBOR to SONIA and the risks it proposes are heavily concerned with data. For the sake of operational success, financial services firms must be prepared.

Peter Ku, VP and Chief Financial Strategist for Informatica, outlines the challenges posed by the transition and how firms can turn them into opportunities.

The London Interbank Offered Rate (LIBOR) underpins some $240 trillion in financial contracts, and with just 11 months to go until the move to risk free rates, Sterling Over Night Indexed Average (SONIA), financial services firms are under pressure to finalise this complex change programme.

Widely considered one of the biggest transformation programmes undertaken by modern financial services firms, the shift away from LIBOR is a complex business challenge which impacts teams across the business. Failure to adequately prepare represents significant operational risk. Why? Because at the heart of it all is data – what is it, where is it, how is it connected, governed, and made available to the business. Board level committees, cross-functional teams and significant resources have been dedicated to managing this intensive and – at times – painful process. However, it’s not all imposition; there are meaningful upsides to having trusted, governed and relevant data, shifting it from tool to strategic business asset.

The Road Ahead

The Bank of England recently published an updated 2021 Roadmap, outlining key milestones that need to be met in order to prepare for the LIBOR transition. It suggests that by the end of Q1 2021, organisations will have completed the identification of all legacy LIBOR contracts. For banks that have hundreds of systems – each with thousands of indexes – locating and tracking the lineage of this data across all systems is a mammoth task.

After completing the LIBOR data inventory, firms can begin conducting an impact analysis on all existing LIBOR contracts. This is a crucial, in-depth exercise covering a number of areas. What will the financial impact be of switching from LIBOR to SONIA? What is the market, operational, credit and reputational risk? Data quality is paramount to being able to perform accurate analysis and in turn manage risk. It’s important to keep in mind that a change of a single data point will impact multiple systems and, in most cases, hundreds of reports. Therefore, having confidence that the data is accurate and trusted is essential. Finance and accounting teams will need to update risk and valuation models once the risk exposure is identified. These include valuation models, pricing future revenue streams and how those impact daily, monthly and annual reporting.

What will the financial impact be of switching from LIBOR to SONIA? What is the market, operational, credit and reputational risk?

Alongside this work, legal and compliance teams will be working to review and replace fall back language in LIBOR contracts which expire in 2022 and beyond. The roadmap published by the Bank of England working group suggests that firms complete these conversions by the end of September 2021. The success of this maps back to the data inventory, and whether teams are able to determine which systems service which contracts, and adequately address corrupt data.

The singular thread through it all, whether it be those managed by legal and compliance, finance and accounting, or risk management, is a dependency on data that is good for use. Unfortunately, many organisations today still struggle with data quality. There are instances where the correct data just isn’t available, or it’s unclear where the data is located or how it is connected to other indexes. This is a continuous work in progress but the conversion from LIBOR to SONIA is undoubtably driving improvements in the automation and scale of existing data governance projects.

Operationalising Data Governance

The LIBOR transition may be a landmark one, but it certainly won’t be the last challenge for the financial sector, which will continue to face increasing market pressures fuelled by rapidly emerging technologies, global interconnectedness, changing economic and jurisdictional factors, and consumer demands. It is the adoption of cloud-based technologies and steady foundation of intelligent data governance that will deliver sustainability, resilience and efficiency moving forward.

As Chief Data Officers round out these gargantuan programmes, a continued focus on two core areas will accelerate the shift of data governance from an IT-centric discipline to a core business function that empowers all within the organisation to be more data-driven.

First, there needs to be a continued focus on resolving data quality issues. Data quality management should be proactive, measured, monitored, and communicated across all data stakeholders from data engineers, analysts, stewards to executive business decision makers. This will ensure data quality management is transparent, predictable and measurable.

Secondly, users need to leverage tools and technologies to make data governance processes more automated and agile. AI-driven data governance solutions can operationalise data governance by decentralising data stewardship and enabling self-service stewardship to reduce the cost to the business, while still allowing data governance to scale.

Data is the new currency of financial services firms. Forward-thinking organisations will view the overhaul required to move away from LIBOR as a stepping stone to turn data management challenges into opportunities.

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