June 28, 2017 - Kenneth Goodwin | Industry Solutions

Facilitating a Responsive Basel III Liquidity Coverage Ratio (LCR)


In December 2010, the Basel Committee on Banking Supervision (BCBS) agreed to new rules (Basel III) outlining global regulatory standards on bank capital adequacy and liquidity.  The Basel III reforms include rules on:

  • Enhanced Risk Weighted Capital Requirements
  • Risk Weighted Coverage Calibrations
  • New Leverage Requirements
  • New G-SIB Capital Surcharge
  • New Liquidity Ratios, including:
    • a liquidity coverage ratio;
    • a net stable funding ratio;
    • principles for liquidity risk management;
    • and liquidity risk supervisory metrics
  • Enhanced Risk Management and Supervision
  • Greater Market Discipline

The goals of these reforms were to: 1) improve the banking sector’s ability to absorb shocks arising from financial and economic stress; 2) improve risk management and governance; and 3) strengthen bank transparency and disclosure. This post describes how native visual analytics addresses the challenges that arise as a result of these rules.

Fundamentals of Liquidity Coverage Ratio and Net Stable Funding Ratio

The Basel Committee on Banking and Supervision (BCBS) first published a framework outlining the LCR on December 2010. It was later revised in January 2013. The goal of the LCR promotes short-term resilience of a bank’s liquidity profile by ensuring that a bank holds an adequate amount of “high-quality liquid assets” (HQLA) on their balance sheet to meet 30-day liquidity stress scenario.  These HQLA include cash or other assets that can be easily converted into cash at little or no loss in value.  These assets are categorized into Level 1, Level 2A, and 2B based on their liquidity characteristics (how fast they could be sold and purchase).

The calculation of the LCR is found by taking the value of HQLA assets, divided by the total net cash outflows over a 30 day timeframe in the stress scenario.  The LCR does not apply to banks with less than $50 billion in assets and applies fully to banks with $250 billion or more in assets and/or $10 billion in international exposure.

The concept of NSFR was first put forth in December 2009 by the BCBS.  It was revised in January 2014.  The goal of NSFR is to ensure that banks hold a minimum amount of stable funding in relation to their characteristics of their balance sheet and off-balance sheet activities over a one year time frame.  The NSFR also seeks to limit over reliance on short-term wholesale funding, improve risk assessment, and promote funding stability.  

The NSFR is calculated by dividing “available stable funding” (ASF) by “required stable funding” (RSF).  This ratio must always be greater than one.

Key Regulatory Challenges and Solutions: LCR and NSFR

The section below provides examples of specific challenges that arise from LCR and NSFR along with how native visual analytics address those challenges.  Native visual analytics optimizes regulatory programs because it enables the subject matter experts to run their own analytics natively (where the data resides) and join any type of data.  For example, an analyst can correlate real-time data with historical analysis to effectuate accurate and timely reporting from one data application.

Regulatory Challenge Enablement Via Visual Analytics Capabilities
Changes to both LCR and NSFR Treatments are more favorable of operational, retail, and small business deposits (by assigning higher ASF weightings to these deposits), and retail and small business loans (by assigning lower RSF weightings to these loans).  Weightings must be correctly tagged and assigned, thus, a need for a robust and aggregated data platform across entities. Data from multiple business sources (wholesale, retail, and commercial) and products lines (deposits, savings, loans, etc.), and jurisdictions need to be aggregated without loss of data to the underlying detail. Data quality review can be optimized through visual analytics that are run natively and simultaneously across multiple non-transposed data sources (i.e., lines of business, products, and algorithmic models).  Data loss is mitigated and the requisite granularity is provided to verify that weightings have been correctly tagged and assigned to assets, ensuring correct calculations of the NSFR and LCR.
Dynamic Internal Controls Framework:  Internal controls need to be flexible and responsive, continuously monitoring changes with treatments of operational deposits, stock borrowing transactions, reverse repos under LCR, and alternative treatment for derivatives under the NSFR.  It is essential for data users to have adequate tools in calculating and reporting these treatments in a timely and accurate manner.    Real-time and historical analysis enables a robust, defensible, and dynamic internal control framework that accurately monitors, measures, and reports changes in the treatments of operational deposits, stock borrowings, and derivatives to suffice LCR and NSFR requirements.
Meeting Regulatory Expectations: Regulatory compliance of both LCR and NSFR is expected by January 2018 by US banks.  The BCBS did not align the two ratios’ implementation dates together as the LCR was required in January 2019.  Nonetheless, banks are beginning to focus more on reviewing its short-term wholesale funding positions, thus requiring more data transparency across functions and lines of businesses.   A self-service visual analytics platform enables subject matter experts to build their own data apps that adapt from short-term to long-term perspectives, regulatory changes, and heightened reporting expectations. The transparency required to ensure compliance with both LCR and NSFR is facilitated when analysts are able to join data, test assumptions, and collaborate with others.

Native visual analytics unifies data discovery, business intelligence, and real-time visualization in a single, integrated platform.  It provides users with direct access to big data (it runs natively in Hadoop clusters to leverage its capabilities) through an intuitive and self-service interface.  Referring to the examples above, a team can explore billions of rows of highly varied data, join views into interactive packages, and share that package with relevant colleagues to solve problems in a timely, secure, and collaborative way.

(Image via Mr Fish)

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