New attitudes for liquidity risk management

By Wei Chen, Director of Risk Research and Quantitative Solutions, SAS

Liquidity risk management has always been central to financial institutions’ business operations. But ever since the great financial crisis of 2008, regulators have pushed banks to manage liquidity risk more strategically.

In 2008, the Basel Committee on Banking Supervision published its Principles for Sound Liquidity Risk Management and Supervision. Since then, many jurisdictions have included liquidity risk management in their regulatory stress testing and resolution planning requirements. This led banks to invest heavily in systems and processes to ensure they would have a timely, accurate picture of their enterprisewide liquidity risk position.

Recent liquidity risk shocks – such as the liquidity crisis in 2019 and the COVID-19 pandemic – have highlighted the need for agile liquidity risk management and planning systems.

There are several key areas banks should consider when addressing their liquidity risk optimization challenges. To effectively manage liquidity risk, banks will need the right strategy, solution architecture and IT systems – plus governance to manage the process. Banks can use this same investment to simultaneously bolster their risk management systems, further realizing business benefits from regulatory compliance. 

Rather than short fixes, banks should look for a long-term strategic solution that provides:

• Data integration and management.
• Open and flexible configuration.
• Scalable computing capabilities.
• Interactive monitoring and reporting.

Data integration and management

Enterprise-level liquidity risk optimization requires an integrated risk management system. Such a system consolidates various data sources and related models across the asset and liability – as well as collateral – inventory. Data quality is key to getting accurate, timely results.

• Being able to effectively integrate data is especially important for banks with global operations and investments. These banks need to achieve risk aggregation and consolidation across different currencies, local rules, time zones and more.
• An integrated risk management system facilitates interaction between finance and risk, giving a complete picture of risk drivers and capital determination.
• A unified data management platform with embedded data quality functions and common metadata for data management and analytics provides a single version of truth.

Open and flexible configuration

A one-size-fits-all approach rarely serves the unique needs of an individual institution, and requirements may change over time. With a flexible and configurable system, banks can adapt quickly and provide continued return on investment.

But liquidity crises can happen quickly, stemming from multiple directions. Changing market conditions and internal operational issues call for rapid responses so banks can make informed contingency plans. Institutions funding conduits and collateral encumbrance must become more dynamic through fast-changing market conditions. Consider that:

• Cash flow projection requires proper reflection of product amortization, embedded optionality and contingencies to provide an accurate view of liquidity needs.
• What-if, simulation and optimization capabilities enable institutions to make strategic decisions with confidence.
• An open and transparent system allows analysts to perform specialized ad hoc analyses and explain the results to managers and regulators.

Scalable, high-performance computing capabilities

Reliable liquidity optimization requires consideration of future liquidity and growth. To accomplish this, banks need to simulate future cash flows and market states at a greater granularity and across multiple time horizons. This requires a high-performance analytics engine that ensures timely, uninterrupted completion of the process.

Often, banks are expected to stress-test their liquidity risk and position under various adverse scenarios. If a bank isn’t adequately equipped with high-performance computing capabilities, it will struggle to:

• Simulate and analyze liquidity over multiple scenarios.
• Validate compliance with supervisory directives and internal policies.
• Improve response time and be agile.

Interactive monitoring and reporting

Liquidity management and asset allocation activities require banks to track liquidity positions and compliance, as well as document economic and market scenarios and corresponding funding plans. This demands an enhanced monitoring and reporting system that provides greater interactive capabilities.

• Monitoring and decisioning may require more dynamic slice-and-dice reporting instead of a static (fixed) hierarchy.
• Greater granularity may be required to support drill-down investigations.
• Timely reporting needs to quickly adapt to evolving liquidity situations.

How SAS can help: An example

Recently SAS helped a large US bank to optimize its processes for resolution liquidity adequacy and positioning (RLAP), resolution liquidity execution need (RLEN) and internal liquidity risk stress testing. The organization’s size and complexity made it difficult to assess liquidity gaps and capacity in a timely manner while satisfying regulatory expectations around resolution planning and internal liquidity risk management. Modeling and data management was nearly impossible to scale, and excessive manual efforts led to high overhead costs and long processing cycles.

SAS provided industry expertise and technology to help the bank consolidate its forward-looking cash flow, collateral valuation and encumbrance data and analysis. With data management and grid computing technology from SAS, the bank streamlined its scenario-based liquidity planning process from weeks down to about two hours. Algorithm improvements, along with automated and highly scalable processes, enhanced performance and explanations of results. By introducing optimization capabilities, the bank further increased return on investment and confidence in its resolution planning and liquidity risk management.