There are several key areas that banks should consider when solving their liquidity risk optimization challenges. To effectively manage liquidity risk, banks need the right strategy, solution architecture and IT systems – plus management to manage the process. Banks can use the same investment to simultaneously strengthen their risk management systems and further realize business benefits by complying with the law.
Instead of short-term corrections, banks must 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
Optimizing liquidity risk at company level 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 the key to getting accurate results in a timely manner.
- 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 the interaction between financing and risk, which provides a complete picture of risk drivers and capital determination.
- A unified data management platform with embedded data quality features and common metadata for data management and analysis provides a single version of the truth.
Open and flexible configuration
A one-size-fits-all approach rarely serves the unique needs of individual institutions, and the 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 and stem from several directions. Changing market conditions and internal operational problems require rapid reactions so that banks can draw up informed contingency plans. Institutions that finance pipelines and collateral need to become more dynamic through rapidly changing market conditions. Consider that:
- Projection of cash flows requires proper reflection of product depreciation, embedded optionality and unforeseen expenses to provide an accurate overview of liquidity needs.
- What-if, simulation and optimization features enable institutions to make strategic decisions with confidence.
- An open and transparent system allows analysts to perform specialized ad hoc analyzes and explain the results to managers and regulators.
High performance scalable computers
Reliable liquidity optimization requires consideration of future liquidity and growth. To achieve this, banks need to simulate future cash flows and market conditions with greater granularity and across multiple time horizons. This requires a high-performance analysis engine that ensures timely, uninterrupted completion of the process.
Banks are often expected to stress test their liquidity risk and position under various unfavorable scenarios. If a bank is not adequately equipped with high-performance computers, it will struggle to:
- Simulate and analyze liquidity across several 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 financing plans. This requires an improved monitoring and reporting system that provides greater interactive features.
- Monitoring and decision making may require more dynamic reporting of pieces and dice instead of a static (fixed) hierarchy.
- Greater granularity may be required to support drill-down investigations.
- Timely reporting must quickly adapt to evolving liquidity situations.
How SAS can help: An example
Recently, SAS helped a large US bank optimize its liquidity adequacy and positioning (RLAP) processes, liquidity performance resolution (RLEN) and internal liquidity risk stress testing. The size and complexity of the organization made it difficult to assess liquidity gaps and capacity in a timely manner and at the same time meet regulatory expectations for liquidation planning and internal liquidity risk management. Modeling and data management were almost impossible to scale, and excessive manual effort led to high costs and long processing cycles.
SAS provided industry expertise and technology to help the bank consolidate its forward-looking cash flow, security assessment 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 approximately two hours. Algorithm improvements along with automated and highly scalable processes, improved performance and explanations of results. By introducing optimization options, the bank further increased its return on investment and confidence in its settlement planning and liquidity risk management.