How SAS® Supports Asset & Liability Management
A holistic, granular approach to balance sheet and liquidity risk management.
Economic volatility and longer-term technology and regulatory trends are driving financial institutions to adopt a more holistic and granular approach to balance sheet management and liquidity risk management. SAS provides powerful analytical, computational and governance capabilities in a scalable, high-performance platform.
Comprehensive ALM functionality
- Includes interest rate sensitivity and stress testing, considering repricing risk, optionality, yield curve and basis risk.
- Enables interest income and EVE simulation using multiple approaches.
- Calculates risk sensitivity measures including durations and convexities for rate-sensitive instruments and Greeks for derivatives.
- Performs scenario-based ALM and liquidity risk analyses on static or dynamic balance sheets.
- Incorporates credit and behavioral assumptions along with macroeconomic and market scenarios.
- Performs risk-adjusted profitability calculations, including funds transfer pricing (FTP).
- Includes regulatory reporting classification and calculation for Basel III liquidity risk ratios (LCR and NSFR) and interest rate risk in the banking book (IRRBB).
Powerful data management with integrated governance & controls
- Supports both ad hoc analyses and fully automated production runs with sophisticated error detection, process monitoring and calculation transparency.
- Efficiently connects to virtually any data source while maintaining full auditability.
- Provides a user-friendly reporting framework that lets business users create their own reports or customize existing reports.
Robust, flexible architecture
- Integrates open source, in-house proprietary, and third-party libraries and risk models.
- Performs timely, on-demand analysis using high-performance execution.
- Adapts to changing business needs and market requirements.
- Provides great scalability with simplified maintenance through cloud-native, microservice-based architecture.
Why choose SAS for asset & liability management?
SAS provides a high-quality, integrated solution for ALM.
Chartis Names SAS a Winner in Seven Categories
Continuing its march up the world’s foremost ranking of the Top 100 risk management and compliance technology providers, SAS bested seven technology award categories, including AI for Banking, Balance Sheet Risk Management, Behavioral Modeling, Enterprise Stress Testing, IFRS 9, Model Risk Management, and Risk & Finance Integration.
Recommended Solutions for Asset & Liability Management
- Kamakura Risk Manager®Get transaction-level valuation, income simulation, liquidity stress testing, cash flow analysis, credit-adjusted economic capital adequacy assessment and regulatory/accounting reporting in a single, integrated ALM solution.
Solutions That Extend ALM Capabilities
- SAS® Solution for Stress TestingMeet the challenges of supervisory stress testing and strategic scenario planning with SAS Solution for Stress Testing.
- SAS® Solution for IFRS 9Accelerate time to benefit and quickly meet IFRS 9 standards.
- SAS® Solution for CECLQuickly meet new US Financial Accounting Standards Board current expected credit loss (CECL) standards with best practices for modeling, workflow and reporting.
- SAS® Model Risk ManagementСократите модельный риск, улучшайте процесс принятия решений и финансовые показатели, соблюдайте требования регулятора к всестороннему корпоративному управлению модельным риском.
Frequently Asked Questions
What is asset liability management?
Asset liability management (ALM) is a process financial institutions use to manage and balance their assets and liabilities in order to mitigate earnings risk and rate repricing risk and to ensure adequate liquidity is maintained while limiting risk to within their stated risk appetite.
Why is ALM important for banks and financial institutions?
An effective ALM program helps financial institutions build and maintain a balanced portfolio, improve earnings, mitigate risk and manage liquidity.
How does ALM help in managing bank liquidity risk?
ALM helps in managing bank liquidity risk by ensuring that banks have sufficient cash and liquid assets to meet their short-term obligations.
What are the key risks associated with bank balance sheet management?
The key risks associated with bank balance sheet management include credit risk, interest rate risk, liquidity risk and operational risk. Failure to adequately manage any of these can also impact reputational risk and trigger regulatory actions.
Regulatory requirements differ by region, but requirements largely leverage guidance from the Basel Committee. The main regulatory metrics employed for liquidity risk management are the liquidity coverage ratio (LCR) and net stable funding ratio (NSFR). Many supervisors also prescribe scenario-based stress testing as a means to ensure liquidity is maintained over adverse conditions.
How do banks use stress testing to manage liquidity risk?
Banks use stress testing to assess their ability to withstand liquidity shocks and ensure they have enough liquidity reserves to meet their obligations over severe, but plausible, adverse scenarios.
What is the role of asset-liability committees (ALCOs) in bank balance sheet management?
ALCOs serve as the internal governing body for asset and liability management at banks. They play a critical role in setting asset and liability targets, monitoring risk exposure and developing strategies to mitigate risk.
How does technology and automation help in managing bank balance sheet and liquidity risk?
Technology and automation help banks streamline their processes, reduce manual errors and key person risks, improve auditability, and improve decision making through richer and more timely insights.
What is the importance of cash flow projections in managing bank liquidity risk?
Cash flow projections allow banks to forecast future cash inflows and outflows and plan accordingly. A range of plausible scenarios should be applied to the forecast exercise.
How can banks optimize their liability management?
Banks can optimize their liability management by diversifying their liabilities and assessing their run-off profile. They should also manage their funding sources using advanced analytics and regularly performed scenario analysis.
What are the emerging trends and challenges in bank financial management?
In light of recent bank failures resulting from inadequate liquidity management practices, banks should anticipate increasing regulatory expectations around their ALM programs. A volatile interest rate environment further challenges balance sheet management. Additionally, as in other industries, financial institutions are facing challenges around finding and maintaining a skilled workforce and operating in a hybrid work environment. To address these challenges, banks are increasingly leveraging technology and analytic advancements, such as cloud computing and AI/ML.