Many forces are at play behind modern asset liability management (ALM) strategies and liquidity risk management programs at banks and other financial institutions. Let’s explore why it’s so crucial for banks – at business and functional levels – to modernize their ALM and overall balance sheet management processes.

Why change is needed now

Regulatory initiatives and market infrastructure changes over the last decade have led to extensive and rigorous requirements around stress testing, liquidity risk management and capital planning. While banks have spent considerable time and resources to meet these requirements, many have developed these programs in isolation from each other. Inevitably, these bespoke approaches resulted in process duplication, multiple versions of the truth, and bottleneck reconciliation issues across multiple functions at banks.

As part of their regulatory stress testing and capital planning exercises, banks are required to produce financial and business plans for surviving adverse and severe economic conditions. They also need to establish a base case scenario(s) for the evolution of their financial conditions and businesses. The resulting information that gets submitted to the regulators is sometimes of much higher quality than what's produced for the board and executive management of the bank.

All the same, bank boards and executive management are increasingly expected to maintain a detailed understanding of the bank’s risk appetite and how the bank manages risks. The boards are required to fully understand the overall integrated risk profile of the bank and to challenge business, financial and capital planning assumptions.

Along with the changing demands, regulatory compliance and strategic planning are converging. Today, banks are expected to integrate the analyses performed under regulatory requirements into their financial planning and budgeting exercises.

Where should integration begin?

A bank’s ALM platform is the most natural place for integrating these programs. It’s where balance sheet and bank management processes – like financial and capital planning, contingency funding planning, recovery and resolution planning, stress testing and regulatory reporting – converge. However, experience reveals the limitations and shortcomings of traditional ALM platforms, which tend to be:

  • Tailored for regular “standardized” runs and reporting and compliance with static environments.
  • Unfriendly for users, with slow interfaces that don’t facilitate rapid user input or model (re)parameterization.
  • Fragmented and siloed by risk type and organizational function. Differences in data definitions, aggregations and timing create convoluted and complex comparisons of analytical results and prevent carrying out more holistic analyses.
  • Expensive, slow and based on legacy technologies with performance or scalability problems. As such, traditional ALM platforms struggle to deliver quick business intelligence and insights for course-correcting and/or decision making (for example, due to pandemic and climate risk scenarios).
  • Too process-intensive for ad hoc and what-if scenario analysis.

As newly established processes evolve into a business-as-usual mode – via regulatory requirements for larger banks and as leading practices for others – the traditional ways of doing asset liability and balance sheet management are becoming both cost-prohibitive and unsustainable. Many banks are now seeking to unify their data, models and processes across the organization to improve efficiencies and extract the business intelligence generated within these sometimes-disparate processes.

Clearly, there’s a critical need for modernizing asset liability management in banks. Only then can the ALM function evolve into the integrating platform for these key bank management processes.

The path forward

Modernizing asset liability management in banks requires that data, models and processes become more unified across the organization to support an integrated approach to balance sheet management. However, the ALM function itself also needs to evolve to adopt a number of new features:

  • Scenario-based analytics, with the ability to perform ad hoc and stylized analyses with short turnaround times.
  • Transparent and agile interest rate risk and liquidity risk management, with seamless support for open source and third-party components
  • An ability to model cash flows of individual financial instruments and assess the unique risks arising from changes in interest rates or borrower behavior for each position.
  • Support for flexible application of behavioral assumptions using a variety of modeling techniques, including emerging machine learning and other AI technologies.
  • Tighter integration with stress tests, allowing for a quick assessment of liquidity, interest rate and other balance sheet risks over a range of economic and business scenarios. This is necessary to assess uncertainties in business and strategic plans and to inform efforts for maximizing profits while managing risks to stay within the institution’s appetite.
  • Dynamic balance sheet modeling over multiple horizons, with highly granular hierarchies and adequate attribution capabilities to quickly identify risks arising across the balance sheet.
  • Cloud-native capacity for fast processing of analytics with on-demand scalability. This allows for modeling cash flows at the financial instrument level and assessing sensitivities from risks or borrower behavior for each position.
  • Being friendly to open source and third-party components, common business objects, data repositories, and workflows for multiple risk and balance sheet management solutions.

Benefits of modernizing asset liability management in banks

Modernizing the ALM function and its technology platform is not all about costs.

As the global economy struggles to regain its footing in the wake of COVID, banks face increasing uncertainty around interest rates and other risks arising from the prolonged pandemic and resulting governmental actions. To remain competitive, banks must have a modern analytics infrastructure that provides a rapid and holistic view of their asset base and overall balance sheet.

A modernized platform allows banks to integrate advanced, dynamic stress testing and analysis tools into their budgeting and strategic planning exercises, complete with a coherent view on capital and liquidity requirements. Modernizing the ALM function and its technology platform is the prerequisite for establishing an integrated approach to risk and balance sheet management at banks. And it’s essential for gaining a long-term competitive advantage.

Learn how SAS can help you with asset and liability management

About Author

Hovik Tumasyan

Principal Industry Consultant at SAS

Hovik has over 20 years of experience in the financial sector, developing and delivering risk and balance sheet management solutions for financial institutions ranging from credit unions and regional banks to global investment banks and multilateral institutions. He has over fifteen years of experience with global management consulting firms E&Y in London and PwC in Toronto. Hovik holds a Ph.D. in Physics from the University of Miami and a Post-graduate Degree in Mathematical Finance from the University of Oxford.

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