Asset liability management in banks – common challenges and how to overcome them in 2023
In this article we focus on asset and liability management in banks and walk you through typical challenges and best ways to solve them.
We’ll be going through the following topics:
What is asset and liability management?
The role of Asset Liability Management in banks
The main target of Asset Liability Management
Common challenges that banks face
The key ingredients of Asset Liability Management
Behavioural assumptions and market rate changes in scenarios
How does MORS help banks solve their ALM challenges?
Key benefits of MORS Integrated ALM approach
What is asset and liability management?
Asset and liability management, also known as Asset Liability Management (or just ALM), is managing the maturity and interest rate risk mismatch, between a bank’s assets and liabilities. This mismatch, often referred to as maturity transformation, causes both interest rate risk and liquidity risk for banks. The risks are due to assets and liabilities having different maturities and different interest rate repricing tenors. Managing these risks is the very reason for bank’s existence.
Interest rate risk exposure typically affects a bank through instantaneous market value changes and income changes over time. In a case when interest rates would rise, a bank might experience a negative market value change. At the same time, their Net Interest Income (NII) would improve over time as their assets (lending to the public) would reprice faster and with higher rates, than their liabilities (borrowing from the public).
Liquidity risk is also related to the maturity mismatch. What if the money at customer accounts (i.e. liabilities for the bank) would be withdrawn and no new funds could be raised to replace the withdrawn money?
At the end of the day, banks’ ability to perform and manage maturity transformation with Asset Liability Management, is the background and ultimate reason for a bank to exist.
The role of Asset Liability Management in banks
Managing the risks (interest rate risk and liquidity risk) caused by maturity transformation is the main purpose of Asset Liability Management in banks. ALM plays an important role in managing and mitigating the risks in the bank’s everyday business as well as in the long-term. ALM has to balance the associated risks and optimise accordingly. The profit of a bank is largely formed by the difference between interest income received from the assets and interest expense paid for the liabilities.
The main target of Asset Liability Management
The difference between interest income and interest expense is called Net Interest Income (NII). The target is to aim for predictable and steady income for the bank over time. Accordingly, the most important goal of ALM is to stabilise and maximise Net Interest Income (NII) for the bank, over time against all risks.
NII has been under severe pressure for many years now in most developed banking markets. Generally lower market rates and competition for market share in core lending markets, such as residential mortgage lending, has led to decreased NII margins. ALM needs to balance all risks and optimise NII over time, in this challenging environment.
So, ALM targets to keep the NII as high and stable as possible. This has to be balanced against the interest rate risk and liquidity risk consequences. Further constraints include regulatory capital requirements.
Common challenges that banks face
ALM is not easy. The risks faced by the banks are multi-dimensional and interconnected. Also, the balance sheet of a bank is typically quite multifaceted and full of complex details linked to risks.
The following are typical challenges that banks face from an Asset Liability Management point of view. These challenges can cause significant profitability effects for a bank.
Challenge #1 – Change in Interest rate risk level
The Interest rate changes and the general interest rate risk level may change for different reasons. When interest rates change, the present values and future cash flows change. Interest rate risk management is thus critical to the stability of banks.
Challenge #2 – Change in Customer behaviour
Customer behaviour is constantly changing. Banks are exposed to these changes, through the possibility offered to their customers to pre-pay their loans or withdraw savings without prior notice. Customers using these possibilities may cause unpleasant surprises for the bank, as the interest rate and liquidity risk may change unexpectedly. It is important to stay at the forefront of possible changes in customer behaviour. It is also important for banks to be able to measure the effect from changed customer behaviour on interest rate and liquidity risk. Being on the front foot of customer behaviour enables banks to react quickly and to protect their Net Interest Income.
Challenge #3 – Managing risks within internal and regulatory constraints
Requirements faced by ALM in banks are continuously changing and are in most cases increasingly challenging. Both, the internal risk policies and limits, as well as the regulatory requirements, are important and must be fulfilled.
Challenge #4 – Balancing between changes in value and changes in net interest income
Balancing between changes in values of the Assets and Liabilities and changes in Net Interest Income is a further complicated challenge for a bank. The potential changes in value are expressed as Economic Value of Equity (EVE), and the potential changes in NII are expressed as Earnings at Risk (EaR). The challenge is that improving EVE might mean a contradictory effect for EaR, and vice versa.
The key ingredients of Asset Liability Management
The three key ingredients ensuring successful ALM, include being prepared for changes in market rates and changes in customers’ behaviour:
- Liquidity Buffer – Banks must have a liquidity buffer to counterbalance unexpected outflows, such as withdrawn liabilities. When customers withdraw funds from their accounts, or other funding cannot be renewed, the liquidity buffer is required to fund the gap. The liquidity buffer typically consists of cash and high-quality assets, such as government bonds.
2. Modelling Assets and Liabilities – Banks must understand the optionality related to customer behaviour and the implications of any changes. This requires modelling assets, such as pre-payment of lending, and liabilities, such as sudden withdrawal of money from current accounts. Alternatively, current accounts or non-maturity deposits (NMD), may stay very stable over time, both from a liquidity and interest rate perspective i.e. they may not be sensitive to market rate changes.
3. Scenario Analyses – ALM departments need capabilities to simulate the outcome of alternative scenarios. Scenario analyses are needed as the risks faced are multi-dimensional and often mutually contradictory. As mentioned earlier, banks and ALM try to optimise and stabilise NII over time. When facing different risks and the regulatory requirements, sometimes risk management actions made with good intentions can lead to un-intended consequences. Using scenarios banks are able to optimise their balance sheet and profitability.
Best practices
The key ingredients as building blocks of the best practice
As such the above-mentioned key ingredients are clear and very understandable tasks. ALM managers need to determine the size of the liquidity buffer to cover a potential liquidity shortfall under severe stress and to cover the prudential requirements. ALM managers need to estimate the pre-payment risk of assets that might pre-pay earlier than agreed. For the liabilities, ALM needs to understand and model how customer savings will behave over time, both from a liquidity and interest rate risk sensitivity point of view. Equally, ALM needs to balance NII optimisation against all other constraints. This all might sound very easy.
While the above-mentioned tasks might sound very easy, the complexity is embedded in the details. One of the difficult details is to be able to correctly set behavioural assumptions and combinations of assumptions.
Behavioural assumptions and market rate changes in scenarios
Each combination of these behavioural assumptions could be called a scenario. As the assumptions are affecting each other, banks often end up setting or testing several alternative scenarios. Further on, the complexity increases as each of these scenarios can be exposed to several combinations of market rate changes. Additionally, some of the scenario assumptions are based on management’s views or concerns. Finally, many scenarios are also required by supervisory regulators and the regulatory assumption might not match the assumptions set by management.
The best practice ALM is to be able to create several understandable assumptions for each of the key ingredients and to run the scenarios with alternative simulated changes. A such combination of alternative simulated scenarios, against the constraints and limits received from management and regulators, should be run over and over again.
Some scenario outcomes can be seen so critical and have such an adverse effect on the bank, that the bank cannot afford the scenario to materialise. Such risks need to be hedged. Some outcomes, even very adverse outcomes might be so unlikely that management decides not to hedge for them. Other outcomes might have lower effect than the management or regulatory required risk appetites and limits, and hence the decision is to accept the risk level.
As a summary, the best practice ALM includes running a large number of scenarios with understandable assumptions on known ingredients and their possible changes.
How does MORS help banks solve their ALM challenges?
Many banks are struggling with their ALM challenges, setting their assumptions for the key ingredients and adopting a best practice ALM operation.
MORS ALM system helps the banks to overcome the challenges, by allowing them to set any number of assumptions for the key ingredients and to run a successful best practice ALM operation.
The main reasons why banks are looking for a solution such as MORS are easy to identify.
For ALM operations in banks it is essential to be able to:
- Gather complex and granular balance sheet and market data information easily, transparently and in an easily repeatable manner.
- Create understandable scenarios for internal and external requirements.
- Calculate scenarios quickly and in a cost-efficient manner.
- React on the results with quick and informed decision making.
MORS Integrated ALM is designed to help banks meet and overcome these challenges. The information required for risk management and decision-making is constantly and automatically updated in MORS. The level of detail of the data is high, so it provides very in-depth information to facilitate decision making.
The challenges of Asset Liability Management are made transparent and easier to manage with our solution. You don’t need many different systems for different things, but instead you can monitor, manage and report all aspects of interest rate risk and liquidity risk in one system. Read more about MORS integrated ALM.
Key benefits of MORS Integrated ALM approach
Why do we and customers believe that MORS integrated ALM solution is the right solution for ALM operations in banks?
Key benefits of MORS integrated ALM:
- MORS is very user-friendly, which is one of its main advantages.
- MORS has intuitive scenario generation and stress testing functionality.
- MORS data is always up to date which speeds up the bank’s ability to react to changes in the market.
- The level of granularity and the overall quality of MORS data helps making good decisions quickly.
- Customers can benefit from MORS forward-looking analytics which drive profitability through balance sheet optimisation.
- MORS is highly cost efficient with low maintenance costs.
- New versions are always included in MORS license.
Further questions on MORS ALM – please ask our experts
Please contact our experts if you would like to discuss how MORS can help overcome your banks ALM challenges. By engaging with us in a dialogue about your bank’s requirements, we will be able to help you fulfill your ALM system needs.
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