Current market conditions have shown that Asset and Liability Management cannot be ignored. In some jurisdictions, regulation is being introduced such as the EU with updates to the Interest Rate Risk in the Banking Book (IRRBB) guidelines. However, regardless of the regulations in a particular jurisdiction, all banks need to be able to manage their balance sheet in such a way that inspires confidence, internally and to their customers, the market and regulators alike. This is a cornerstone of broader good business management.
To achieve this, banks need to know that their Asset Liability Management (ALM) foundation is robust and agile enough to respond to their evolving needs. This includes modelling the balance sheet, projecting Net Interest Income (NII) and Economic Value of Equity (EVE), and undertaking scenario analysis and stress testing to assess the impact on Key Performance Indicators (KPIs). Only this will give markets, customers and regulators the confidence that they, in turn, need.
Can your ALM system replicate your portfolio from a sensitivity point of view?
2023 has seen several banks in difficulty, with some failing and regulators stepping in to prevent contagion and a wider banking industry crisis. We have seen the realities of the interconnectivity between interest rate risk, liquidity risk and concentration risk, and the role of market confidence. Confidence challenges may see depositors wishing to move to perceived safer institutions, while at the same time banks must be able to cope with the operationalization of data and aggregation. They need their ALM systems to be flexible yet dependable.
An ALM professional today must be able to fully replicate the portfolio from a sensitivity point of view when modelling the balance sheet. They need to be able to replicate cashflow including complex structured products and embedded optionality. It is important they can do this beyond the vanilla. Accuracy and reliability in replicating cashflow is at the heart of robust ALM and it is critical that ALM practitioners can demonstrate what is happening within the portfolio right now. They need to be able to calculate Net Interest Income (NII) and Economic Value of Equity (EVE) and project these over time to assess earnings and the value of assets.
There is a trend of convergence in asset and liability management due to the regulatory direction, and over time common processes are evolving. However, it is not possible to standardize everything as all banks have their own, specific liabilities and deposits and their interactions will be different.
Can your system cope with demands for stress-testing and scenario analysis?
As a result, the stress testing and scenario analysis demands on banks today are high. They need to be able to show the impact on NII and EVE and understand the effect on all their KPIs. In particular, they need to be able to model the links between macro factors and risk factors, for example how an impact on cashflow can lead to an impact on credit spreads which can lead to a counterparty risk. Speed and accuracy are vital when performing large and complex scenario analyses such as these, particularly with short deadlines. Banks need to be able to rely on the flexibility and short processing times associated with the cloud, without sacrificing any trust or generating any data privacy concerns.
A credit stress model needs to be able to respond consistently to multiple scenarios. For example, it may need to model higher interest rates leading to a slow-down in the economy, or pressures on commercial real estate in the post covid world with a lower need for office space. A bank needs to be able to project every contract to the budget horizon. The level of detail, planning and forecasting must be consistent and trustable.
The platform needs to be adaptable and extensible to be responsive to evolving requirements. Simulation is only useful if it is connected to the actual balance sheet and uses real data.
For example, a bank may need to run scenario analysis, including stress testing non-interest bearing deposits if there is a move to a higher interest rate. They need to understand potential deposit decay and deposit beta. Banks need confidence in their analytics to trust the outcomes. They also need to be able to respond to changing market conditions. In previous times, banks were caught needing to model negative interest rate and had to adapt accordingly. Recently this has been the norm but now there is a requirement to look at inverted yield curves. Banks need to be able to demonstrate that their ALM system can respond to market conditions as they occur in order to maintain confidence and trust.
A bank’s portfolio is too complex to model conceptually so a true, interoperable platform is needed in order to create the cashflow replication that is needed. It is too complicated for a one fit solution; there are too many aspects to consider around the balance sheet and the modelling required is complex. ALM professionals need results that are accurate, reliable and actionable.
Does your ALM platform inspire confidence and trust with its reliability and accuracy?
Ultimately, all banks need a balance sheet that it managed effectively and is trustable. Demonstrating this is vital to any bank looking to generate confidence with their customers, regulators and the market in general. In some cases, banks may have previously been outsourcing their ALM operations, but current market challenges are driving them to consider bringing it inhouse. Banks that place robust ALM at the foundation of their business inspire confidence and this stability is attractive to depositors and builds sustainable growth for the future of the business.
ALM is rightly top of mind today for the banking industry, its regulators and customers. The market is contending with challenging environmental factors, a reduction in confidence and some individual banks are succumbing to a liquidity crisis. These market conditions demonstrate need for robust, adaptable, reliable and strong ALM system, irrespective of any regulatory pressures that might apply. In all markets, even those without specific regulation such as the US, banks need robust liquidity analysis in order to protect the portfolio, the balance sheet and ultimately the whole bank. We are entering a new phase in balance sheet management in the banking industry. Banks that act now will be well positioned to, not only meet these challenges, but thrive.
Protecting the portfolio, the balance sheet and ultimately the whole bank with an integrated solution
Wolters Kluwer OneSumX is an extensible, adaptable, trustable platform that provides actionable insights for its clients. It projects cash flows at contract level for any scenario, enabling true portfolio analysis. Stress testing is possible on a huge range of scenarios which can include macro factors and risk factors such as market, credit, behavior, and business strategy. Banks can access both internal and regulatory measures such as the Liquidity Coverage Ratio (LCR) and the Net Stable Funding Ratio (NSFR), as well as IRRBB measures. The solution also allows for collaborative development with clients to continue to the virtuous circle.
The increasing digitization of banking systems means that lower risk can be achieved with an integrated platform. Such a platform can integrate data, processes, and workflow with a comprehensive layer of ALM. Get in touch to see how OneSumX ALM can provide your bank with a powerful solution to liquidity reporting requirements.