For much of the past decade, liquidity was abundant and readily available at relatively low cost, allowing many banks to focus primarily on meeting regulatory requirements and maintaining comfortable buffers above minimum levels.
That environment is changing as liquidity becomes more costly, funding markets more competitive, customer behavior more dynamic, and balance sheet resources more constrained. This article explores what these changes mean for liquidity management and how banks can build a more anticipatory and integrated approach, supported by better data, behavioral modeling, dynamic scenarios, governance, and decision-making.
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Banks are competing for funding in an environment where their stability, characteristics, cost, and currency increasingly matter. This is particularly relevant for liquidity in currencies that are not naturally generated by an institution’s domestic funding base, especially the US dollar. European banks rely on dollar funding to support parts of their long-term wholesale business, while banks in the Middle East can face additional challenges accessing international liquidity beyond their domestic currencies. Geopolitical tensions, together with developments in US inflation and interest rates, can add further pressure to the availability and cost of dollar funding.
These pressures sit alongside capital requirements, recurring issuance needs, and balance sheet growth, placing additional demands on available resources. Managing liquidity requires a clearer understanding of the position, how it may evolve under different market and currency conditions, and how liquidity decisions interact with the rest of the balance sheet.
The advantage may not necessarily come from holding more liquidity, but from understanding the position more accurately, anticipating needs earlier, and making better-informed decisions about how liquidity, funding, capital, and other balance sheet resources are deployed.
The liquidity environment facing banks today is very different from the one that followed the global financial crisis, when years of abundant central bank liquidity, asset purchase programs, and favorable funding conditions allowed many institutions to operate with substantial liquidity buffers. As those conditions normalize, funding costs and competition for resources are becoming more relevant to balance sheet management, particularly as liquidity has to be considered alongside capital requirements, MREL and TLAC needs, internal buffers, and recurring issuance programs.
Currency adds another layer to this competition, as banks may need to secure liquidity across several currencies rather than simply meet their aggregate funding needs. The challenge is particularly relevant for US dollar funding when the dollar is not naturally generated by the markets where an institution has its main sources of liquidity. This can affect European banks with significant long-term wholesale business in dollars, as well as banks in the Middle East, where geopolitical tensions can further complicate access to dollar liquidity. Competition for funding also has a currency dimension, with the availability and cost of US dollars becoming an important consideration for institutions with material dollar needs.
These constraints are closely connected because the same balance sheet has to accommodate them simultaneously. An issuance decision can affect the funding and maturity profile, NSFR, liquidity capacity, financial cost, and the capacity available to support future business, while new lending requires funding and also has implications for liquidity, stable funding, and capital consumption. Understanding these interactions is increasingly relevant to the economics of new business and to decisions around pricing and profitability.
This is particularly visible in deposit management, where the volume of funding raised remains important but provides only part of the picture. Two deposit balances of the same size can have very different liquidity implications depending on the customer relationship, transactional activity, concentration, pricing sensitivity, and expected behavior under different conditions. A large balance attracted primarily through pricing, for example, may behave differently from a deposit embedded in a broader operational relationship.
Understanding these differences requires information that goes beyond the balance itself and captures how customers use their accounts, the nature of the relationship, and how their behavior evolves over time. The challenge is becoming more complex as AI democratizes access to financial knowledge, giving customers tools and information that were once largely the domain of financial experts. Customers can compare rates, products, and alternatives more easily and respond more quickly as conditions change, potentially altering behaviors that banks have historically relied on for modeling. Historical data remains essential, but it may no longer be sufficient on its own to anticipate future behavior with the same degree of confidence. This gives banks a richer view of their funding base and allows them to assess its expected stability, cost, and concentration while incorporating behavioral characteristics more effectively into liquidity planning. As competition for stable funding increases, understanding these characteristics becomes increasingly relevant to how banks manage their liquidity.
Regulatory metrics remain fundamental to liquidity management, with LCR, NSFR, internal limits, and the broader ILAAP framework providing important safeguards for ensuring that banks can withstand liquidity stress. Regulatory compliance, however, does not by itself explain how effectively an institution understands and manages its liquidity position, particularly when banks may maintain ratios comfortably above regulatory and internal thresholds for a combination of prudential and business reasons.
The amount of additional management headroom can also be influenced by the confidence an institution has in its data, forecasts, behavioral assumptions, and ability to anticipate changes in its position. Incomplete or insufficiently granular data can weaken behavioral assumptions and reduce confidence in projections and scenarios, making future liquidity positions more difficult to anticipate and potentially encouraging more conservative management decisions. Uncertainty at one point in the process can consequently influence the degree of confidence management has in the position as a whole.
Better information and stronger forecasting do not imply that banks should operate closer to regulatory minima. They provide management with a more reliable basis for determining how much liquidity is appropriate for the institution’s risk profile, risk appetite, business model, and expected conditions, while improving its understanding of why that liquidity is being held and how the position could evolve.
Regulatory ratios are an essential part of the picture, but institutions with similar balance sheets and similar ratios can still have very different capabilities to understand, anticipate, and manage their liquidity. Those differences depend increasingly on the information, assumptions, and processes behind the reported position.
The difference between two banks with similar balance sheets may lie less in the amount of liquidity available than in their ability to use the information behind it. Data quality, behavioral assumptions, coordination between functions, and operational processes all influence how clearly management can understand the position and how quickly it can respond when conditions change.
Granular, reconciled, and timely information provides greater visibility into concentrations, customer behavior, collateral availability, and future funding needs. When this information is shared consistently across Treasury, Risk, Finance, ALM, and the business, the institution can assess liquidity decisions in the context of their wider balance sheet implications.
Fragmented information creates a different management environment. Functions may work with different versions of the same data, assumptions may be difficult to reconcile, and forecasts may carry greater uncertainty. Even with an apparently strong liquidity position, these limitations can make it harder to anticipate needs, coordinate responses, and mobilize available resources efficiently.
This is the sense in which liquidity can be optimized. Accumulating liquidity is costly, particularly when that liquidity cannot be deployed productively, so the objective is to understand future needs accurately and identify the management actions that provide sufficient flexibility to respond as the position evolves. This gives the institution the capacity to maintain liquidity at appropriate levels to support its business plans while preserving its risk profile and solvency.
Effective liquidity management starts with granular and reliable information. Accurate balances are essential, but banks also need to understand the characteristics behind them, including customer behavior, transactionality, concentration, contractual terms, pricing sensitivity, and collateral availability. The information needs to be reconciled, traceable, and available frequently enough to support management decisions.
For deposits in particular, greater granularity can provide insight into:
How accounts are actually used and the level of transactional activity
The depth of the broader customer relationship
Sensitivity to changes in pricing
Concentrations and potential withdrawal behavior
How these characteristics evolve over time
The value lies in capturing information that helps explain behavior rather than simply accumulating more data. It also means making better use of information already available across the organization. Where different functions enrich the same underlying data independently, inconsistencies can emerge, and useful information may remain confined to individual teams. Reusing that enrichment through a more common data foundation can provide a more consistent basis for analysis.
Behavioral models build on this information by translating observed patterns into assumptions about how the liquidity position may evolve. Contractual positions alone cannot always explain deposit behavior, expected withdrawals, refinancing capacity, and collateral availability. The credibility of these assumptions depends on the quality of the underlying data and on the bank’s ability to validate and update them as behavior changes.
Scenario analysis then allows management to examine how the position could develop under different conditions. Alongside established regulatory and internal stress scenarios, banks benefit from the ability to test changes in customer behavior, funding access, deposit outflows, collateral availability, credit growth, or market conditions as circumstances evolve. This makes scenario analysis useful for ongoing management decisions and for assessing potential actions before they are required.
The resulting information needs to reach decision-makers consistently and with sufficient frequency. Automation can shorten production cycles and reduce manual reconciliation, while early-warning indicators can help identify material changes and their underlying drivers earlier. The objective is to provide Treasury, Risk, Finance, ALM, and the business with a consistent view of the balance sheet that they can use as the basis for decisions.
Data, models, scenarios, and management information are parts of the same management process. Their value ultimately depends on the confidence they provide in understanding the liquidity position and the decisions that follow.
Liquidity decisions inevitably interact with other balance sheet objectives. Changes in funding, asset composition, collateral, or commercial activity can affect liquidity alongside funding costs, margins, capital consumption, interest rate risk, and the capacity available for new business. Managing these effects requires the relevant functions to evaluate them together.
ALCO provides the natural forum for this coordination, bringing together different perspectives across the bank:
Treasury: liquidity, funding, market access, and funding costs.
Risk: risk appetite, limits, scenarios, and independent challenge.
Finance: margin, planning, and financial performance, FTP.
ALM: balance sheet structure and sensitivities.
Business teams: customer behavior, pricing, and expected growth.
Bringing these perspectives together allows management to see the wider implications of individual decisions. A funding action may strengthen the liquidity position while increasing financial cost, while changes in the asset or liability structure can affect margin, IRRBB, capital, or other balance sheet metrics. An integrated view makes these trade-offs visible and helps management determine the appropriate response for the institution as a whole.
Liquidity and funding costs also need to be reflected in the economics of products and businesses. Funds Transfer Pricing can transmit these costs internally, linking funding and liquidity characteristics to pricing and profitability decisions so commercial decisions more accurately reflect the balance sheet resources they consume.
Technology supports this process by connecting data, models, scenarios, and management information and making them available with the speed required for decision-making. Its value goes beyond calculation speed, improving the institution’s ability to identify changes, understand their implications, evaluate possible responses, and incorporate that information into the decision process.
These capabilities are also relevant to the supervisory assessment of liquidity management. Reliable data, credible assumptions, effective governance, and clearly defined management processes help an institution demonstrate how liquidity risk is identified, measured, monitored, and managed, reinforcing the importance of liquidity management as an integrated capability rather than primarily as the production and monitoring of regulatory metrics.
Liquidity optimization does not necessarily mean holding less liquidity. It means understanding the position well enough to determine what the institution needs, how that liquidity may behave, and how available resources can be managed within its risk appetite and regulatory constraints.
Two banks can operate with very similar balance sheets and regulatory ratios while having very different capabilities to manage their liquidity. The difference lies in the quality of the information and assumptions behind the position, the coordination between functions, and the ability to anticipate how liquidity needs may evolve. With funding costs, customer behavior, and balance sheet constraints increasingly interconnected, this understanding gives management greater confidence in assessing trade-offs, allocating resources, and making informed decisions across the balance sheet. The ability to understand and manage available liquidity is what turns liquidity capacity into a more effective resource for balance sheet management.
Mirai ALM & Liquidity brings data, behavioral modeling, scenarios, liquidity metrics, and balance sheet analysis into a single environment, helping banks build a more consistent view of their position and assess how it may evolve under changing conditions. Integrated with Mirai FTP & Profitability, it also connects liquidity and funding costs with pricing and profitability, supporting more informed decisions across the balance sheet.
Why isn't a strong LCR or NSFR ratio enough on its own?
Ratios confirm compliance but don't reflect the quality of the data, behavioral assumptions, and forecasting confidence behind the position — two banks with identical ratios can have very different real capabilities.
What makes deposit quality different from deposit volume?
Two deposit balances of equal size can carry very different liquidity implications depending on customer relationship depth, transactional activity, concentration, and pricing sensitivity.
What role does ALCO play in liquidity decisions?
ALCO brings Treasury, Risk, Finance, ALM, and business teams together to evaluate liquidity actions alongside their effects on margin, capital, and IRRBB, rather than in isolation.
How does behavioral modeling improve liquidity management?
It translates observed patterns—deposit stability, refinancing capacity, withdrawal behavior—into forward-looking assumptions that contractual maturities alone can't capture.
What does "liquidity optimization" mean if it isn't holding less liquidity?
It means understanding the position accurately enough to know what's genuinely needed, how it may evolve, and which actions preserve flexibility, rather than defaulting to larger buffers.
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