For decades, deposits were often treated as a relatively stable funding base whose behaviour could be estimated from long historical patterns. That assumption has become harder to rely on. Digital banking makes money easier to move, customers can compare rates more quickly, and concentrated corporate or wealth balances can react sharply to changes in confidence or pricing. Banks are therefore paying closer attention not just to how many deposits they hold, but to how those deposits behave. Basel Committee - Non-maturity deposit stability, 2026
The shift is creating a new layer of liquidity intelligence. Rather than viewing deposits as a single balance-sheet line, banks increasingly need to understand them by customer type, insurance status, concentration, rate sensitivity, transaction behaviour and likely response under stress. The Basel Committee’s 2026 review of non-maturity deposit stability reflects this concern, noting that technological change, competition and recent banking stress have renewed questions about how deposit behaviour should be modelled. BIS - Fragile wholesale deposits and liquidity risk, 2025
The speed of money has changed the risk
The key change is not that depositors have suddenly become irrational. It is that the cost and friction involved in moving money have fallen. A customer with online access can transfer substantial balances without visiting a branch or waiting for a manual process. Information also travels faster, allowing concerns about a bank or the attractiveness of competing products to spread quickly. Basel Committee - Non-maturity deposit stability, 2026
This compresses the time available for management action. Traditional liquidity frameworks may have been calibrated around outflows that developed over days or weeks. Digital channels can make some movements much faster. That does not mean every bank faces run-like behaviour, but it increases the value of early-warning indicators and near-real-time visibility into unusual flows.
Not all deposits are equally stable
A dollar of deposits can have very different liquidity characteristics depending on who owns it and why it is held. Operational corporate cash used for payroll and payments may behave differently from surplus treasury balances. Retail accounts within deposit-insurance limits may behave differently from large uninsured balances. Wealth clients may be more rate-sensitive than customers who value transaction services or lending relationships. BIS - Fragile wholesale deposits and liquidity risk, 2025
This is why aggregate deposit growth can give an incomplete picture. A bank can report stable total balances while the composition shifts toward more concentrated or expensive funding. Conversely, a decline in low-rate balances may be less concerning if the remaining base is more diversified and relationship-driven. Liquidity intelligence is therefore as much about quality and behaviour as it is about quantity.
Pricing and liquidity are now linked more closely
When interest rates rise or alternative cash products become more attractive, customers can demand higher returns on deposits or move funds elsewhere. The Federal Reserve has documented how rising rates contributed to deposit declines and higher funding costs in parts of the US banking system. This means deposit pricing is not just a margin-management decision; it is also a liquidity decision. Federal Reserve - Banking System Conditions, May 2024
Banks must balance the cost of retaining deposits against the potential cost of replacing them. Wholesale funding can provide a valuable backstop, but it may be more expensive or less stable than granular retail funding. BIS research on fragile wholesale deposits shows how stress in wholesale funding markets can influence banks’ lending terms and maturity transformation. The funding mix therefore has consequences beyond treasury itself.
Behavioural segmentation is becoming more important
More granular analytics can help banks identify which deposit segments are likely to be stable and which may react quickly to market conditions. Useful signals can include account tenure, transaction frequency, balance volatility, concentration, relationship depth and historical response to rate changes. The goal is not to predict individual withdrawals with certainty but to improve the distribution of possible outcomes used in liquidity planning. Federal Reserve - Funding Risks, October 2023
This matters for non-maturity deposits in particular because their contractual availability differs from their observed economic behaviour. Customers may be able to withdraw funds immediately even if a portion has historically remained stable for long periods. Better segmentation allows banks to avoid relying too heavily on averages that may conceal very different underlying behaviours.
Stress testing needs faster scenarios
Recent banking episodes have increased attention on how quickly liquidity stress can develop. A realistic stress test may therefore need to consider not only the total amount of potential outflows but also their timing. The same cumulative withdrawal can have very different implications if it occurs over a month rather than a day. BIS Annual Economic Report 2026
Faster scenarios place more emphasis on operational readiness: which assets can be monetised, which facilities can be accessed, how quickly collateral can be positioned and whether decision rights are clear. Liquidity intelligence is useful only if it connects to actions. The bank needs to know what it would do when a signal changes, not simply observe that the signal has changed.
Customer behaviour can affect product strategy
Deposit analytics can also influence product design. If customers are increasingly sensitive to yield, banks may need clearer tiering, sweep products or savings propositions. If operational balances are particularly stable, institutions may place more value on payments, cash management and transaction-banking relationships that anchor those balances.
This reinforces the idea that deposits are not simply purchased funding. They can be the financial expression of a broader customer relationship. A bank that provides critical day-to-day services may have a different funding profile from one competing primarily on headline rates. Understanding that distinction can shape both commercial strategy and liquidity assumptions.
Technology can improve monitoring but also create model risk
More sophisticated dashboards, machine learning and transaction analytics can identify patterns that traditional reports miss. Yet complexity creates its own risks. A model trained on calm periods may not capture behaviour during stress, while correlations between customer segments can change suddenly when market sentiment shifts. Data quality and governance therefore matter as much as analytical sophistication.
Banks also need to avoid false precision. Deposit behaviour remains influenced by confidence, regulation, market conditions and individual circumstances. The purpose of analytics should be to improve ranges, scenarios and decision-making rather than to produce a single apparently exact forecast.
Liquidity management is becoming a behavioural discipline
The evolution of deposit risk points to a broader change in banking. Balance-sheet management is becoming more connected to customer behaviour, digital technology and real-time data. Treasury teams need to understand how clients use accounts; product teams need to understand the funding consequences of pricing; risk teams need to test assumptions against changing patterns.
This cross-functional view can make a bank more resilient without implying that deposits have become inherently unstable. Most deposits may continue to behave predictably most of the time. The challenge is that the exceptions can matter greatly. In a faster banking environment, the ability to recognise those exceptions early is becoming an increasingly valuable part of liquidity management.
Key questions
What is liquidity intelligence in banking?
It is the use of more granular data and behavioural analysis to understand the stability, concentration, pricing sensitivity and potential outflow patterns of a bank’s funding.
Why are non-maturity deposits receiving more attention?
They can be withdrawn quickly even though a portion may behave as long-term funding, making assumptions about customer behaviour central to liquidity modelling.
Can banks predict deposit withdrawals precisely?
No. The objective is to improve scenarios and early-warning signals, not to treat customer behaviour as perfectly predictable.
References
Basel Committee - Non-maturity deposit stability, 2026
BIS - Fragile wholesale deposits and liquidity risk, 2025
Federal Reserve - Banking System Conditions, May 2024