Non-Maturity Deposits (NMDs) are often seen as one of the more stable parts of a bank’s funding base.
Current accounts, savings accounts, and operational balances do not have a contractual maturity date. On the surface, that gives the impression of stability.
But in practice, these balances can be one of the most difficult areas to assess properly from a treasury, ALM, and liquidity management perspective.
Because the real issue is not whether these balances mature.
The real issue is:
How much of them are likely to remain, and for how long?
That is a very different question.
And it is one that many institutions still do not answer as well as they should.
The Core Problem with Non-Maturity Deposits
Unlike term deposits, NMDs are behavioral.
They are influenced by:
- customer habits
- pricing sensitivity
- market confidence
- interest rate changes
- competition
- seasonality
- operational flows
- liquidity conditions
That means the balances can look stable for long periods — until they suddenly do not.
This creates a major challenge for treasury and ALM teams.
Because if NMD assumptions are too optimistic, they can distort:
- liquidity planning
- funding stability assumptions
- internal FTP logic
- runoff expectations
- stress testing outcomes
- behavioral maturity views
- management decision-making
In short:
NMDs may not have contractual maturity, but they absolutely carry behavioral risk.
And that risk is often underestimated.
How Many Banks Still Handle This Today
In reality, many banks still rely on methods that are understandable from a practical point of view — but often too simplistic for forward-looking decision support.
Typical approaches still include:
1. Static runoff assumptions
A bank may assume that a fixed percentage of balances is “stable” and another portion is “less stable,” then carry that assumption forward.
2. Historical averages
Some institutions rely on average balance behavior over past periods and apply broad judgment on top.
3. Spreadsheet-based maturity layering
Deposits are often manually bucketed into assumed behavioral maturities, with adjustments made through internal ALCO or treasury discussion.
4. Judgment-heavy overlays
In some cases, the final view depends heavily on expert judgment rather than a repeatable analytical framework.
To be fair, these methods are not “wrong.”
They are often used because they are familiar, manageable, and easy to explain internally.
But they also have clear limitations.
Where Traditional Approaches Become Weak
The issue is not that traditional methods are useless.
The issue is that they often become too static in an environment where deposit behavior is not static at all.
Some of the common weaknesses are:
They often assume stability instead of testing it
A balance that has been stable historically is often treated as if it will remain stable going forward — without enough analytical challenge.
They may not capture changing rate environments
When interest rates move, customer behavior can change. Static assumptions often fail to reflect that properly.
They can hide segment differences
Retail and wholesale balances do not behave the same way. Nor do all currencies or customer groups.
They are difficult to scale consistently
Once spreadsheets become large and heavily manual, consistency and explainability can weaken.
They are often backward-looking
Many traditional approaches describe what happened. They are less effective at showing what may happen next.
And that is the key point.
Banks do not only need historical explanation. They need forward-looking visibility.
Why Forward-Looking NMD Analysis Matters
A treasury team does not just need to know what balances did last quarter.
They need to understand what those balances are likely to do next month, next quarter, and beyond.
That matters because forward-looking NMD analysis supports better decisions in areas such as:
- liquidity planning
- funding strategy
- internal pricing and FTP discussion
- stress and sensitivity review
- management reporting
- scenario discussion
- behavioral stability monitoring
Without that, institutions are often left with one of two problems:
- they overestimate stability, or
- they remain too dependent on manual assumptions
Neither is ideal.
What a More Supportive Approach Should Do
A stronger NMD process should not just produce a number.
It should help the bank understand deposit behavior more clearly and support decision-making more confidently.
That means a useful solution should be able to:
- separate balances by currency and segment
- distinguish retail vs wholesale behavior
- identify historical stability and runoff trends
- support forward-looking forecasting
- incorporate interest rate sensitivity where relevant
- produce outputs that are usable for management discussion
- reduce dependency on heavily manual spreadsheet work
In other words, it should move the process from:
“What assumption should we use?”
to:
“What does the data suggest, and how should we interpret it?”
That is a much more useful position for treasury and ALM teams to be in.
How NMDHub Supports This Process
This is exactly why we developed NMDHub.
The objective is not simply to automate a spreadsheet.
The objective is to provide a more structured, forward-looking, and decision-useful way to assess Non-Maturity Deposit behavior.
What the app does
NMDHub allows users to upload historical deposit data and generate a structured view of likely future balance behavior.
It is designed to support a more practical forecasting process by helping institutions move from broad assumptions toward a clearer analytical view.
What it supports
The app is built to support:
- historical NMD balance uploads
- forecasting by currency
- forecasting by segment
- differentiation between retail and wholesale balances
- optional use of interest rate input
- structured runoff and survival interpretation
- management-style reporting outputs
- a clearer basis for internal discussion and review
Rather than relying only on static assumptions, the tool helps create a more evidence-based and forward-looking perspective.
What the App Shows
This is where it becomes particularly useful for internal teams.
NMDHub is not only about producing a forecast line.
It helps show:
1. Forecasted balance behavior
How balances may evolve over the selected forecast horizon.
2. Runoff interpretation
How much balance appears likely to decay or remain over time.
3. Survival rate perspective
A clearer view of what portion of balances may continue to behave as stable funding.
4. Segment-level behavior
Which categories or balance groups appear more stable or more sensitive.
5. Forward-looking management insight
A more practical basis for discussion in treasury, ALCO, and planning conversations.
That is important because management teams do not just need charts.
They need something that helps answer:
- Which balances look genuinely stable?
- Which segments deserve closer attention?
- Where is runoff risk building?
- How sensitive is the portfolio likely to be going forward?
That is the level where forecasting becomes useful.
Why This Is Helpful for Banks
A better NMD forecasting process helps a bank move from a largely assumption-based approach toward something that is more:
- structured
- explainable
- scalable
- forward-looking
- useful for management discussion
That does not mean judgment disappears.
Judgment will always remain important.
But judgment becomes much stronger when it is supported by clearer analytics.
And that is really the point.
The best treasury decisions are not made from assumptions alone. They are made from assumptions supported by evidence.
Final Thought
Non-Maturity Deposits are often described as stable funding.
And many of them are.
But stability should not simply be assumed.
It should be assessed, challenged, and monitored with a process that is capable of looking ahead.
That is where stronger forecasting and better analytical tools can make a real difference.
Because in treasury and ALM, confidence does not come from having a number.
It comes from being able to explain why that number makes sense.
Explore Further
If you would like to see how NMDHub supports a more structured and forward-looking approach to Non-Maturity Deposit forecasting, you are welcome to explore further or request a demo.
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