Can banks improve financial performance through scenario-based planning?

The benefits are greatest when scenario analysis is integrated into ongoing Asset and Liability Management (ALM) and Treasury processes rather than treated solely as a regulatory exercise. Banks can use scenarios to understand how changes in interest rates, funding conditions, customer behaviour and balance sheet development could affect key measures such as net interest income (NII), economic value of equity (EVE) and liquidity.

The questions below explain how scenario-based planning works in banking, how it differs from stress testing, and how banks can use scenario analysis to support ALCO and balance sheet decisions.

What types of scenarios do banks typically model in financial planning?

Banks typically use a combination of baseline, alternative and adverse scenarios, with the exact structure depending on the purpose of the analysis. Scenarios may examine changes in interest rates, funding and liquidity conditions, customer behaviour and the broader macroeconomic environment.

Common examples include:

• Interest rate scenarios: parallel shifts up or down, steepening or flattening yield curves and changes in the shape of the yield curve
• Funding and liquidity scenarios: deposit outflows, changes in deposit mix, higher funding costs or reduced access to particular funding sources
• Behavioural scenarios: different assumptions for non-maturing deposits, deposit repricing, deposit stability and mortgage prepayments
• Balance sheet scenarios: planned lending growth, changes in product mix, new funding or different balance sheet development assumptions
• Macroeconomic scenarios: alternative paths for economic growth, inflation and market interest rates that affect the assumptions used in ALM planning

Banks can combine these assumptions to understand how several changes might affect the balance sheet simultaneously.

The most useful scenario libraries are therefore not static. Assumptions need to be reviewed as interest rates, customer behaviour, funding markets and the bank’s own balance sheet change.

The goal is not to predict one version of the future with certainty. It is to understand how the bank could perform under different conditions and what management actions may be available in each case.

How does scenario-based planning actually improve financial performance?

Scenario-based planning can support financial performance by turning uncertainty into structured balance sheet analysis. Instead of optimising the bank around a single forecast, management can compare different strategies across multiple conditions and assess their potential effect on earnings, economic value and liquidity.

The benefits can materialise in several interconnected ways.

First, scenario analysis can reveal concentrations in repricing, funding or liquidity that may not be obvious in a baseline forecast. Identifying these exposures gives management an opportunity to consider changes to the balance sheet, funding structure or hedging strategy before conditions change.

Second, scenario analysis can support pricing and product decisions. By understanding how different interest rate and behavioural assumptions could affect NII across the loan and deposit portfolio, banks can assess the implications of different pricing strategies more systematically.

For example, the impact of higher market rates depends not only on asset repricing but also on how quickly deposit rates change, how customers respond to pricing decisions and how the composition of deposits develops.

Third, scenario-based planning supports strategic balance sheet decisions. Banks can assess planned lending growth, funding strategies, hedging decisions or changes to the product mix under different conditions rather than assuming that today’s market environment will continue.

This does not mean that scenario planning itself guarantees higher profitability. Its value comes from giving ALCO and management a clearer analytical basis for deciding which actions to take and understanding the potential consequences before those decisions are implemented.

What’s the difference between scenario planning and stress testing in banking?

Scenario planning and stress testing both use forward-looking assumptions, but they are generally used for different purposes.

Scenario planning allows banks to explore how alternative assumptions could affect earnings, economic value, liquidity and the balance sheet and to compare possible management actions.

Stress testing focuses more specifically on the bank’s resilience under adverse conditions. It is used both as an internal risk management tool and as part of regulatory supervision.

Regulatory stress testing can involve prescribed scenarios, assumptions and methodologies. For example, the European Banking Authority’s EU-wide stress testing framework is designed to assess banks’ resilience to adverse market developments using common methodologies, scenarios and assumptions.

Scenario-based ALM analysis can be more flexible. Banks can test assumptions and management strategies that reflect their particular balance sheet, funding structure, behavioural characteristics and business plans.

The distinction is therefore not simply strategy versus compliance. Both can support risk management and decision-making. Scenario planning gives management the flexibility to ask a broader range of what-if questions, while stress testing puts greater emphasis on understanding resilience under adverse conditions.

Banks can also use insights from stress testing to inform their internal scenario analysis rather than treating the two processes as completely separate exercises.

How do interest rate scenarios affect a bank’s balance sheet?

Interest rate scenarios affect a bank primarily through their impact on earnings and economic value. Two key measures used in ALM and Interest Rate Risk in the Banking Book (IRRBB) are net interest income (NII) and economic value of equity (EVE).

This distinction is also reflected in European IRRBB requirements. European Banking Authority supervisory outlier tests address prescribed interest rate shock scenarios and their impact on both EVE and NII.

The NII channel

NII sensitivity examines how changes in market rates could affect interest income and interest expense over a defined time horizon.

The outcome depends on how quickly assets and liabilities reprice and how customer behaviour changes as rates move. A bank with a high proportion of variable-rate assets and slower-repricing liabilities may, for example, experience an increase in NII when rates rise.

However, the actual outcome depends on factors including deposit pricing, deposit betas, repricing assumptions, customer behaviour, new business and hedging.

Running multiple scenarios helps banks understand how these assumptions interact and where their earnings may be most exposed to changes in interest rates.

The EVE channel

EVE takes a longer-term economic value perspective by assessing how changes in interest rates affect the present value of future balance sheet cash flows.

Differences in the timing and interest rate sensitivity of asset and liability cash flows can therefore create significant EVE sensitivity.

Looking at NII and EVE together can provide a more complete view of interest rate risk than analysing either measure in isolation. European IRRBB reporting similarly incorporates EVE and NII measures, repricing cash flows, behavioural modelling and scenario assumptions.

Why are behavioural assumptions important in ALM scenario planning?

Behavioural assumptions are important because the contractual characteristics of a product do not always describe how customers will behave when market conditions change.

Non-maturing deposits (NMDs) are a key example. Although customers may be able to withdraw these deposits at short notice, a proportion of balances may remain relatively stable over time. Banks therefore need assumptions about deposit stability, repricing behaviour and the effective maturity of these balances.

Mortgage prepayments provide another example. Changes in rates or customer behaviour may alter expected mortgage cash flows, which in turn can affect a bank’s interest rate risk profile.

Scenario analysis allows banks to test how alternative behavioural assumptions influence ALM results rather than relying on a single set of assumptions.

For example, a bank could compare what happens to NII, EVE and liquidity when customers move deposits more quickly than expected, deposit rates reprice differently, or mortgage prepayment rates change.

This makes behavioural modelling an important connection between customer behaviour, cash flow projections and balance sheet risk management.

What tools and data do banks need for effective scenario planning?

Effective scenario planning requires three elements: reliable balance sheet data, a modelling environment capable of applying different assumptions and scenarios, and outputs that can support management decisions.

On the data side, banks need sufficiently granular information about assets, liabilities, off-balance-sheet positions and their associated cash flows and repricing characteristics. The analysis may also require behavioural assumptions for products such as non-maturing deposits and mortgages.

The modelling environment needs to allow banks to change assumptions and understand how those changes flow through the balance sheet.

Depending on the purpose of the analysis, this can include:

• interest rate and yield curve assumptions
• balance sheet development
• deposit behaviour and repricing
• prepayment assumptions
• funding assumptions
• cash flow projections
• NII
• EVE
• liquidity

As the number of products, scenarios, behavioural assumptions and reporting dimensions increases, maintaining this analysis through multiple spreadsheets and manual processes can become increasingly difficult.

An integrated ALM environment can help banks apply consistent data, assumptions and calculations across scenarios while reducing manual data handling.

The results also need to reach ALCO and other decision-makers in a usable form. The objective is not simply to produce more scenario reports. It is to make the differences between scenarios, their balance sheet effects and the implications of possible management actions easier to understand.

How can MORS support scenario-based ALM planning?

MORS supports banks in bringing ALM calculations, cash flow projections, behavioural assumptions and scenario analysis into the same environment.

This allows banks to analyse how alternative assumptions about interest rates, customer behaviour, funding and balance sheet development could affect measures such as NII, EVE and liquidity.

Rather than analysing individual risks in isolation, banks can use a consistent view of the underlying balance sheet and projected cash flows to compare scenarios and understand the potential effects of different management decisions.

For example, a bank can examine how a change in the interest rate environment interacts with deposit repricing assumptions, expected new business and the structure of assets and liabilities. Alternative assumptions can then be compared to understand their effects on earnings and balance sheet risk.

This supports a more practical scenario-based ALM process in which analysis can feed directly into ALCO discussions and balance sheet planning.

For MORS, the value of scenario analysis is therefore not about predicting exactly what will happen. It is about giving banks the analytical tools to explore what could happen, how it could affect the balance sheet, and what actions management could consider in response.

When should banks run new scenarios versus updating existing ones?

Banks should consider creating new scenarios when the risk environment changes in a way that existing scenarios do not adequately capture. Existing scenarios can instead be updated when the underlying scenario remains relevant but assumptions or parameters need to be refreshed.

Triggers for creating new scenarios can include:

• significant changes in interest rate or funding conditions
• emerging risks not captured in the existing scenario library
• regulatory changes requiring different assumptions or methodologies
• significant changes to the bank’s balance sheet or business model
• new products or funding strategies with materially different behavioural or repricing characteristics

Triggers for updating existing scenarios can include:

• changes to interest rate expectations
• updated deposit repricing or behavioural assumptions
• changes in observed prepayment behaviour
• revised funding assumptions
• changes to planned balance sheet development
• regular ALM and planning-cycle reviews

Banks should also keep regulatory scenarios under review. For example, the Basel Committee on Banking Supervision recalibrated the prescribed interest rate shocks used in its IRRBB standard, with implementation from 1 January 2026.

Ultimately, a well-maintained scenario framework helps banks move beyond a single view of the future. By combining ALM scenario analysis, cash flow projections, behavioural assumptions, NII, EVE and liquidity analysis, banks can compare alternative balance sheet outcomes and give ALCO a stronger basis for decision-making.

For banks looking to develop this approach, MORS provides ALM scenario planning within an integrated environment for analysing the balance sheet under different assumptions.