Banks do not simply collect deposits and provide loans.
They continuously manage a complex balance sheet containing assets and liabilities with different maturities, interest rates, cash-flow characteristics and behavioural patterns.
A bank may provide long-term fixed-rate loans while depending on deposits that can reprice or leave much earlier.
Market interest rates can change.
Depositors can withdraw funds.
Borrowers can prepay loans.
Funding costs can rise.
Liquidity conditions can deteriorate.
All of these factors create risks that need to be measured and managed.
This is where Asset Liability Management, commonly known as ALM, becomes important.
A practical ALM course should teach learners how banks manage liquidity, funding and interest-rate risk across the balance sheet. It should move beyond theoretical definitions into actual calculations involving repricing gaps, liquidity statements, LCR, NSFR, NII, EVE, IRRBB, stress testing, behavioural assumptions and Fund Transfer Pricing.
For banking professionals, treasury teams, risk analysts and finance learners, ALM is one of the most important areas connecting financial risk management with actual balance-sheet decision-making.
This guide explains what an ALM course should cover, how Asset Liability Management works and which practical skills are required for careers in banking, treasury and risk.
What Is Asset Liability Management?
Asset Liability Management is the process through which financial institutions manage risks created by differences between their assets and liabilities.
A bank's assets may include:
- Loans
- Mortgages
- Bonds
- Investments
- Cash
Its liabilities may include:
- Savings accounts
- Current accounts
- Term deposits
- Wholesale funding
- Borrowings
The problem is that these assets and liabilities do not behave identically.
They may have different:
- Maturities
- Interest rates
- Repricing dates
- Cash-flow structures
- Liquidity characteristics
ALM attempts to understand and manage these mismatches.
The primary risks commonly associated with ALM include:
- Liquidity risk
- Funding risk
- Interest-rate risk
- Repricing risk
- Basis risk
- Optionality risk
Asset Liability Management therefore sits at the intersection of banking, treasury and risk management.
Why Is ALM Important for Banks?
Consider a simple banking example.
A bank provides a five-year fixed-rate loan.
It funds that loan partly using short-term deposits.
Now suppose market interest rates rise sharply.
The bank may need to increase deposit rates to retain customers.
Its funding costs rise.
But the interest income from the fixed-rate loan remains unchanged.
The result?
The bank's Net Interest Income may decline.
This demonstrates a basic ALM problem.
The asset and liability respond differently to interest-rate movements.
A bank may also face liquidity problems if depositors withdraw funds faster than assets can generate cash.
ALM helps institutions identify these exposures before they become severe.
What Should an ALM Course Teach?
A strong ALM course should move progressively from banking fundamentals into quantitative balance-sheet modelling.
Important areas include:
- Balance-sheet structure
- Liquidity risk
- Funding risk
- Maturity gaps
- Repricing gaps
- Structural Liquidity Statements
- LCR
- NSFR
- Interest-rate risk
- Duration
- IRRBB
- NII
- EVE
- Behavioural modelling
- Stress testing
- Fund Transfer Pricing
- ICAAP
- ILAAP
Peaks2Tails' published ALM curriculum currently includes Structural Liquidity Statements, LCR, NSFR, NII/EVE calculations, ICAAP, ILAAP and Fund Transfer Pricing using Excel.
This type of structure matters because professional ALM involves actual balance-sheet calculations rather than definitions alone.
Understanding the Bank Balance Sheet
Before learning ALM models, learners need to understand how a bank balance sheet works.
Unlike a normal manufacturing company, financial institutions earn significant income from the relationship between:
- Assets
- Liabilities
- Interest rates
- Funding costs
Assets may generate interest income.
Liabilities create interest expenses.
The difference contributes to Net Interest Income.
But profitability alone is not enough.
The bank must also maintain sufficient liquidity and control the risk created by mismatched maturities.
This makes balance-sheet structure fundamental to ALM.
Liquidity Risk in ALM
Liquidity risk is the risk that a financial institution cannot meet its obligations when they become due without suffering unacceptable losses.
A bank may need cash for:
- Customer withdrawals
- Loan commitments
- Debt repayment
- Margin requirements
- Operational payments
The challenge is that many banking assets are not instantly liquid.
A long-term loan cannot always be converted into cash immediately.
A bank therefore needs to ensure that enough liquidity is available even during stressful conditions.
Funding Liquidity Risk
Funding liquidity risk focuses on the ability of a bank to obtain funding when needed.
Funding can come from:
- Retail deposits
- Corporate deposits
- Wholesale markets
- Interbank borrowing
- Debt issuance
A bank heavily dependent on one funding source may be vulnerable if that source becomes unavailable.
ALM professionals therefore monitor:
- Funding concentration
- Funding maturity
- Deposit behaviour
- Market access
Diversification of funding can become an important part of balance-sheet management.
Structural Liquidity Statement
A Structural Liquidity Statement, or SLS, organises expected cash inflows and outflows across maturity buckets.
Typical buckets might include:
- Next day
- 2–7 days
- 8–14 days
- 15–30 days
- 1–3 months
- Longer periods
Assets and liabilities are allocated according to expected maturity or behavioural assumptions.
The analyst can then identify:
- Positive liquidity gaps
- Negative liquidity gaps
- Cumulative mismatches
Peaks2Tails' current ALM curriculum explicitly includes preparation of Structural Liquidity Statements in Excel.
This is a useful practical exercise because learners see how balance-sheet cash flows are transformed into liquidity-risk information.
Liquidity Gap Analysis
Liquidity gap analysis compares expected cash inflows with expected cash outflows.
A simplified relationship is:
Liquidity Gap = Cash Inflows − Cash Outflows
A negative gap indicates that expected outflows exceed expected inflows during that period.
This does not automatically mean the bank has a liquidity crisis.
The institution may have:
- Liquid assets
- Market borrowing capacity
- Central-bank facilities
But persistent or large negative gaps require attention.
Liquidity Coverage Ratio
The Liquidity Coverage Ratio, commonly called LCR, is a major liquidity-risk measure.
At a high level, LCR compares the stock of High Quality Liquid Assets with expected net cash outflows during a short-term stress horizon.
Conceptually:
LCR = High Quality Liquid Assets / Net Cash Outflows
The ratio is designed to assess whether sufficient liquidity is available to survive a severe short-term stress scenario.
A practical ALM course should teach learners how LCR components are actually calculated rather than asking them only to memorise the definition.
Peaks2Tails currently includes LCR computation as part of its ALM modelling curriculum.
High Quality Liquid Assets
High Quality Liquid Assets, or HQLA, are assets expected to remain relatively liquid during periods of financial stress.
ALM learners should understand:
- Asset eligibility
- Liquidity characteristics
- Haircuts
- Regulatory treatment
Holding liquidity provides protection.
But highly liquid assets may generate lower returns than less-liquid lending activities.
ALM therefore often involves balancing:
Liquidity + Profitability + Risk.
Net Stable Funding Ratio
The Net Stable Funding Ratio, or NSFR, focuses on longer-term funding stability.
Conceptually, it compares:
Available Stable Funding
with
Required Stable Funding
The objective is to reduce excessive dependence on unstable short-term funding for long-term assets.
LCR and NSFR therefore address different liquidity horizons.
LCR focuses more heavily on short-term stress liquidity.
NSFR examines structural funding resilience over a longer horizon.
A complete ALM course should teach both.
Interest Rate Risk in ALM
Liquidity is only one side of Asset Liability Management.
Interest-rate risk is equally important.
Banks hold assets and liabilities that react differently to interest-rate changes.
Examples include:
- Fixed-rate loans
- Floating-rate loans
- Savings deposits
- Fixed deposits
- Bonds
- Borrowings
When rates move, the bank's:
- Interest income
- Interest expense
- Asset values
- Liability values
can all change.
This creates Interest Rate Risk in the Banking Book, commonly called IRRBB.
What Is IRRBB?
IRRBB stands for Interest Rate Risk in the Banking Book.
It measures risk arising when interest-rate movements affect banking-book assets, liabilities and relevant off-balance-sheet positions.
Peaks2Tails' current IRRBB training material describes IRRBB as affecting both bank earnings and economic value and positions it as a core ALM and treasury-risk discipline.
A proper ALM course should therefore include IRRBB rather than treating it as a separate topic.
Repricing Risk
Repricing risk occurs when assets and liabilities reset their interest rates at different times.
Consider:
Assets repricing after three years.
Liabilities repricing after three months.
If interest rates rise:
Funding costs may rise quickly.
Asset income may remain unchanged for much longer.
The bank's interest margin can shrink.
This is a classic ALM mismatch.
Repricing Gap Analysis
Repricing gap analysis groups interest-sensitive assets and liabilities according to when their interest rates reset.
A simplified calculation is:
Repricing Gap = Rate-Sensitive Assets − Rate-Sensitive Liabilities
Positive and negative gaps create different sensitivities to interest-rate changes.
However, gap analysis is only an introductory measure.
Modern ALM requires additional analysis because:
- Cash flows are distributed across time
- Yield curves do not move uniformly
- Customer behaviour is uncertain
- Products contain embedded options
This leads to more advanced metrics.
Net Interest Income
Net Interest Income, commonly abbreviated as NII, is broadly the difference between interest income and interest expense.
Conceptually:
NII = Interest Income − Interest Expense
Interest-rate movements affect NII because assets and liabilities reprice differently.
An ALM analyst may simulate NII under scenarios such as:
- Rates increase
- Rates decrease
- Yield curve steepens
- Yield curve flattens
The objective is to understand how future earnings may react.
Peaks2Tails' current ALM and IRRBB curricula include practical NII computation.
Earnings at Risk
Earnings at Risk evaluates how interest-rate movements could affect future earnings.
Instead of focusing on economic value, it focuses more directly on the income impact over a defined horizon.
NII simulation is commonly used as part of earnings-based analysis.
This perspective is particularly important for management because significant NII deterioration can directly affect bank profitability.
Economic Value of Equity
Economic Value of Equity, or EVE, examines the longer-term economic-value effect of interest-rate changes.
Conceptually, EVE considers the present value of:
Assets − Liabilities.
Interest-rate shocks change the discounted value of future cash flows.
Therefore, a bank may experience changes in economic value even when near-term earnings appear manageable.
Peaks2Tails' current ALM curriculum includes EVE computation in Excel, while its IRRBB training also covers yield-curve scenarios, EVE and NII analysis.
NII vs EVE
NII and EVE provide different perspectives.
NII focuses more heavily on:
earnings sensitivity.
EVE focuses more heavily on:
economic-value sensitivity.
An institution can look relatively comfortable under one measure while appearing more exposed under the other.
That is why modern IRRBB frameworks frequently examine both perspectives.
Duration
Duration measures sensitivity to interest-rate movements.
For fixed-income instruments, duration helps estimate how much value may change when interest rates change.
ALM professionals may study:
- Macaulay duration
- Modified duration
Duration provides useful intuition.
But it is an approximation.
Its limitations become more important for larger rate movements or instruments containing optionality.
Convexity
Convexity improves the approximation of how bond values respond to interest-rate changes.
While duration assumes a largely linear relationship, the true price-yield relationship is curved.
Convexity helps capture some of that curvature.
Advanced ALM and IRRBB training should therefore move beyond gap analysis into:
- Duration
- Convexity
- Full revaluation
depending on the application.
Yield Curve Risk
Interest rates across maturities do not always move together.
The yield curve can:
- Shift upward
- Shift downward
- Steepen
- Flatten
- Change shape
This creates yield-curve risk.
A simple parallel-rate shock cannot represent every possible interest-rate scenario.
Advanced ALM analysis therefore needs to consider multiple yield-curve movements.
Basis Risk
Basis risk occurs when different interest rates do not move together perfectly.
For example:
A loan may be linked to one benchmark.
Funding may be linked to another.
Even if both rates generally move in the same direction, the spread between them can change.
That difference can affect profitability.
Basis risk is therefore an important component of IRRBB.
Embedded Optionality
Many banking products contain customer options.
Borrowers may:
- Prepay loans
Depositors may:
- Withdraw early
These behaviours can change the expected cash-flow profile.
For example, mortgage borrowers may prepay more aggressively when rates fall because refinancing becomes attractive.
That means contractual maturity and expected behavioural maturity may be very different.
Advanced ALM training should account for this.
Behavioural Modelling in ALM
Behavioural modelling is increasingly important because customer products do not always behave according to contractual terms.
Important areas include:
- Non-maturity deposits
- Deposit beta
- Deposit decay
- Loan prepayment
- Early withdrawal
Peaks2Tails' current IRRBB training explicitly includes behavioural modelling, non-maturity deposits, deposit beta, deposit decay and loan prepayments.
Any advanced ALM course teaching only gap analysis and duration is therefore incomplete.
Non-Maturity Deposits
Non-Maturity Deposits, or NMDs, include products such as:
- Current accounts
- Savings accounts
Contractually, customers may withdraw funds quickly.
Behaviourally, however, part of these balances can remain relatively stable for long periods.
ALM teams therefore need models to estimate:
- Stable balances
- Deposit decay
- Effective maturity
- Rate sensitivity
These assumptions can materially affect both liquidity and IRRBB calculations.
Deposit Beta
Deposit beta measures how much deposit rates respond when market rates change.
For example:
Market rates rise by 2%.
Deposit rates increase by only 1%.
This suggests a beta of approximately 50% in simplified terms.
Deposit pricing behaviour can materially influence NII.
Therefore, deposit beta modelling is highly relevant for ALM teams.
Loan Prepayment Modelling
Borrowers may repay loans earlier than contractually expected.
Prepayments affect:
- Cash flows
- Interest income
- Duration
- EVE
- NII
Prepayment behaviour may change depending on:
- Interest rates
- Borrower characteristics
- Loan age
- Economic conditions
This makes loan prepayment modelling an important advanced ALM skill.
Liquidity Stress Testing
Normal conditions are not enough.
Banks must understand what might happen under stress.
Possible scenarios include:
- Rapid deposit withdrawals
- Loss of wholesale funding
- Reduced market liquidity
- Credit-line drawdowns
- Collateral calls
Stress tests estimate how long available liquidity may support the institution under adverse conditions.
A practical ALM course should therefore teach both normal-gap analysis and stress scenarios.
Interest Rate Stress Testing
ALM teams also test changes in interest rates.
Possible scenarios include:
- Parallel upward shock
- Parallel downward shock
- Steepening curve
- Flattening curve
- Short-rate shock
- Long-rate shock
The analyst then evaluates effects on:
- NII
- EVE
- Product behaviour
- Hedging
Peaks2Tails' IRRBB material specifically includes regulatory and internal interest-rate scenarios within practical ALM implementation.
Fund Transfer Pricing
Fund Transfer Pricing, commonly abbreviated as FTP, is an important ALM concept.
FTP creates an internal pricing mechanism for transferring funding and liquidity costs between different parts of the bank.
For example:
A lending business uses funding.
A deposit business provides funding.
FTP helps assign the economic value of those funding contributions appropriately.
This can support:
- Product pricing
- Performance measurement
- Balance-sheet incentives
- Funding-cost allocation
Peaks2Tails' published ALM curriculum includes Fund Transfer Pricing concepts with Excel implementation.
ALCO and Asset Liability Management
ALCO stands for Asset Liability Committee.
ALCO typically oversees major balance-sheet risks and decisions.
Its responsibilities can include:
- Liquidity
- Funding
- Interest-rate risk
- Balance-sheet structure
- Risk limits
- Pricing
ALM teams often provide ALCO with analysis on:
- Repricing gaps
- NII
- EVE
- Liquidity ratios
- Stress testing
- Funding concentration
Technical modelling becomes valuable only when it supports management decisions.
ALM and ICAAP
ICAAP stands for Internal Capital Adequacy Assessment Process.
It considers whether an institution has sufficient capital relative to its risk profile.
ALM-related risks can connect with ICAAP through areas such as:
- IRRBB
- Concentration
- Stress testing
- Capital planning
Peaks2Tails currently positions ALM together with ICAAP within its wider treasury-risk curriculum.
This integration is useful because balance-sheet risks do not exist independently from capital planning.
ALM and ILAAP
ILAAP stands for Internal Liquidity Adequacy Assessment Process.
It focuses on liquidity adequacy.
Relevant areas can include:
- Liquidity-risk governance
- Funding risk
- Stress testing
- Liquidity buffers
- Contingency funding
Peaks2Tails' current ILAAP training explicitly connects ILAAP with ALM, LCR, NSFR, behavioural deposit modelling and funding concentration.
This makes ILAAP a natural extension of advanced ALM learning.
ALM and Basel
Banking ALM is also influenced by regulatory frameworks.
Learners may encounter:
- LCR
- NSFR
- Leverage ratio
- IRRBB
- Capital frameworks
But regulatory calculations should not be treated as isolated formulas.
The objective is to understand what risk each measure is trying to capture.
This makes the training much more practical.
Excel for ALM
Excel remains highly useful in Asset Liability Management.
It can be used for:
- Structural Liquidity Statements
- LCR
- NSFR
- Repricing gaps
- NII
- EVE
- Stress testing
- FTP
Peaks2Tails' published ALM curriculum specifically uses Excel across Structural Liquidity Statements, LCR/NSFR, NII/EVE, ICAAP, ILAAP and FTP.
Excel is particularly useful for learners because calculations remain transparent.
They can follow each cash flow and assumption.
Python for ALM
As balance sheets and datasets become larger, Python becomes useful.
Python can support:
- Cash-flow engines
- Yield-curve construction
- NII simulation
- EVE simulation
- Behavioural models
- Scenario generation
- Model monitoring
Useful libraries can include:
- Pandas
- NumPy
- SciPy
- Statsmodels
- Scikit-learn
Peaks2Tails' current IRRBB training also includes Python implementation alongside Excel.
The goal should not be to use Python simply because it appears more sophisticated.
Use it when it improves scalability, consistency and reproducibility.
Excel vs Python for ALM
For many learners, the strongest approach is:
Understand in Excel → Scale in Python.
Excel allows you to inspect:
- Cash flows
- Assumptions
- Calculations
Python allows you to handle:
- Larger datasets
- More scenarios
- Repeated simulations
- Statistical modelling
Both have value.
Practical ALM Projects
An effective ALM course should require learners to build models.
Useful projects include:
Structural Liquidity Statement
Create maturity buckets and calculate liquidity gaps.
LCR Model
Calculate High Quality Liquid Assets and stressed net cash outflows.
NSFR Model
Estimate available and required stable funding.
Repricing Gap Model
Map assets and liabilities into interest-rate buckets.
NII Simulation
Estimate earnings under changing interest-rate scenarios.
EVE Model
Calculate economic-value sensitivity.
Deposit Behaviour Model
Estimate deposit beta or effective maturity.
Loan Prepayment Model
Analyse how prepayments alter cash flows.
FTP Model
Develop a simplified internal transfer-pricing framework.
Practical projects are what turn ALM theory into professional capability.
ALM Course for Treasury Professionals
Treasury professionals may particularly benefit from ALM training covering:
- Funding
- Liquidity
- Yield curves
- NII
- EVE
- Interest-rate hedging
- FTP
Treasury decisions directly influence the balance sheet.
A strong understanding of ALM helps professionals connect individual transactions with overall institutional risk.
ALM Course for Risk Professionals
Risk professionals may focus more heavily on:
- Risk appetite
- Risk limits
- Independent measurement
- Stress testing
- IRRBB
- Liquidity risk
- Model validation
The objective is not only to calculate risk.
It is also to challenge whether the assumptions and methodologies are appropriate.
ALM Course for Banking Professionals
ALM knowledge can be useful across several banking functions.
These include:
- Treasury
- Risk
- Finance
- ALCO
- Internal audit
- Model validation
- Regulatory reporting
Senior banking professionals also benefit from understanding ALM because balance-sheet decisions affect:
- Profitability
- Liquidity
- Capital
- Risk
Career Opportunities Related to ALM
ALM knowledge can be relevant to roles such as:
- ALM Analyst
- Treasury Analyst
- Liquidity Risk Analyst
- IRRBB Analyst
- Banking Risk Analyst
- Treasury Risk Analyst
- Balance Sheet Risk Analyst
- Model Validation Analyst
- Risk Consultant
Actual job requirements vary significantly between organisations.
A course alone does not qualify someone automatically for these roles.
Learners also need:
- Banking knowledge
- Financial mathematics
- Excel
- Analytical ability
- Communication
More quantitative positions may additionally require Python and statistics.
ALM Course at Peaks2Tails
Peaks2Tails currently has dedicated Asset Liability Management curriculum material covering:
- Structural Liquidity Statements
- LCR
- NSFR
- IRRBB NII and EVE
- ICAAP
- ILAAP
- Fund Transfer Pricing
The exercises are structured around Excel implementation.
Peaks2Tails' newer IRRBB training also expands the ALM framework into:
- Yield curves
- Duration
- Convexity
- Behavioural cash flows
- Non-maturity deposits
- Deposit beta
- Loan prepayments
- Stress testing
- Hedging
- Model validation
- Python implementation
Its broader Integrated Treasury Risk Modelling pathway positions ALM and IRRBB together with ICAAP and ILAAP.
This matters because modern Asset Liability Management is not simply a maturity-gap exercise.
It is an integrated balance-sheet-risk discipline.
Step-by-Step ALM Learning Roadmap
A sensible learning path begins with banking fundamentals.
Understand the balance sheet.
Then learn:
Liquidity risk
Understand cash inflows, outflows and maturity mismatches.
SLS
Build structural liquidity statements.
LCR and NSFR
Understand short-term liquidity and structural funding.
Interest-rate risk
Learn repricing gaps, duration and yield curves.
IRRBB
Build NII and EVE analysis.
Behavioural modelling
Study deposits and prepayments.
Stress testing
Analyse adverse liquidity and interest-rate scenarios.
FTP
Understand internal funding economics.
ICAAP and ILAAP
Connect ALM with broader capital and liquidity frameworks.
Then progress from Excel into Python where scalability is required.
How to Choose an ALM Course
A serious ALM course should not stop at definitions.
Look for practical coverage of:
- Bank balance sheets
- Liquidity risk
- Gap analysis
- SLS
- LCR
- NSFR
- Interest-rate risk
- NII
- EVE
- IRRBB
- Stress testing
- Behavioural modelling
- FTP
For advanced learners, additional coverage of:
- ICAAP
- ILAAP
- Deposit modelling
- Prepayment
- Python
can make the programme substantially more useful.
The strongest question is:
Will you actually build ALM models?
Common Mistakes While Learning ALM
One common mistake is treating ALM as a list of regulatory ratios.
Another is focusing only on liquidity while ignoring interest-rate risk.
Learners also often rely entirely on contractual maturities while ignoring customer behaviour.
Another mistake is calculating NII or EVE without understanding the assumptions driving the result.
ALM is not simply calculation.
It requires understanding how the balance sheet behaves.
Is ALM Difficult?
ALM can become technically challenging because it combines:
- Banking
- Liquidity
- Fixed income
- Interest rates
- Financial mathematics
- Regulation
- Behavioural modelling
But beginners do not need to learn everything simultaneously.
Start with the bank balance sheet.
Understand cash flows.
Then add liquidity.
Then interest rates.
Then behavioural modelling.
A structured progression makes advanced ALM much easier to understand.
Frequently Asked Questions About ALM Courses
What is an ALM course?
An ALM course teaches Asset Liability Management for banks and financial institutions, including liquidity risk, funding, interest-rate risk and balance-sheet management.
What does ALM stand for?
ALM stands for Asset Liability Management.
Is ALM part of treasury?
ALM commonly works closely with treasury because both areas deal with funding, liquidity, interest rates and balance-sheet risk.
What are LCR and NSFR?
LCR measures short-term liquidity resilience, while NSFR focuses on longer-term structural funding stability.
What is IRRBB?
IRRBB means Interest Rate Risk in the Banking Book. It measures the effect of interest-rate movements on banking-book earnings and economic value.
What are NII and EVE?
NII measures the earnings impact of interest-rate changes, while EVE examines the impact on economic value.
Is Excel useful for ALM?
Yes. Excel is widely useful for gap analysis, liquidity statements, LCR, NSFR, NII, EVE and stress testing.
Is Python required for ALM?
Not for every ALM role, but Python can be useful for larger datasets, simulation, behavioural modelling and automated risk calculations.
Conclusion: An ALM Course Should Teach You How the Bank Balance Sheet Actually Behaves
A practical ALM course should do much more than explain what Asset Liability Management stands for.
Learners should understand how banks manage the interaction between:
- Assets
- Liabilities
- Liquidity
- Funding
- Interest rates
- Customer behaviour
That means learning how to build and interpret:
- Structural Liquidity Statements
- Liquidity gaps
- LCR
- NSFR
- Repricing gaps
- NII
- EVE
- IRRBB stress scenarios
- Behavioural deposit models
- Prepayment assumptions
- Fund Transfer Pricing frameworks
The learning progression matters.
A beginner may start with a simple maturity-gap statement.
The next step is calculating liquidity ratios.
Then comes repricing analysis.
Then NII and EVE.
Then behavioural modelling.
Then integrated stress testing.
Eventually, a professional should be able to answer much more important questions:
Where does the bank's liquidity risk come from?
How quickly could funding disappear?
What happens to earnings when interest rates change?
What happens to economic value?
How do customer behaviours affect the model?
Which balance-sheet actions can reduce the risk?
That is where Asset Liability Management becomes useful.
Peaks2Tails currently combines ALM, LCR, NSFR, IRRBB, NII/EVE, ICAAP, ILAAP and FTP within its treasury-risk learning framework, with practical Excel modelling and more advanced Python-based implementation available through its broader IRRBB training.
For learners searching for an ALM course, the objective should therefore not be to memorise banking ratios.
It should be to understand how liquidity, funding, interest rates and customer behaviour interact across the entire banking balance sheet—and how those risks can be measured and managed in practice.