IRRBB Training: Build Practical EVE, NII, Behavioural Modelling and Interest Rate Risk Management Skills

Interest-rate movements can materially affect a bank even when the underlying positions are not held for trading.

A change in the yield curve can alter:

• Net interest income
• Deposit margins
• Loan income
• Funding costs
• Economic value of assets
• Economic value of liabilities
• Prepayment behaviour
• Deposit behaviour
• Hedge effectiveness
• Capital planning
• Balance-sheet strategy

This exposure is known as Interest Rate Risk in the Banking Book, or IRRBB.

The Basel Framework defines IRRBB as the current or prospective risk to a bank’s capital and earnings arising from adverse movements in interest rates affecting banking-book positions. Changes in interest rates affect both the present value and timing of future cash flows, thereby affecting economic value as well as earnings. IRRBB remains primarily a Pillar 2 risk because of its heterogeneous nature across banks and business models.

That makes IRRBB much more than a regulatory calculation.

It is a core Asset Liability Management and treasury-risk discipline.

A strong IRRBB training programme should therefore teach participants not only what IRRBB means, but how to:

• Build repricing cash flows
• Construct yield curves
• Calculate Economic Value of Equity
• Model Net Interest Income
• Apply regulatory interest-rate shocks
• Model non-maturity deposits
• Model loan prepayments
• Assess basis risk
• Evaluate optionality
• Design stress scenarios
• Analyse hedging strategies
• Validate behavioural models
• Connect IRRBB with ICAAP and ALM
• Implement models in Excel and Python

In 2024, the Basel Committee recalibrated the prescribed IRRBB interest-rate shocks and revised the calibration methodology. Those changes became effective on 1 January 2026, making updated shock calibration and scenario implementation especially relevant for current IRRBB training.

What Is IRRBB?

IRRBB stands for Interest Rate Risk in the Banking Book.

It arises when changes in interest rates affect the value or earnings associated with assets, liabilities and off-balance-sheet positions that are held in the banking book rather than the trading book.

Examples include:

• Fixed-rate mortgages
• Floating-rate corporate loans
• Retail term deposits
• Current accounts
• Savings accounts
• Bonds held in the banking book
• Borrowings
• Interest-rate swaps used for hedging
• Loan commitments
• Embedded options

The risk occurs because these products do not all reprice at the same time or respond identically to changes in market rates.

For example:

A bank may have long-term fixed-rate loans funded through short-term deposits.

If market rates rise rapidly:

• Deposit costs may reprice upward quickly.
• Loan income may remain fixed.
• Net interest margin may decline.
• The economic value of the fixed-rate loans may fall.

The bank is therefore exposed through both earnings and economic value.

What Is IRRBB Training?

IRRBB training is specialised professional training designed to help banking, treasury, ALM and risk professionals identify, measure, manage, stress test and govern interest-rate risk within the banking book.

A practical IRRBB training course may cover:

• IRRBB fundamentals
• Yield curves
• Repricing gaps
• Duration
• Modified duration
• Convexity
• Economic Value of Equity
• Net Interest Income
• Earnings at Risk
• Non-maturity deposits
• Deposit beta
• Deposit decay
• Loan prepayment
• Embedded optionality
• Basis risk
• Yield-curve risk
• Gap risk
• Option risk
• Basel IRRBB scenarios
• Supervisory outlier tests
• Hedging
• Interest-rate derivatives
• Model validation
• ICAAP integration
• ALM implementation
• Excel modelling
• Python modelling

Training should move from basic concepts into actual balance-sheet implementation.

Why IRRBB Training Is Important

IRRBB is technically challenging because many banking-book products have uncertain cash-flow behaviour.

Consider three common products:

Fixed-Rate Loan

The contractual interest rate may remain unchanged even when market rates move.

Floating-Rate Loan

The loan may reprice periodically, but its benchmark, spread and repricing frequency may differ from the liability funding it.

Savings Deposit

There may be no contractual maturity, and the deposit rate may not move one-for-one with market rates.

These differences create:

• Repricing risk
• Basis risk
• Yield-curve risk
• Option risk

The bank therefore cannot understand IRRBB simply by looking at contractual maturity dates.

Behaviour matters.

The Four Major Sources of IRRBB

1. Repricing Risk

Repricing risk arises when assets and liabilities reprice at different times.

Suppose a bank has:

• A five-year fixed-rate loan
• Funded by three-month deposits

If market rates rise, deposit costs may increase every three months while loan income stays unchanged.

The bank’s NII may decline.

Repricing risk is one of the most fundamental concepts in IRRBB training.

2. Yield-Curve Risk

Interest rates at different maturities do not necessarily move by the same amount.

The yield curve may:

• Shift upward
• Shift downward
• Steepen
• Flatten
• Invert

A bank may therefore have limited exposure to a parallel rate movement but significant exposure to a change in the shape of the yield curve.

This is why advanced IRRBB modelling should use multiple scenarios rather than a single parallel shock.

3. Basis Risk

Basis risk occurs when different interest-rate benchmarks or product rates do not move together.

For example:

A floating-rate loan may reference one benchmark while its funding cost responds to another.

Even if both rates increase, they may increase by different amounts.

Basis risk can arise between:

• Loan rates and deposit rates
• Different benchmark rates
• Secured and unsecured rates
• Prime lending rates and market rates
• Internal transfer-pricing curves and external funding curves

4. Option Risk

Banking products frequently contain embedded options.

Examples include:

• Mortgage prepayment
• Loan refinancing
• Early deposit withdrawal
• Deposit rollover
• Interest-rate caps
• Interest-rate floors
• Callable securities

Customers exercise these options in response to interest rates.

This creates non-linear cash flows.

For example, when rates fall, borrowers may refinance fixed-rate loans earlier.

The bank may then lose high-yielding assets exactly when reinvestment opportunities offer lower yields.

Economic Value and Earnings Perspectives

A credible IRRBB framework should consider both:

• Economic-value impact
• Earnings impact

These perspectives answer different questions.

Economic Value of Equity

Economic Value of Equity (EVE) measures the change in the present value of assets, liabilities and relevant off-balance-sheet positions when interest rates change.

Conceptually:

EVE = Present Value of Assets − Present Value of Liabilities

The bank then evaluates:

ΔEVE = EVE under shocked rates − EVE under base rates

A negative ΔEVE indicates a reduction in economic value under the relevant scenario.

The economic-value perspective captures the longer-term impact of interest-rate movements over the remaining life of banking-book positions.

Why EVE Matters

Consider a bank holding a large portfolio of long-term fixed-rate mortgages.

If market rates rise sharply:

• Existing fixed-rate mortgages become less valuable economically.
• Funding costs may eventually increase.
• The present value of the bank’s asset cash flows declines.

Even when accounting rules do not immediately recognise the full valuation change in earnings, the underlying economic exposure still exists.

EVE helps management understand that longer-term effect.

Net Interest Income

Net Interest Income (NII) focuses on earnings rather than present economic value.

Conceptually:

NII = Interest Income − Interest Expense

IRRBB analysis examines how NII changes when interest rates move.

For example:

Base NII = ₹1,000 crore

Stressed NII = ₹820 crore

Change in NII = −₹180 crore

This reduction provides an earnings-based measure of interest-rate sensitivity.

EVE vs NII

EVE and NII should not be treated as competing measures.

They provide complementary views.

EVE

Focuses on:

• Present value
• Longer-term economic impact
• Full remaining cash-flow profile
• Capital sensitivity

NII

Focuses on:

• Earnings
• Repricing
• Shorter planning horizons
• Profitability sensitivity

A bank can show moderate EVE risk and significant NII risk—or the reverse.

Training should therefore teach participants how to interpret both.

Basel IRRBB Shock Scenarios

The Basel standard applies a defined set of interest-rate scenarios for IRRBB measurement.

The standardised framework includes six scenarios:

1. Parallel shock up
2. Parallel shock down
3. Steepener shock
4. Flattener shock
5. Short rates shock up
6. Short rates shock down

These scenarios are designed to capture different yield-curve movements rather than assuming rates always move in parallel.

A practical IRRBB training programme should not only list these scenarios.

Participants should understand how each scenario changes rates across different maturity points.

Why the Basel IRRBB Shocks Changed

The Basel Committee recalibrated the IRRBB shocks in July 2024.

The revised methodology:

• Extended the calibration data through December 2023
• Replaced global shock factors with local currency-specific shock factors
• Moved from a 99th percentile calibration to a 99.9th percentile calibration
• Reduced shock rounding increments from 50 basis points to 25 basis points

The revised standard became effective on 1 January 2026.

Current IRRBB training should therefore use the applicable updated framework rather than relying exclusively on older shock calibrations.

Supervisory Outlier Test

The Basel IRRBB framework includes supervisory outlier tests intended to identify institutions with unusually high interest-rate risk.

For the EVE-based test, the Basel standard specifies a threshold that should be at least as stringent as 15% of Tier 1 capital for determining an outlier bank.

Participants should understand that an outlier test is not equivalent to a complete risk-management framework.

A bank can remain below an outlier threshold and still carry material interest-rate risk.

Internal limits should generally be more granular than one regulatory threshold.

Repricing Gap Analysis

Gap analysis is one of the simplest IRRBB tools.

Assets and liabilities are grouped according to their next repricing date.

Example:

A negative gap means liabilities reprice faster than assets in that bucket.

If rates rise, earnings may be negatively affected.

A positive gap means assets reprice faster.

Gap analysis is useful, but it has limitations.

It does not fully capture:

• Cash-flow timing within buckets
• Non-linear options
• Yield-curve changes
• Basis risk
• Long-term economic value
• Behavioural assumptions

Therefore, it should not be the only IRRBB methodology.

Duration Analysis

Duration measures interest-rate sensitivity.

Training may include:

• Macaulay duration
• Modified duration
• Effective duration
• Key-rate duration

For a simple instrument, modified duration approximates the percentage price change for a small change in yield.

Duration therefore helps quantify how economic value changes as rates move.

Convexity

Duration assumes a roughly linear relationship between rates and price.

However, bond and loan prices are generally non-linear.

Convexity improves the approximation for larger rate movements.

It becomes particularly important when:

• Rate shocks are large
• Products contain options
• Long maturities are involved

A technical IRRBB programme should explain when duration alone is insufficient.

Yield Curve Construction

IRRBB models require appropriate yield curves.

Training may include:

• Zero-coupon curves
• Spot rates
• Forward rates
• Discount factors
• Bootstrapping
• Interpolation
• Extrapolation

Participants should understand the relationship:

Market instruments → Yield curve → Discount factors → Present values → EVE

Errors in the yield curve can flow through every downstream IRRBB calculation.

Non-Maturity Deposits

Non-maturity deposits, or NMDs, are among the most important and difficult IRRBB modelling areas.

Examples include:

• Current accounts
• Savings accounts
• Certain demand deposits

These products may have:

• No contractual maturity
• Immediate withdrawal rights
• Sticky behavioural balances
• Administered interest rates

The Basel application guidance specifically recognises that NMD behaviour may not always be economically rational and therefore requires careful behavioural assumptions.

Why Non-Maturity Deposits Are Difficult to Model

Suppose a savings account is contractually withdrawable immediately.

Treating the entire balance as overnight funding may be too conservative from a behavioural perspective.

Historically, a portion may remain for years.

But treating the balance as permanently stable would also be unrealistic.

The bank needs to estimate:

• Stable balance
• Non-core balance
• Behavioural maturity
• Deposit beta
• Deposit decay
• Repricing lag

These assumptions can have a substantial impact on EVE and NII.

Core and Non-Core Deposits

NMD modelling may separate deposits into:

Core Deposits

Balances expected to remain relatively stable.

Non-Core Deposits

Balances that are more volatile or rate sensitive.

Possible segmentation dimensions include:

• Retail
• SME
• Corporate
• Operational
• Non-operational
• Deposit size
• Customer tenure
• Channel
• Geography
• Rate sensitivity

Segmentation should be based on observed behaviour rather than arbitrary percentages.

Deposit Beta

Deposit beta measures how much a deposit rate changes relative to a market-rate change.

Suppose:

Market benchmark increases by 200 basis points.

Deposit rate increases by 80 basis points.

Approximate deposit beta:

80 / 200 = 40%

A low beta indicates that deposit rates reprice less than the market benchmark.

A high beta indicates greater pass-through.

Deposit beta can materially affect NII projections.

Deposit Repricing Lag

Deposit rates may also respond with a delay.

For example:

• Market rates rise in January.
• Deposit rates begin increasing in March.
• Full repricing occurs only by June.

An NII model that assumes immediate repricing may therefore produce unrealistic results.

IRRBB training should distinguish between:

• Beta
• Lag
• Ultimate repricing
• Speed of repricing

Deposit Decay Modelling

Deposit decay estimates how quickly balances run off over time.

Possible methods include:

• Historical runoff analysis
• Survival analysis
• Vintage analysis
• Exponential decay
• Regression
• Machine-learning methods

The model should consider both normal and stressed behaviour.

Loan Prepayment Risk

Borrowers may repay loans earlier than contractual maturity.

Prepayment behaviour is often interest-rate sensitive.

When market rates fall:

• Borrowers with fixed-rate loans may refinance.
• High-yielding assets may disappear.
• The bank must reinvest at lower yields.

When rates rise:

• Prepayments may slow.
• Asset duration may extend.

This creates option risk.

Prepayment Modelling

Training may cover variables such as:

• Rate incentive
• Loan age
• Loan-to-value
• Borrower segment
• Seasonality
• Economic conditions
• Refinancing cost
• Product type

Possible modelling approaches include:

• Conditional prepayment rates
• Survival models
• Logistic regression
• Hazard models
• Machine-learning models

The appropriate level of complexity should reflect portfolio materiality and available data.

Early Withdrawal of Deposits

Depositors may also exercise options.

Term deposits may permit:

• Early withdrawal
• Partial withdrawal
• Penalty-based withdrawal

When market rates increase significantly, customers may break older low-rate deposits and reinvest at higher rates.

This changes:

• Deposit maturity
• Funding cost
• NII
• Economic value

Behavioural modelling should therefore cover liabilities as well as assets.

Interest Rate Caps and Floors

Products may include contractual caps or floors.

Examples:

• Floating-rate loans with minimum rates
• Loans capped at a maximum rate
• Deposits with administered rate floors

These features create non-linear interest-rate sensitivity.

Participants should understand how optionality affects projected cash flows under different scenarios.

Basis Risk Modelling

Basis risk should be modelled when assets and liabilities reference different rates.

For example:

Assets may reprice with:

• Repo-linked benchmarks
• Treasury yields
• Internal prime rates

Liabilities may reprice with:

• Deposit-administered rates
• Wholesale market rates
• Different external benchmarks

Historical correlations can help quantify basis relationships, but correlations may change under stress.

A strong IRRBB framework should therefore include basis stress scenarios.

Static vs Dynamic Balance-Sheet Modelling

Static Balance Sheet

A static approach generally assumes existing positions run down according to specified assumptions without significant new business.

It is useful for:

• Standardised regulatory analysis
• Current risk assessment
• Economic-value measurement

Dynamic Balance Sheet

A dynamic approach may include:

• New lending
• Deposit growth
• Refinancing
• Reinvestment
• Product pricing
• Funding strategy
• Hedging

Dynamic modelling is particularly relevant to NII and business planning.

However, it introduces more assumptions.

Those assumptions must be transparent.

Constant Balance-Sheet Assumptions

Some earnings simulations maintain the balance sheet at a constant size by replacing maturing products with similar new products.

Training should examine:

• Replacement product
• New-business rate
• Maturity
• Spread
• Funding source
• Hedging

A constant balance sheet does not mean a constant risk profile.

Replacement pricing and yield-curve conditions can materially change NII.

NII Simulation

A practical NII simulation may include:

1. Opening balance sheet
2. Contractual cash flows
3. Repricing schedules
4. Behavioural assumptions
5. Yield-curve scenarios
6. Deposit beta
7. Loan prepayments
8. Funding replacement
9. New-business assumptions
10. Hedging cash flows

The model then estimates income and expense under:

• Base scenario
• Rate-up scenario
• Rate-down scenario
• Yield-curve scenarios
• Internal stress scenarios

EVE Modelling

An EVE model typically requires:

1. Project contractual cash flows.
2. Apply behavioural adjustments.
3. Assign cash flows to appropriate currencies.
4. Apply base discount curves.
5. Calculate base present values.
6. Apply shocked curves.
7. Recalculate present values.
8. Determine change in economic value.

This process should be transparent enough for independent replication.

IRRBB Hedging

IRRBB can be managed through balance-sheet actions and derivatives.

Possible tools include:

• Interest-rate swaps
• Forward Rate Agreements
• Futures
• Options
• Swaptions
• Caps
• Floors

Management may also change:

• Product pricing
• Asset duration
• Liability maturity
• Deposit strategy
• Loan mix

The objective is not always to eliminate IRRBB.

Banks normally accept a controlled amount of interest-rate risk as part of intermediation.

The objective is to maintain the exposure within approved risk appetite.

Interest Rate Swaps

Suppose a bank holds fixed-rate assets funded by floating-rate liabilities.

The bank may use a pay-fixed/receive-floating or receive-fixed/pay-floating structure depending on the exposure it intends to hedge.

Training should cover:

• Swap cash flows
• Fixed leg
• Floating leg
• Discounting
• Forward rates
• Present value
• Duration impact
• NII impact
• Hedge effectiveness

Participants should understand both the derivative valuation and its ALM purpose.

Hedge Effectiveness

A hedge may reduce one risk measure while increasing another.

For example:

A swap might reduce EVE sensitivity but create:

• Short-term NII volatility
• Basis risk
• Counterparty risk
• Collateral requirements

Management should therefore evaluate hedging holistically.

IRRBB Stress Testing

Regulatory shocks should not be the institution’s only stress scenarios.

Internal stress testing may consider:

• Faster rate increases
• Delayed deposit repricing
• Higher deposit beta
• Faster deposit migration
• Slower loan prepayments
• Faster loan prepayments
• Basis widening
• Yield-curve inversion
• Rate volatility
• Funding spread increases

Scenarios should reflect institution-specific vulnerabilities.

Behavioural Stress Testing

Behavioural assumptions themselves should be stressed.

For example:

Base deposit beta = 35%

Stress deposit beta = 70%

Base core deposit proportion = 75%

Stress core deposit proportion = 50%

Base mortgage prepayment = 8%

Stress prepayment = 18%

This can reveal that model assumptions are as important as the rate scenario itself.

IRRBB Risk Appetite

A risk-appetite framework may include limits for:

• ΔEVE
• NII sensitivity
• Earnings at Risk
• Duration
• Repricing gaps
• Basis exposure
• Non-maturity deposit assumptions
• Hedge effectiveness

Limits may be established at:

• Group level
• Legal-entity level
• Currency level
• Business-unit level
• Product level

Escalation rules should be defined before limits are breached.

IRRBB Governance

Effective governance should involve:

• Board
• ALCO
• Treasury
• Risk management
• Finance
• Model development
• Model validation
• Internal audit

Responsibilities should be clearly separated.

Treasury may manage the exposure.

Risk should independently monitor and challenge it.

Validation should assess material models.

Internal audit should assess framework effectiveness.

Board and ALCO Responsibilities

Senior management should understand:

• Major IRRBB exposures
• EVE sensitivity
• NII sensitivity
• Behavioural assumptions
• Hedge strategy
• Limit utilisation
• Stress outcomes
• Model limitations

The board does not need to reproduce the mathematics.

It should be able to challenge assumptions that materially affect risk.

IRRBB and ICAAP

IRRBB is closely connected with ICAAP because adverse interest-rate movements can affect both economic value and earnings.

An ICAAP framework may therefore consider:

• EVE losses
• NII stress
• Capital implications
• Earnings reduction
• Management actions
• Interest-rate risk limits

The Basel Framework treats IRRBB primarily within Pillar 2, reinforcing its connection with broader internal capital adequacy assessment.

IRRBB and ILAAP

IRRBB also interacts with liquidity risk.

For example:

Rapid rate increases may:

• Increase deposit competition
• Increase deposit outflows
• Change funding costs
• Increase collateral requirements
• Affect securities values

This can create both IRRBB and liquidity pressure.

ICAAP, ILAAP and IRRBB should therefore use internally consistent balance-sheet and scenario assumptions.

IRRBB and CSRBB

Credit Spread Risk in the Banking Book (CSRBB) is related to, but distinct from, IRRBB.

CSRBB concerns changes in credit spreads that affect banking-book positions but are not fully explained by IRRBB or credit-default risk.

The EBA’s updated framework addresses IRRBB and CSRBB together while maintaining their conceptual distinction.

Training should not automatically combine interest-rate and spread movements into one risk factor.

Model Risk in IRRBB

IRRBB depends heavily on assumptions.

Examples include:

• Deposit beta
• Deposit maturity
• Prepayment
• Withdrawal
• Yield curve
• Reinvestment
• New business
• Discounting
• Hedge behaviour

Small changes in these assumptions may materially affect risk results.

IRRBB is therefore also a model-risk-management problem.

IRRBB Model Validation

Independent validation may cover:

• Conceptual soundness
• Data quality
• Cash-flow generation
• Yield curves
• Discounting
• Repricing logic
• NMD models
• Prepayment models
• Deposit beta
• Stress scenarios
• EVE
• NII
• Hedging
• Implementation

The objective is not simply to confirm that formulas run without errors.

Validation should determine whether the model represents actual banking behaviour reasonably.

Backtesting Behavioural Models

Behavioural assumptions should be compared against realised outcomes.

Examples include:

Deposit Beta

Compare predicted deposit repricing with actual repricing.

Deposit Decay

Compare predicted balance runoff with actual runoff.

Prepayment

Compare forecast prepayments with realised prepayments.

Withdrawal Behaviour

Compare early-withdrawal assumptions with actual customer behaviour.

Significant deviations should trigger investigation.

Model Monitoring

Ongoing monitoring may include:

• Actual versus predicted deposit beta
• Actual versus predicted runoff
• Prepayment performance
• Stage changes in customer behaviour
• Yield-curve movements
• Limit utilisation
• Hedge performance
• EVE sensitivity
• NII sensitivity

Models should not remain unchanged simply because they were previously validated.

Data Requirements for IRRBB

A robust model may require:

• Product
• Balance
• Currency
• Interest rate
• Benchmark
• Spread
• Fixed/floating indicator
• Repricing date
• Maturity date
• Cash flows
• Customer type
• Optionality
• Deposit history
• Prepayment history
• Collateral
• Hedge information

Data quality problems can materially distort IRRBB.

For example:

An incorrect repricing date may place a loan in the wrong time bucket and change both NII and EVE.

Data Lineage

Training for data teams should cover:

• Source systems
• Data transformations
• Mapping rules
• Product classifications
• Manual adjustments
• Reconciliation
• Version control
• Audit trails

The final IRRBB number should be traceable back to underlying banking positions.

Practical IRRBB Training Exercises

A serious programme should include hands-on exercises.

Exercise 1: Build a Repricing Gap

Participants receive a simplified bank balance sheet and classify positions by next repricing date.

They calculate:

• Rate-sensitive assets
• Rate-sensitive liabilities
• Bucket gaps
• Cumulative gap

Exercise 2: Calculate Duration

Participants calculate:

• Macaulay duration
• Modified duration
• Price sensitivity

for fixed-income assets and liabilities.

Exercise 3: Construct a Yield Curve

Participants use market rates to estimate:

• Zero rates
• Discount factors
• Forward rates

They then use the curve to value banking-book cash flows.

Exercise 4: Calculate EVE

Participants calculate base EVE and shocked EVE.

They determine:

• ΔEVE
• Percentage change
• Principal risk drivers

Exercise 5: Build an NII Model

Participants model:

• Interest income
• Interest expense
• Repricing
• Deposit beta
• Loan repricing
• Funding replacement

They compare baseline and stressed NII.

Exercise 6: Model Non-Maturity Deposits

Participants estimate:

• Core deposits
• Deposit beta
• Repricing lag
• Behavioural maturity
• Decay profile

They examine the effect on EVE and NII.

Exercise 7: Model Loan Prepayments

Participants estimate the impact of rate changes on expected prepayments.

They adjust:

• Cash flows
• Duration
• EVE
• NII

Exercise 8: Apply Basel Shock Scenarios

Participants implement:

• Parallel up
• Parallel down
• Steepener
• Flattener
• Short-rate up
• Short-rate down

and compare resulting EVE impacts.

Exercise 9: Evaluate a Hedge

Participants add an interest-rate swap and compare:

• Pre-hedge EVE
• Post-hedge EVE
• Pre-hedge NII
• Post-hedge NII
• Residual basis risk

Exercise 10: Validate an IRRBB Model

Participants review a model containing deliberately weak assumptions.

They identify:

• Incorrect repricing
• Unsupported deposit beta
• Unrealistic maturity assumptions
• Weak prepayment models
• Missing basis risk
• Poor documentation

Excel-Based IRRBB Training

Excel is highly effective for demonstrating IRRBB mechanics.

Participants can build:

• Repricing ladders
• Yield curves
• Duration models
• EVE models
• NII simulations
• Deposit-beta models
• Deposit-decay models
• Prepayment models
• Stress scenarios
• Hedging analysis

The calculation logic remains visible, making Excel particularly useful for learning and model review.

Spreadsheet controls should nevertheless include:

• Formula validation
• Version control
• Input controls
• Change logs
• Independent review

Python-Based IRRBB Training

Python can support:

• Large transaction datasets
• Yield-curve construction
• Cash-flow engines
• EVE simulation
• NII simulation
• Behavioural modelling
• Statistical deposit modelling
• Prepayment modelling
• Scenario generation
• Model monitoring

Relevant libraries may include:

• Pandas
• NumPy
• SciPy
• Statsmodels
• Scikit-learn
• Matplotlib

The goal should be reproducibility and scalability, not unnecessary complexity.

Who Should Attend IRRBB Training?

The programme may be relevant for:

• Treasury professionals
• ALM professionals
• Interest-rate risk analysts
• Market-risk teams
• Enterprise-risk teams
• Banking-risk professionals
• Finance teams
• ICAAP teams
• Model developers
• Model validators
• Internal auditors
• Regulatory-risk professionals
• Data analysts
• Quantitative analysts
• Consultants
• Senior management
• ALCO members

IRRBB Training for Treasury Teams

Treasury-focused modules may include:

• Balance-sheet hedging
• Yield curves
• Interest-rate swaps
• Funding strategy
• Duration
• NII
• EVE
• Hedge effectiveness

IRRBB Training for ALM Teams

ALM teams may focus on:

• Repricing gaps
• Behavioural cash flows
• NMD modelling
• Deposit beta
• Prepayments
• NII simulation
• EVE
• Risk limits

IRRBB Training for Risk Teams

Risk teams may focus on:

• Independent measurement
• Risk appetite
• Regulatory scenarios
• Internal stress testing
• Model risk
• Limit monitoring
• Governance

IRRBB Training for Model Developers

Technical training may include:

• Yield-curve modelling
• Statistical deposit models
• Survival analysis
• Prepayment models
• Scenario simulation
• Calibration
• Python implementation
• Documentation

IRRBB Training for Model Validators

Validation teams may focus on:

• Model conceptual soundness
• Benchmarking
• Replication
• Backtesting
• Assumption review
• Sensitivity analysis
• Data quality
• Implementation verification

IRRBB Training for Internal Audit

Audit-focused modules may examine:

• Governance
• Policies
• Limit frameworks
• Data controls
• Model governance
• Independent validation
• Stress testing
• Management reporting
• Remediation

IRRBB Training for Senior Management

Senior management should understand:

• Major EVE exposure
• Major NII exposure
• Balance-sheet vulnerabilities
• Deposit assumptions
• Loan optionality
• Hedge strategy
• Limit utilisation
• Stress results

Management should be capable of challenging the risk rather than relying solely on model outputs.

Recommended IRRBB Training Formats

Physical Corporate Workshop

Suitable for:

• Cross-functional teams
• Practical modelling
• Case studies
• ALCO workshops
• Model review

Live Virtual Training

Suitable for:

• Distributed teams
• Instructor-led models
• Excel demonstrations
• Python demonstrations
• Interactive exercises

Self-Paced IRRBB Training

Suitable for:

• Foundations
• Refresher learning
• Employee induction

Self-paced programmes should include assessments.

Watching content does not prove implementation capability.

Hybrid Programme

A hybrid IRRBB programme may combine:

• Recorded concepts
• Live workshops
• Excel models
• Python exercises
• Assignments
• Assessments
• Mentoring
• Post-training support

How to Choose an IRRBB Training Provider

Regulatory Accuracy

The provider should understand:

• Basel IRRBB framework
• Pillar 2
• EVE
• NII
• Supervisory outlier tests
• Behavioural assumptions
• Jurisdiction-specific requirements

Quantitative Capability

The training should contain actual modelling.

Participants should calculate and interpret:

• Duration
• Yield curves
• EVE
• NII
• Deposit beta
• Prepayment
• Stress scenarios

Behavioural Modelling Capability

Any course that teaches only duration and gap analysis is incomplete.

Modern IRRBB requires serious attention to:

• Non-maturity deposits
• Deposit pricing
• Prepayments
• Embedded options

ALM Understanding

IRRBB cannot be separated from balance-sheet management.

The provider should understand:

• Treasury
• ALM
• Funding
• Product pricing
• Hedging
• Risk appetite

Practical Implementation

Training should use:

• Excel
• Python
• Case studies
• Balance-sheet datasets
• Model validation exercises

IRRBB Training with Peaks2Tails

Peaks2Tails currently provides an IRRBB short-course pathway covering areas including yield curves and scenarios, NII and EVE computation, derivative valuation, interest-rate option modelling and behavioural modelling.

Its broader Integrated Treasury Risk Modelling programme positions IRRBB alongside ICAAP, ILAAP and CSRBB and provides practical resources including concept lectures, Excel models, Python code, reading materials and practice questions.

A customised IRRBB corporate training programme can therefore be structured around:

• IRRBB fundamentals
• Yield curves
• Gap analysis
• Duration
• Convexity
• EVE
• NII
• Basel scenarios
• Non-maturity deposits
• Deposit beta
• Deposit decay
• Loan prepayments
• Embedded options
• Basis risk
• Yield-curve risk
• Stress testing
• Hedging
• Model validation
• ICAAP integration
• Excel implementation
• Python implementation

The final programme should be adapted to the institution’s balance sheet, products, currencies, existing models and participant responsibilities.

Frequently Asked Questions About IRRBB Training

What is IRRBB training?

IRRBB training teaches banking professionals how to identify, measure and manage interest-rate risk arising from banking-book assets, liabilities and off-balance-sheet positions.

What does IRRBB stand for?

IRRBB stands for Interest Rate Risk in the Banking Book.

Is IRRBB a Pillar 1 or Pillar 2 risk?

Under the Basel Framework, IRRBB is primarily treated under Pillar 2 because of the heterogeneous nature of the risk across institutions.

What is EVE in IRRBB?

EVE stands for Economic Value of Equity. It measures the economic-value impact of interest-rate changes on banking-book assets, liabilities and relevant off-balance-sheet positions.

What is NII in IRRBB?

NII stands for Net Interest Income. It measures the earnings effect of interest-rate changes on interest income and interest expense.

What is the difference between EVE and NII?

EVE focuses on longer-term present-value sensitivity.

NII focuses primarily on earnings sensitivity over a specified planning horizon.

Both are important.

What risks are covered under IRRBB?

IRRBB commonly includes:

• Repricing risk
• Yield-curve risk
• Basis risk
• Option risk

What are the Basel IRRBB shock scenarios?

The Basel framework includes six standardised rate scenarios: parallel up, parallel down, steepener, flattener, short-rate up and short-rate down.

Have Basel IRRBB shocks changed recently?

Yes. The Basel Committee recalibrated the prescribed shocks in July 2024, with the revised standard implemented from 1 January 2026.

What is a non-maturity deposit?

An NMD is a deposit without a specified contractual maturity, such as many current and savings accounts. Because customer behaviour determines effective maturity and repricing, NMDs require behavioural modelling.

What is deposit beta?

Deposit beta measures the degree to which deposit rates respond to changes in a reference market interest rate.

Does IRRBB training include behavioural modelling?

A comprehensive programme should include NMD behaviour, deposit beta, repricing lags, deposit decay and loan prepayments.

Can IRRBB be modelled in Excel?

Yes. Excel is useful for gap analysis, yield curves, duration, EVE, NII, behavioural assumptions and scenario analysis.

Can Python be used for IRRBB?

Yes. Python can support cash-flow engines, behavioural models, simulations, yield curves, EVE, NII and monitoring.

Who should attend IRRBB training?

Treasury, ALM, risk, finance, model development, model validation, internal audit and quantitative teams can benefit from IRRBB training.

Does IRRBB training guarantee regulatory compliance?

No.

Training builds knowledge and implementation capability. Regulatory compliance also depends on applicable jurisdictional rules, governance, systems, data, models, controls and supervisory assessment.

Conclusion: IRRBB Training Must Build Balance-Sheet Risk Management Capability, Not Just Regulatory Knowledge

IRRBB is sometimes presented as a narrow regulatory problem:

Apply an interest-rate shock.

Calculate EVE.

Compare the result with a limit.

Prepare the report.

That approach misses the real risk.

Interest-rate risk exists because banks perform maturity transformation.

They accept deposits.

They provide loans.

They transform short-term funding into longer-term assets.

They offer fixed and floating rates.

They give customers options.

They hedge selected exposures.

Every one of these decisions creates sensitivity to changes in interest rates.

IRRBB is therefore fundamentally a balance-sheet-management problem.

A bank can be profitable under today’s interest-rate environment while carrying significant vulnerability to tomorrow’s rate environment.

Consider a simplified example.

A bank originates large volumes of long-term fixed-rate loans when interest rates are low.

At the same time, much of its funding comes from savings deposits.

Initially, this structure may generate an attractive margin.

Then market rates rise.

Customers expect higher deposit rates.

Deposit beta increases.

Funding costs rise.

The fixed-rate loans continue earning the original coupon.

Net interest margin contracts.

At the same time, the economic value of the long-duration fixed-rate assets falls.

The bank now experiences both:

• NII pressure
• EVE pressure

This example shows why IRRBB cannot be understood through one metric.

The earnings perspective asks:

What happens to profitability?

The economic-value perspective asks:

What happens to the underlying value of the balance sheet?

Management needs both answers.

The challenge becomes greater because banking products do not behave according to contractual terms alone.

A savings deposit may technically be withdrawable tomorrow but remain with the bank for many years.

A fixed-rate mortgage may contractually mature in twenty years but refinance after five.

A term deposit may be withdrawn early.

A floating-rate loan may not reprice immediately.

A customer may tolerate a low deposit rate when market rates move modestly but demand rapid repricing after larger increases.

These behaviours determine the actual interest-rate exposure.

That is why behavioural modelling sits at the centre of advanced IRRBB.

A bank may spend considerable resources building sophisticated EVE calculations while relying on unsupported assumptions for non-maturity deposits.

That creates false precision.

If the assumed behavioural maturity of deposits is wrong, the entire duration profile can be distorted.

If deposit beta is wrong, projected NII may be unreliable.

If mortgage prepayment behaviour is wrong, asset cash flows may be misstated.

The most complicated mathematical model cannot repair poor behavioural assumptions.

Effective IRRBB training should therefore force participants to understand where assumptions come from.

For every important parameter, the analyst should ask:

• Which data support this assumption?
• How long is the observation period?
• Does behaviour change across customer segments?
• Does behaviour change across rate cycles?
• What happened during periods of stress?
• How sensitive is the result to the assumption?
• Has the model been independently validated?

This creates a much stronger risk-management culture than simply importing percentages into a model.

Deposit beta provides a good example.

Suppose historical data suggest that the deposit rate increases by 30% of a benchmark-rate increase.

It may be tempting to use a permanent beta of 30%.

But customer behaviour may change.

Competitors may offer higher rates.

Digital channels may make switching easier.

Deposit concentration may increase.

The current interest-rate cycle may differ from the historical sample.

A beta that worked previously may not work in the next rate cycle.

The model therefore needs:

• Monitoring
• Backtesting
• Segmentation
• Stress testing
• Recalibration

The same applies to behavioural maturity.

A bank should not assume that all savings deposits have identical stability.

A small salary account may behave differently from a large rate-sensitive corporate balance.

Operational deposits may differ from surplus cash.

Digital-only customers may behave differently from relationship customers.

Proper segmentation improves risk understanding.

Loan optionality creates another layer of complexity.

Prepayment is not merely an operational event.

It changes duration.

When rates decline, borrowers may refinance.

The bank loses higher-yielding assets and reinvests at lower rates.

When rates rise, refinancing may slow.

Assets remain on the balance sheet longer.

The bank therefore faces asymmetric behaviour.

A simple contractual cash-flow model cannot capture this properly.

This is why option risk matters.

Yield-curve risk creates another challenge.

Interest rates do not move as a single number.

Short-term rates can rise while long-term rates remain stable.

The curve can steepen.

It can flatten.

It can invert.

Different banking-book positions respond to different parts of the curve.

A bank may appear well hedged against a parallel movement while remaining exposed to a steepener or flattener scenario.

The Basel framework’s six standardised scenarios explicitly reflect this multidimensional nature of interest-rate risk.

The 2024 Basel recalibration, effective from January 2026, reinforces the need for training material and internal models to remain current rather than relying indefinitely on historical regulatory parameters.

But regulatory scenarios are only the starting point.

Each institution should also ask:

Which rate movements are particularly dangerous for our balance sheet?

A bank funded largely through short-term deposits may be vulnerable to rapid short-rate increases.

A bank with long-duration securities may be vulnerable to long-rate shocks.

A mortgage lender may be highly exposed to rate-driven prepayment changes.

A bank with administered-rate deposits may face significant deposit-beta uncertainty.

Internal stress testing should reflect these characteristics.

This is where IRRBB becomes closely linked to strategy.

The business team decides which loans to originate.

Treasury determines funding and hedging.

ALM analyses balance-sheet structure.

Finance forecasts earnings.

Risk defines appetite and limits.

Model teams quantify behavioural assumptions.

Validation challenges those models.

These functions cannot operate independently.

For example, business teams may aggressively originate long-duration fixed-rate products because current margins appear attractive.

Treasury may assume that savings deposits will remain sticky.

Finance may forecast stable deposit costs.

Risk may calculate acceptable EVE under current assumptions.

Individually, every analysis may appear reasonable.

Combined, the organisation may be building a substantial hidden rate position.

An effective IRRBB framework brings these views together.

That is also why risk appetite matters.

The institution should decide how much:

• EVE sensitivity
• NII sensitivity
• Basis exposure
• Repricing mismatch
• Option risk

it is willing to accept.

Limits should be linked to management action.

A breached limit should trigger more than an email.

Possible responses may include:

• Rebalancing assets
• Changing deposit pricing
• Changing loan pricing
• Altering product mix
• Extending funding maturity
• Executing derivatives
• Reducing originations

But hedging itself requires careful analysis.

A derivative can reduce one risk and create another.

An interest-rate swap may reduce duration exposure but introduce:

• Basis risk
• Counterparty risk
• Collateral requirements
• NII volatility

The objective is not to make every sensitivity zero.

Banking inherently involves interest-rate risk.

The objective is to understand the exposure, price it appropriately and maintain it within risk appetite.

This principle is important because excessive hedging can also create problems.

A model based on inaccurate deposit assumptions may recommend a hedge that becomes inappropriate when customer behaviour changes.

Hedging decisions are only as good as the underlying risk measurement.

Independent model validation therefore has a major role.

Validators should challenge more than formulas.

They should examine:

• Data
• Product mapping
• Repricing logic
• Yield curves
• Behavioural assumptions
• Scenario construction
• Discounting
• Hedge modelling
• Implementation

A model can calculate exactly what it was programmed to calculate and still be wrong economically.

That distinction should be central to IRRBB training.

Participants should learn how to challenge a model.

Why was this deposit maturity selected?

Why is this beta appropriate?

Why is this prepayment assumption stable?

Why was this curve used?

Why is this hedge mapped to this exposure?

Why is this scenario severe enough?

These questions create risk-management capability.

IRRBB must also connect with broader bank risk frameworks.

A major interest-rate shock can reduce NII.

Lower NII can weaken profitability.

Lower profitability can weaken capital generation.

Falling securities values can reduce economic value.

Deposit pricing pressure can increase liquidity risk.

Customer withdrawals can activate ILAAP stress.

Capital implications can feed into ICAAP.

Therefore:

IRRBB, ICAAP and ILAAP should not be modelled as unrelated regulatory silos.

They are different views of the same balance sheet.

This is particularly important for stress testing.

A coherent scenario should affect:

• Interest rates
• Funding
• Deposits
• Loan behaviour
• NII
• EVE
• Liquidity
• Capital

Consistent assumptions improve management understanding.

Data is another critical issue.

IRRBB depends on granular information.

A bank needs to know:

• When a product reprices
• What benchmark it follows
• Whether the rate is fixed or floating
• Whether the customer can prepay
• Whether the customer can withdraw
• Which currency applies
• Which behavioural model applies

A single incorrect field can change cash-flow timing and therefore change the measured risk.

This is why technology and data teams should be included in IRRBB training.

They need to understand that data fields are not simply reporting requirements.

They represent economic behaviour.

The same principle applies to senior management.

Managers do not need to become quantitative modellers.

But they should know the questions to ask.

For example:

• What causes our EVE exposure?
• What causes our NII exposure?
• Which deposit assumptions matter most?
• How would a rate cut affect us?
• How would a rapid rate increase affect us?
• What happens if deposit beta doubles?
• How much of our exposure is hedged?
• What are the hedge limitations?
• Which models drive the result?
• When were those models validated?

A board capable of asking these questions is far better positioned than a board that receives only an IRRBB ratio.

This is the real purpose of training.

The objective is not to memorise that IRRBB stands for Interest Rate Risk in the Banking Book.

It is not to memorise six regulatory shock names.

It is not to reproduce a duration formula.

The objective is to build professionals who understand how interest rates move through the balance sheet.

A trained ALM analyst should be able to construct repricing cash flows.

A treasury professional should understand hedging choices.

A quantitative analyst should model deposit behaviour.

A modeller should calculate EVE and NII.

A validator should challenge assumptions.

An auditor should evaluate governance.

A senior manager should understand the strategic implications.

This is the point at which IRRBB training becomes valuable.

It converts regulatory terminology into balance-sheet decision-making capability.

An effective training programme should therefore combine:

• Regulatory understanding
• Financial mathematics
• Yield-curve modelling
• Behavioural modelling
• EVE
• NII
• Stress testing
• Hedging
• Validation
• Excel
• Python
• Case studies

The success of the programme should be judged by whether participants can answer practical questions:

1. Where does our interest-rate risk come from?
2. How does it affect economic value?
3. How does it affect earnings?
4. Which customer behaviours create uncertainty?
5. How would different yield-curve scenarios affect us?
6. How much risk can we accept?
7. Which hedges are appropriate?
8. How reliable are our models?

When those answers become clear, IRRBB moves beyond regulatory compliance.

It becomes what it should be:

a practical framework for understanding and managing the interest-rate sensitivity of the banking balance sheet.