ALM - RiskTAIIB

ALM – Definition

Asset Liability Management (ALM) is a comprehensive and dynamic framework used by banks and other financial institutions to manage the risks that arise due to mismatches between the assets and liabilities. The essence of ALM lies in addressing the timing differences in the maturity of assets and liabilities and ensuring that the liquidity requirements of the institution are met. This involves managing the risks associated with changes in interest rates, liquidity, currency exchange rates, and credit and market variables that can impact the institution’s capital and earnings.

Asset Liability Management is concerned with strategic balance sheet management involving risks caused by changes in interest rates, exchange rate, credit risk and the liquidity position of bank. ALM is about management of Net Interest Margin (NIM) and EVE to ensure that its level and riskiness are compatible with risk/return objectives of the bank. It is an integrated approach to bank financial management requiring simultaneous decision about types and amount of financial assets and liabilities it holds or its mix and volume.

Goals of ALM

The primary goals of ALM are given below:

1. Currency Risk Management – For Banks engaged in international banking, managing the risk associated with fluctuation in foreign exchange rates is crucial.

2. Capital Management – ALM helps in maintaining the capital adequacy ratio as required by Regulators, thereby supporting the bank’s growth and stability.

3. Interest Rate Risk Management – Manage the impact of interest rate changes on Balance Sheet to maintain a stable net interest margin and overall economic value.

4. Liquidity Management – Ensure that the financial institution has enough liquid resources to meet its obligations at all times. This involves maintaining a balance between short term assets and liabilities to avoid liquidity shortages.

5. Profitability and Growth – Strategic assets and liabilities matching should also aim at enhancing profitability and supporting sustainable growth.

Objectives of ALM

Review the interest rate structure and compare the same to the interest and product pricing of both assets and liabilities.
Evaluate the loan and investment portfolios in the light of the foreign exchange risk and liquidity risk that might arise.
Examine the credit risk and contingency risk that may originate either due to rate fluctuations or otherwise and assess the quality of assets.
Review, the actual performance against the projections made and analyse the impact on spreads.
To manage liquidity by ensuring that the bank has enough cash and liquid assets to meet short-term and long-term obligations.
To meet all Regulatory Compliances such as Basel III, IFRS 9, and other regulatory requirements related to capital and liquidity.
To stabilise the short-term profits and long-term earnings of the bank. The parameters that are selected for the purpose of stabilising asset liability management of banks are:

-Net Interest Income (NII)

-Net Interest Margin (NIM)

-Economic Equity Ratio

Importance of ALM in Banking

1. Risk Management – ALM helps in identifying and managing the market risks that include interest rate risk, currency risk and other price risks.

2. Liquidity Management – It ensures that a bank maintains adequate liquidity to meet its obligations as they become due, without incurring unacceptable losses. ALM framework helps in forecasting and planning for future cash flow requirements under various scenarios.

3. Regulatory Compliance- Banks are required to adhere to various regulatory norms related to liquidity and capital adequacy which are facilitated by effective ALM practice.

4. Profit Optimisation – By strategically matching assets and liabilities, banks can optimise their interest margin and enhance profitability while maintaining risk within acceptable limits.

5. Stability and Confidence – Effective ALM contributes to the overall financial stability of a bank and strengthens the confidence of investors, customers and other stake holders in its financial health.

Asset Liability Committee (ALCO)

The ongoing raft of bank failures and an evolving regulatory landscape have renewed the focus on balance sheet, interest rate, and liquidity risk management. The ALCO is a decision-making unit, consisting of the Bank’s senior management including CEO, CFO, CRO responsible for integrated balance sheet management from risk-return perspective including the strategic management of interest rate risk and liquidity risks. The role of ALCO is three-dimensional. First, the committee is responsible for proper governance, in terms of understanding the inherent risk of the organization, and establishing a policy framework that supports its risk appetite. Secondly, it is responsible for ensuring that such risk is measured properly and continuously, through a suite of approaches that may include EVE, and earnings at risk. Lastly, ALCO must ensure that such measurements and changes in risk trends over time are effectively and regularly reported and communicated to the board and other key stakeholders, through a set of comprehensive—but also transparent and meaningful reports.

Traditionally, ALCO covers these areas.

Fundamental analysis, including an economic and market review
IRR
Liquidity Risk
Currency Risk
Balance Sheet Strategy
Capital Allocation and Management

Alternatively, some ALCOs take a different approach like return on equity (ROE).

Economic market review and current ROE decomposition
IRR
Balance Sheet Strategy
Liquidity Risk
Currency Risk
Capital Allocation and Management
ROE forecast

While each FI will have to decide the role of its ALCO, its powers and responsibilities as also the decisions to be taken by it, its responsibilities would normally include:

monitoring the market risk levels of the FI by ensuring adherence to the various risk-limits set by the Board;
articulating the current interest rate view and a view on future direction of interest rate movements and base its decisions for future business strategy on this view as also on other parameters considered relevant.
deciding the business strategy of the FI, both – on the assets and liabilities sides, consistent with the FI’s interest rate view, budget and pre-determined risk management objectives. This would, in turn, include:
determining the desired maturity profile and mix of the assets and liabilities;
product pricing for both – assets as well as liabilities side;
deciding the funding strategy i.e. the source and mix of liabilities or sale of assets; the proportion of fixed vs floating rate funds, wholesale vs retail funds, money market vs capital market funding
domestic vs foreign currency funding, etc.
reviewing the results of and progress in implementation of the decisions made in the previous meetings

Composition of ALCO

The size (number of members) of ALCO depends on the size of each institution, business mix and organisational complexity. To ensure commitment of the Top Management and timely response to market dynamics, the CEO/CMD/DMD or the ED normally heads the Committee. Though the composition of ALCO could vary across the FIs as per their respective set up and business profile, ALCO normally admits the Chiefs of Investment, Credit, CRO, CFO, Resources Management or Planning, Funds Management, Treasury (forex and domestic), International Business and Economic Research and Head of the Technology Division as the members of the Committee. The Management Committee of the Board or any other Specific Committee constituted by the Board oversees the implementation of the ALM system and review its functioning periodically.

Components of ALM

(1) Liquidity Risk Management

(2) Interest Rate Risk Management

(3) Currency Risk Management

(4) Balance Sheet Management

Liquidity Risk Management

Measuring and managing liquidity risks are vital for effective operation of Fis and Banks. Funding liquidity risk is the risk that the bank will not be able to meet efficiently both expected and unexpected current and future cash flow. Market liquidity risk is the risk that a firm cannot easily offset or eliminate a position without significantly affecting the market price because of inadequate market depth or market disruption. By assuring a FI’s ability to meet its liabilities as they become due, liquidity management tries to reduce the probability of an adverse situation developing. The importance of liquidity transcends individual institutions, as liquidity shortfall in one institution can have repercussions on the entire system. Banks measure not only the liquidity positions on an ongoing basis but also examine how liquidity requirements are likely to evolve under different assumptions. Past experience shows that assets commonly considered to be liquid, such as Government securities and other money market instruments, could also become illiquid when the market and players become unidirectional. Therefore, liquidity has to be tracked through maturity or cash flow mismatches. For measuring and managing net funding requirements, the use of a maturity ladder and calculation of cumulative surplus or deficit of funds at selected maturity dates is adopted as a standard tool. The Maturity Profile are used for measuring the future cash flows of Bank in different time buckets. The time buckets are distributed as under:

i) 1 to 14 days

ii) 15 to 28 days

iii) 29 days and upto 3 months

iv) Over 3 months and upto 6 months

v) Over 6 months and upto 1 year

vi) Over 1 year and upto 3 years

vii) Over 3 years and upto 5 years

viii) Over 5 years and upto 7 years

ix) Over 7 years and upto 10 years

x) Over 10 years.

The investments are assumed as illiquid due to lack of depth in the secondary market and are, therefore, generally shown, as per their residual maturity, under respective time buckets. However, Banks maintaining securities in the ‘Trading Book’, are subject to the following preconditions:

i) The composition and volume of the Trading Book should be clearly defined;

ii) Maximum maturity/duration of the trading portfolio should be restricted;

iii) The holding period of the trading securities should not exceed 90 days;

iv) Cut-loss limit(s) should be prescribed;

v) Product-wise defeasance periods (i.e. the time taken to liquidate the ‘position’

on the basis of liquidity in the secondary market) should be prescribed;

vi) Such securities should be marked-to-market on a daily/weekly basis and the

revaluation gain/loss should be charged to the profit and loss account.

The ALCO of the banks approve the volume, composition, maximum maturity and duration, holding and defeasance period, cut loss limits of the ‘Trading Book’ and Banking Book.

Sources of Liquidity Risk

Asset Related

1. Insufficient availability of collateral.

2. Disruption in payment or settlement system.

3. Increased collateral requirements due to market risk losses, rating triggers or asymmetric documentation.

4. Inadequacy of a firm’s infrastructure to conduct securitisation transaction.

5. Reduced liquidity of outright market for securities.

6. Too large a trading position relative to market volume, open interest and number of market makers.

7. Failure of specialist liquidity providers in niche security markets.

8. Unwillingness of counterparties to take settlement risk on collateral transfer across time zones.

9. Spurious diversification, while portfolios might be diversified strategies and may be correlated across counterparties.

10. Lack of demonstrable liquidity due to bespoke nature of transaction.

11. Mismanagement and corruption in giving Loans and Advances

Liability Related

1. Accelerated withdrawal of relationship based and transactional deposits from bank and dealers.

2. Lack of competitive deposit strategy and products.

3. More rapid loan than deposits.

4. Loss of access to unsecured wholesale funding or extreme increase in cost.

5. Material dependence on wholesale short and long term unsecured funding, including from higher related counterparties.

6. Failure of major provider of unsecured funds.

7. Concentration of wholesale funding sources.

8. Reduction in the availability of money market lines available to the bank.

9. Reduction in ability to raise term money.

10. Reliance on credit dependent source of secured funding, correspondingly, availability of committed irrevocable secured funding lines.

11. Restricted access to secured funding markets.

12. Reliance on synthetic funding from better-rated counterparties.

13. Technology risk related to funding.

14. Rating downgrade.

15. Reliance on few wholesale depositors

Liquidity Risk Management Tools

1. Static Funding Gap Defines the short fall in maturing liabilities required to service maturing assets– it is usually calculated on a maturity bucket basis and is calculated as the net asset

position over total liabilities.

2. Dynamic Cash Flow Gap This includes a measurement based on maturing assets and liabilities plus assumed marketable asset liquidation over a given period.

3. Liquidity Ratio = Assets/ Liabilities

4. Liquidity Asset Ratios This is the ratio of liquid assets to total liabilities with liquid assets defined to include items such as cash and cash equivalents, trading account securities, repos investments into government securities.

5. Concentration Ratios This is an important ratio that reassures the funding from

a particular source compared to assets or liabilities or capital.

6. Liquidity Stress Measurement A number of ratios can be examined here looking at

multiple low stress and high stress scenarios

7. Liquidity Coverage Ratio (LCR) – High-quality liquid assets to cover 30-day net cash outflows.

8. Net Stable Funding Ratio (NSFR) – Long-term stable funding vs. required stable funding.

9. Cash Flow Gap Analysis – Measures mismatches between inflows and outflows.

1. Base Case Analysis- MTM Analysis

2. Simulation Analysis

Monte Carlo Simulation

Delta Normal Simulation

Covariance Simulation

Historical Simulation

Mixed Simulation

Advanced Simulation

3. Cashflow Optimisation

4. What If Analysis

Scenario Analysis

Sensitivity Analysis

Scenario Simulation

5. Profit and Loss Analysis

Profit and Loss Curve Analysis

Profit and Loss Surface Analysis

10. Dependency Ratio on Significant Deposit = (Cash + Tradable Securities + Short Term Banking Exposure (0-14 Days)/ Sum of Significant Deposit

11. Dependency on Financial Institution = (Cash + Tradable Securities + Short Term Banking Exposure (0-14 Days)/ Resources from Financial Institutions

12. Liquidity Vulnerability = Easily Disposable Assets/ Easily Withdrawal Funds

13. Easily Disposable Assets:

Cash in hand and Settlement Accounts

Placement on the Interbank market with short maturity, within 14 days

Treasury Bonds and Government Bonds

14. Easily Withdrawal Funds

Sight Deposits and Current Account Deposits

Term Deposits

Liabilities from REPO

15. Coverage Ratio of Bank Investment = Shareholder’s Equity/ (Tangible Assets + Intangible Assets + Business Shares)

16. Coverage of Loans by Deposits = Deposits/ Loans

17. Coverage Short Term Liabilities by Liquid Assets = Highly Liquid Assets/ Short Term Liabilities

18. Loan to Deposit Ratio = Loan/ Deposit

If LTD >100%, sign of excessive asset growth while LTD<70% implies excessive liquidity and low return on funds.

Behavioural Cash Flow Generation Model

The following types are some of the behavioural models that are used by Banks for cash flow analysis for dynamic portfolio construction.

1. Behavioural model for Fixed Deposit.

2. Behavioural model for Core Deposit (savings/current)

3. Future business growth

4. Analysis of prime rate changes by external interest rate, SOFR

5. Deposit Run-off model

6. Contingency Funding model

7. Prepayment model (Prepayment ratio calculation at offered rate, Prepayment ratio calculation by interest rate difference, by Product(retail, corporate, treasury)

8. Early redemption model for FDs

9. Undrawn utilisation

10. Rollover model

11. Deposit Step Up model

12. Deposit Step Down model

13. Natural attrition model

Currency Risk

Currency Risk in Asset-Liability Management in Banking refers to the potential impact of exchange rate fluctuations on a bank’s financial condition, specifically on its assets, liabilities, and off-balance-sheet exposures denominated in foreign currencies.

The increased capital flows across free economies following deregulation have contributed to increase in the volume of transactions. Large cross border flows together with the volatility has rendered the Bank’s balance sheets vulnerable to exchange rate movements.

If the liabilities in one currency exceed the level of assets in the same currency, then the currency mismatch adds value or erodes value depending upon the currency movements. Mismatched currency position, besides exposing the balance sheet to movements in exchange rate, also exposes it to country risk and settlement risk. Banks undertake operations in foreign exchange such as borrowings and making loans in foreign currency, which exposes them to currency or exchange rate risk. Thus, Currency risk affects the value of a bank’s assets, liabilities and off-balance sheet items denominated in foreign currencies. Effective currency risk management in ALM requires balancing the costs of hedging with the potential risks of leaving positions unhedged, while complying with regulatory requirements and maintaining adequate liquidity.

Sources of Currency Risk

Currency risk arises from fluctuations in the value of one currency relative to another. These fluctuations can impact businesses, investors, and governments engaged in international transactions. Some of the primary sources of currency risk are given below:

1. Transaction Risk – Occurs when a bank, company or individual has receivables or payables denominated in a foreign currency. Example: A U.S. company selling goods to Europe invoices in euros. If the euro weakens against the dollar before payment is received, the company gets fewer dollars than expected.

2. Translation Risk (Accounting Risk) – Affects banks and multinational companies when consolidating financial statements from foreign subsidiaries into the parent company’s reporting currency. Example: A British company with a U.S. subsidiary must convert USD-denominated assets and liabilities into GBP for reporting. Exchange rate changes can alter the reported financial position.

3. Economic Risk (Operating Exposure)- Long-term risk arising from changes in exchange rates affects the bank and company’s future cash flows, competitiveness, and market value. Example: A Japanese car exporter becomes less competitive if the yen appreciates, making its cars more expensive in foreign markets.

4. Sovereign Risk (Political & Regulatory Risk) – Government actions (e.g., capital controls, currency pegs, or devaluations) can impact exchange rates. Example: A sudden currency devaluation (like Argentina’s peso in 2018) eroded foreign investors’ returns.

5. Interest Rate Differentials – Higher interest rates in a country attracts foreign capital, strengthening its currency and vice versa. Example: If the U.S. Federal Reserve raises interest rates while Europe keeps rates low, the USD may appreciate against the euro.

6. Inflation Differentials – Countries with higher inflation typically see their currency depreciate because purchasing power declines. Example: If inflation in Turkey is much higher than in the EU, the Turkish lira may weaken against the euro over time.

7. Speculative Trading & Market Sentiment – Currency values can be influenced by traders, hedge funds, and algorithmic trading based on economic forecasts, political events, or risk appetite. Example: A sudden shift to “risk-off” sentiment can strengthen safe-haven currencies like the USD, JPY, or CHF.

8. Balance of Payments (Trade Deficits/Surpluses) – A country running a persistent trade deficit may see its currency weaken due to higher demand for foreign currencies. Example: A prolonged U.S. trade deficit could pressure the USD downward if imports exceed exports.

9. Geopolitical Events & Crises – Wars, elections, trade wars, or pandemics can trigger currency volatility. Example: Brexit caused significant GBP fluctuations due to uncertainty over the UK’s economic future.

10. Liquidity Risk – Thinly traded currencies (exotic currencies) can experience sharp swings due to low trading volumes. Example: The South African rand (ZAR) or Thai baht (THB) may be more volatile than major currencies like EUR or USD.

Types of Currency Risk

Structural Currency Risk: Mismatches between foreign currency assets and foreign currency liabilities
Transactional Risk: Impact on future cash flows from foreign currency transactions. Loan disbursed in USD, but funded with INR.
Translation Risk: Effect on Financial Statements and Balance Sheet when consolidating foreign operations. A UK bank consolidating results of its Indian subsidiary.

Measurement Approaches for managing Currency Risk

1. Gap Analysis – Measures mismatches between foreign currency assets and liabilities by maturity buckets. Identifies net open positions in each currency

2. Value-at-Risk (VaR) Models – Estimates potential losses from currency movements at a given confidence level. Incorporates volatility and correlation between currencies

3. Stress Testing – Assesses impact of extreme currency movements. Scenario analysis (e.g., 10% depreciation of local currency).

Management Strategies

1. Natural Hedging – Matching foreign currency assets with liabilities in the same currency. Creating offsetting positions through business operations.

2. Financial Hedging Instruments – Forward contracts, Currency swaps, Options (caps, floors, collars) and Futures contracts.

3. Limits Framework – Setting open position limits by currency. Establishing loss limits for currency exposures.

4. Regulatory Considerations – Basel Framework Requirements for Capital requirements for foreign exchange risk (Market Risk framework) and Stress testing requirements.

5. Local Regulatory Limits – Regulators impose net open position limits (often as % of capital) and Reporting requirements for large currency exposures.

6. Contingency planning for currency crises.

7. Integration with overall ALM and interest rate risk management.

8. Implementing real-time monitoring and reporting systems.

9. Ensuring top management and ALCO involvement in setting and reviewing currency risk policies.

10. Establishing risk tolerance levels for FX exposure.

Challenges in Managing Currency Risk

Correlation Breakdowns: When historically correlated currencies move differently.
Liquidity Constraints: Applicable to Emerging market currencies.
Accounting Complexities: Hedge accounting requirements are different in different jurisdictions.
Operational Risks: Settlement risk in foreign currency transactions.
Market volatility: Unexpected geopolitical or macroeconomic shocks.
Basis risk: Imperfect correlation between hedge instrument and underlying exposure.
Liquidity risk in FX markets
Regulatory changes: Vary across jurisdictions.
Complex modeling: In multi-currency scenarios with optionality.

Impact of Currency Risk on Balance Sheet

ALM aims to manage the mismatch between assets and liabilities in terms of maturity, interest rate sensitivity, and currency. In banks with multi-currency operations, exchange rate volatility can:

Balance Sheet Volatility , altering the value of foreign assets and liabilities.
Earnings Uncertainty , impacting net interest income (NII)
Affect regulatory capital adequacy ratios under Base III norms.
Distort liquidity positions
Create basis risk from hedging mismatches

Interest Rate Risk (IRR)

Interest rate risk is the risk when changes in market interest rates might adversely affect a FI’s financial condition. The immediate impact of changes in interest rates is on FI’s earnings by changing its Net Interest Income (NII). A long-term impact of changing interest rates is on FI’s Market Value of Equity (MVE) or Net Worth as the economic value of bank’s assets, liabilities and off-balance sheet positions get affected due to variation in market interest rates. The interest rate risk when viewed from these two perspectives is known as ‘earnings perspective’ and ‘economic value’ perspective, respectively. The risk from the earnings perspective can be measured as changes in the Net Interest Income (NII) or Net Interest Margin (NIM). There are many analytical techniques for measurement and management of Interest Rate Risk.

The Gap or Mismatch risk can be measured by calculating Gaps over different time intervals as at a given date. Gap analysis measures mismatches between rate sensitive liabilities and rate sensitive assets (including off-balance sheet positions). An asset or liability is normally classified as rate sensitive if:

i) within the time interval under consideration, there is a cash flow;

ii) the interest rate resets/reprices contractually during the interval;

iii) it is contractually pre-payable or withdrawable before the stated maturities;

iv) It is dependent on the changes in the Bank Rate

The interest rate gaps are identified in the following time buckets:

i) 1-28 days

ii) 29 days and upto 3 months

iii) Over 3 months and upto 6 months

iv) Over 6 months and upto 1 year

v) Over 1 year and upto 3 years

vi) Over 3 years and upto 5 years

vii) Over 5 years and upto 7 years

viii) Over 7 years and upto 10 years

ix) Over 10 years

x) Non-sensitive

The Gap is the difference between Rate Sensitive Assets (RSA) and Rate Sensitive Liabilities (RSL) for each time bucket. The positive Gap indicates that it has more RSAs than RSLs whereas the negative Gap indicates that it has more RSLs. The Gap reports indicate whether the institution is in a position to benefit from rising interest rates by having a positive Gap (RSA > RSL) or whether it is in a position to benefit from declining interest rates by a negative Gap (RSL > RSA). The Gap can, therefore, be used as a measure of interest rate sensitivity.

ALCO sets the prudential limits on interest rate gaps in various time buckets. Such prudential limits have a relationship with the Total Assets , Earning Assets or Equity. The Bank sets up the prudential limits on Earnings at Risk (EaR) and Net Interest Margin (NIM) based on their views on interest rate movements with the approval of the Board/ALCO.

The impact of embedded options (customers exercising their options for premature closure of term deposits, premature encashment of bonds and pre-payment of loans and advances) on the liquidity and interest rate risks profile of Bank and the magnitude of embedded option risk during the periods of volatility in market interest rates, is quite substantial.

Interest rate risk is a major financial risk faced by banks, arising from fluctuations in interest rates that can affect their earnings, asset values, and overall financial stability. Here are the key sources of interest rate risk in banks:

Sources of Interest Rate Risk

1. Repricing Risk – Occurs when the maturity or repricing dates of assets and liabilities differ, leading to changes in net interest income (NII) when rates change. If a bank funds a 10-year fixed-rate loan (asset) with a 1-year deposit (liability). If interest rates rise after one year, the bank must pay higher interest on deposits while earning the same fixed rate on the loan, squeezing its profit margin.

2. Yield Curve Risk – Arises from unexpected shifts in the yield curve (steepening, flattening, or inversion) which impacts the relative profitability of different maturities. If short-term rates rise faster than long-term rates, a bank relying on short-term borrowing to fund long-term loans may see margins compress.

3. Basis Risk – Occurs when the interest rates of different financial instruments (loans vs. deposits or floating-rate assets vs. liabilities indexed to different benchmarks) do not move in sync. A bank’s loans are tied to SOFR, but its deposits are linked to the Fed Funds Rate. If SOFR rises while the Fed rate stays flat, funding costs increase without a corresponding rise in loan income.

4. Embedded Option Risk – Results from customers exercising prepayment options on loans or early withdrawal options on deposits when interest rates change. When rates fall, borrowers may refinance mortgages (prepaying old loans), forcing the bank to reinvest at lower yields. Conversely, if rates rise, depositors may withdraw funds to seek higher returns elsewhere.

5. Regulatory and Capital Risk – Sudden changes in interest rates can affect capital adequacy ratios (CET1) by altering asset valuations or loan loss provisions. Rising rates may increase loan defaults, forcing the bank to set aside more capital, thereby reducing profitability.

6. Macroeconomic and Policy Risk – Central bank actions (Fed rate hikes) or inflation trends can indirectly impact bank operations. Aggressive monetary tightening may slow economic growth, reducing loan demand and increasing default risks.

7. Competitive Risk – Banks may face pressure to adjust deposit or loan rates due to competition, even if market rates haven’t changed. If rivals offer higher deposit rates to attract customers, a bank may have to raise its rates, squeezing margins.

Interest Rate Risk (IRR) Management Tools

Banks use various tools and techniques under Asset-Liability Management (ALM) to measure, monitor, and mitigate interest rate risk (IRR). These tools help ensure stability in Net Interest Income (NII) and the Economic Value of Equity (EVE). Below are the key IRR management tools:

1. Gap Analysis (Static & Dynamic)

Measures the difference (Gap) between rate-sensitive assets (RSAs) and rate-sensitive liabilities (RSLs) across different time buckets.
Positive Gap (RSA > RSL): Bank benefits from rising rates.
Negative Gap (RSL > RSA): Bank suffers if rates rise.

2. Duration Gap Analysis

Measures the sensitivity of a bank’s Economic Value of Equity (EVE) to interest rate changes by comparing the calculating the weighted average duration of assets and liabilities.
Duration Gap = Asset Duration − ( Market value Liabilities / Market value Assets× Duration of Liabilities)

Positive Duration Gap: If rates rise, EVE falls (assets lose more value than liabilities).
Negative Duration Gap: If rates fall, EVE declines (liabilities reprice slower than assets).

3. Basis Risk Analysis:

Tracks mismatches between different interest rate benchmarks (e.g., SOFR vs. Prime Rate).

4. Earnings-at-Risk (EaR) & Net Interest Income Simulation

Estimates the potential impact of rate changes on net interest income (NII) over a given horizon.

Projects NII under different rate scenarios (+100 bps, -50 bps) to assess potential earnings volatility.
Uses Monte Carlo simulations or deterministic models to forecast cash flows.
Helps banks set risk appetite limits (e.g., “NII should not fall by more than 5% in a +200 bps shock”).

5. Economic Value of Equity (EVE) Analysis

Estimates the present value of all future cash flows from assets minus liabilities under different rate scenarios.
A decline in EVE indicates long-term solvency risk.
Regulators require EVE stress testing.

6. Value-at-Risk (VaR) for Interest Rate Risk

Estimates the maximum potential loss in NII or EVE over a given confidence level (say, 99%) and time horizon.
Useful for trading portfolios with bonds, swaps, and derivatives.

7. Stress Testing & Scenario Analysis

Evaluates the impact of extreme rate movements (e.g., parallel shifts, yield curve twists) or macroeconomic shocks.
Regulatory stress tests (e.g., CCAR, ICAAP) require banks to model severe scenarios.

8. Hedging Instruments (Derivatives)

Banks use derivatives to neutralize mismatches between assets and liabilities:

Interest Rate Swaps (IRS): Convert fixed-rate loans to floating (or vice versa).
Futures & Forwards: Lock in future borrowing and lending rates.
Options (Caps, Floors, Collars): Protect against adverse rate moves while retaining upside.
Swaptions: Options to enter into an IRS later.

9. Dynamic Balance Sheet Management

Adjusts loan pricing, deposit rates, and funding mix proactively based on rate outlook.
Example: If rates are expected to rise, a bank may:

Increase long-term fixed-rate loans (to lock in higher yields).

Issue long-term debt (to avoid future higher funding costs).

Offer floating-rate deposits (to reduce repricing risk).

10. Behavioural Modeling

Accounts for non-contractual behaviours like:

Prepayment risk (mortgages refinancing when rates fall).

Deposit stickiness (some deposits don’t reprice immediately with market rates).

Uses historical data to model customer behaviour.

11. Funds Transfer Pricing (FTP)

Allocates a transfer rate to business units based on the bank’s cost of funds, ensuring proper IRR pricing.
Example: A corporate loan priced at FTP + spread ensures the lending unit accounts for funding risk.

ALM Terminologies

ASSETS

Cash & Cash Equivalents – Incudes currency held by the bank and assets that are readily convertible to cash.

Marketable Securities – These are Government Bond, T Bills, Corporate Bonds and other securities held for investment purpose or for trading.

Loans and Advances -The core of most bank’s business models, these are money lent to individuals, businesses and other entities.

Fixed Assets – Physical assets like land, buildings, equipment, furniture and so on.

Other Assets – Intangible assets and deferred tax assets, receivables, intellectual property and goodwill and inventory.

LIABILITIES

Deposits – The largest liability for most banks, representing money held by the bank that belong to depositors.

Borrowed Funds – Includes money that the bank borrows from other financial institutions or through issuance of debt securities.

Other Liabilities – Accrued Expenses, Tax Liabilities, Payables.

Equity Capital – Includes bank’s capital and retained earnings.

Maturity Profiles – Analysing the maturity dates of assets and liabilities helps in understanding the liquidity risks and interest rate risks. Assets and Liabilities are categorised into buckets , based on their maturity.

Rate Sensitivity – This involves examining the interest rate sensitivity of both assets and liabilities.

Contingency Funding Plan – All Banks have a well-defined CFP outlining strategies to address potential liquidity shortfalls during stressed conditions. This plan identifies alternative funding sources and mechanism for assessing emergency liquidity facilities during crisis.

Cash Flow Analysis – Understanding the timing and predictability of cash flows from both assets and liabilities is crucial, assessing the inflows and outflows over different time horizons.

Scenario Analysis and Stress Testing – Banks use these tools to assess how certain hypothetical scenarios like market crashes or economic downturns or sudden interest rate changes or external factors like war, oil price hike or FED action would affect their balance sheet.

Capital Adequacy Requirement – Objective is to ensure that Banks have adequate capital to withstand periods of stress. Capital Buffers are an essential aspect of mitigating risk associated with ALM.

Liquidity Coverage Ratio – LCR ensures that Banks have enough high quality liquid assets to cover total net cash outflows over 30 Calendar days.

Net Stable Funding Ratio – NSFR requires banks to maintain a stable funding profile in relation to the composition of their assets and off-balance sheet activities.

Leverage Ratio – This is a supplementary measure to the risk-based capital requirements intended to restrain the build-up of leverage in the banking sector.

Funds Transfer Pricing (FTP)

Funds Transfer Pricing is a critical framework used by banks to allocate costs and revenues between different business units (lending, deposits, treasury) to measure profitability, manage interest rate risks and optimize capital allocation and support asset-liability management and regulatory compliance. The funds transfer pricing system is the fundamental ALM tool in a bank. It creates the ability to immunize business units from risk and provides the basis for economic and product transparency.

The process of FTP is designed to identify interest margins and remove interest rate and funding and liquidity risk. From Business Unit perspective, it effectively locks in the margin on loans and deposits by assigning a transfer rate that reflects the repricing and cash flow profile of each balance sheet item – it is applied to both assets and liabilities. From the ALM unit’s perspective, it isolates business performance into discrete portfolios that can be assigned individualised metrics and facilitates the centralisation and management of interest rate mismatches. It effectively allocates responsibilities between the organizational business units and the treasury department.

FTP mechanism is also used as a tool to assist with management of the balance sheet structure with FTP rates adjusted to either encourage or discourage product and customer flows. The associated analytical process leads to greater understanding of a bank’s competitive advantage, assisting with asset allocation and protection of the franchise.

FTP rates are structured to include both interest rate and funding liquidity risks with the derived transfer yield curve constructed to include appropriate premiums. Such premiums capture all elements associated with the banks funding cost. These include the cost of holding liquidity reserves; optionality costs, where pre-payment rights exist; term funding program costs and items such as basis risk.

Objectives of FTP

Profitability Attribution: Isolates NII by separating lending spreads from funding costs.
Liquidity Risk Pricing: Charges business units for liquidity risk (e.g., long-term funding of short-term loans).
Regulatory Compliance: Aligns with Basel III liquidity rules (LCR, NSFR).
Managing Internal Capital Adequacy Assessment Process (ICAAP).
Supporting risk-adjusted performance measures like RAROC (Risk-Adjusted Return on Capital).
Incentivizing Optimal Behaviour: Encourages branches to gather stable deposits and lend efficiently.
Product Pricing: Ensure pricing reflects actual cost of funds and risk.
Interest Rate Risk Management: Align business with ALM and duration risk.
Performance Evaluation: Enable fair and risk-adjusted performance comparison of units.
IFRS 9: FTP affects expected credit loss (ECL) models.
US GAAP: Impacts NII forecasting & hedge accounting.

FTP Methodologies

FTP methodologies determine how banks internally allocate funding costs and revenues across business units. The choice of methodology impacts profitability measurement, risk management, and strategic decision-making. Different FTP methodologies are given below:

1. Single-Rate FTP Methodology

Applies one uniform transfer rate to all assets and liabilities.
Typically based on the bank’s weighted average cost of funds (WACF)
Simple but doesn’t account for maturity mismatches or risk profile.

2. Multi-Rate FTP Methodology

Different FTP rates based on product type, maturity, and liquidity.

Deposits (demand, term, wholesale)

Loans (mortgages, corporates, floating-rate)

Trading book instruments

More accurate for risk-based pricing but still doesn’t fully account for term structure risk.

Implementation

python

# Simplified multi-rate FTP example

ftp_rates = {

‘demand_deposits’: 1.5%,

‘1yr_term_deposits’: 3.2%,

‘5yr_fixed_loans’: 4.8%,

‘prime_floating_loans’: SOFR + 2.5%

}

3. Matched-Maturity FTP Methodology

Assigns FTP rates based on precise funding tenor and currency of the asset or liability.
Best for managing interest rate risk.

Uses yield curve (SOFR, Treasury) + liquidity premium.
Most accurate reflection of true economic cost.
Aligns with Basel III liquidity requirements.
Enables proper transfer of interest rate risk to Treasury.
Uses multiple curves (e.g., SOFR for USD, €STR for EUR).
Behavioural Adjustments for Non-maturity deposits (NMDs) and Option-adjusted spread for Mortgage prepayments.

Enhances accuracy in performance attribution and pricing.
Widely used in large banks for loans, deposits, and trading books.

Term Structure Implementation

Tenor	Treasury Rate	Bank Spread	FTP Rate
O/N	5.50%	+0.40%	5.90%
1Y	4.70%	+0.50%	5.20%
5Y	4.20%	+1.00%	5.20%

4. Pooled FTP

Groups similar products into pools with average funding costs.

Short-term liquidity pool (<1Y)

Medium-term funding pool (1-5Y)

Long-term capital pool (>5Y)

Less precise but easier to implement.
Regional banks with standardized products.
When exact maturity data is unavailable.

Example Pool Allocation

Pool	Average Duration	FTP Rate
ST	6 months	5.6%
MT	3 years	5.1%
LT	7 years	4.9%

5. Marginal Cost Pricing

Uses next marginal dollar cost for pricing decisions.
Last Dollar FTP. Cost of most recent funding.
Incremental FTP. Cost of next funding tranche.

Used for strategic pricing for large corporate loans.
Used in liquidity stress scenarios.

6. Market-Based FTP

Uses external market rates (e.g., SOFR, government bonds) to determine FTP.
Ensures transparency and links pricing to real market conditions.
Suitable for banks operating in volatile or highly competitive markets.

7. Liquidity Premium Method

FTP Rate = Risk-Free Rate (SOFR) + Liquidity Spread + Contingency Cost

Adds a liquidity premium to the base FTP to reflect funding liquidity risk.
Stress-Based FTP, charges extra for products needing HQLA buffers.
Adjusts for prepayment risk (mortgages) and early withdrawal risk (CDs).
Specifically relevant under Basel III’s Liquidity Coverage Ratio and Net Stable Funding Ratio.
Used to manage long-term funding and stress scenarios.

8. Spread-Based Method

Uses internal spreads added to a benchmark rate to set FTP.
Helps align with strategic goals like incentivizing stable deposits or penalizing risky assets.

9. Dynamic FTP

Continuously adjusts FTP based on changing market and internal conditions.
Uses advanced systems and real-time data.
Global banks using FTP for strategic planning and competitive pricing.

Challenges in FTP Implementation

Data Quality – Requires accurate cash flow and pricing data.
Model Complexity – Multi-curve FTP (considering LIBOR/SOFR, credit spreads).
Behavioural Adjustments – Prepayment risks on loans, sticky deposits.
Regulatory Scrutiny – Must align with Basel III, IFRS 9, and accounting standards.

FTP Implementation

Step 1: Construct the Bank’s Funding Curve

Derive a term structure of FTP rates from:
- Wholesale funding costs (advances, bonds).
- Deposit betas (how fast deposits reprice to market rates).
- Regulatory liquidity buffers (LCR, NSFR-adjusted).

Step 2: Assign Behavioural Maturity for Non-Maturity Deposits (NMDs)

“Core Deposits” (stable) → Assigned longer FTP tenor (e.g., 5Y).
“Volatile Deposits” (transactional) → Assigned short FTP tenor (e.g., 1M).

Step 3: Apply Liquidity Premiums

Product Type	Liquidity Premium	Reasoning
30-Year Mortgage	+0.75%	Long-term funding risk
Demand Deposits	-0.20%	Low liquidity risk (stable)
Brokered CDs	+0.50%	Volatile funding source

Step 4: Calculate FTP Earnings

Loan FTP Profit = Loan Yield – FTP Rate
Deposit FTP Profit = FTP Rate – Deposit Interest Paid

FTP Reporting & Performance Analysis

Ratio	Formula	Purpose
Net FTP Margin	(Loan FTP Income – Deposit FTP Cost) / Average Assets	Measures core profitability
Liquidity Cost Ratio	Liquidity Premiums / Total Assets	Tracks funding risk efficiency
FTP Contribution by Branch	Sum of FTP credits/charges per unit	Incentivizes optimal deposit gathering

Emerging Trends in FTP

1. FTP + Climate Risk – Green loans are getting preferential FTP rates. Banks are adopting Carbon-adjusted funding costs.

2. Blockchain Applications – Smart contracts for real-time FTP adjustments are being adopted.

3. AI-Driven FTP – Machine learning for behavioural modeling and Dynamic rate optimization will be the future trend.

4. GenAI for FTP Documentation will be used for auto-generating Basel III audit trails.

5. Quantum Computing will be used in Banks to optimizing bank-wide FTP in real time.

6. DeFi Integration will accelerate. FTP for crypto deposits and loans in future will be incorporated.

AI-Driven FTP Systems in Banking

AI-powered FTP systems enhance traditional Funds Transfer Pricing by leveraging machine learning, big data analytics and real-time processing to optimize pricing, liquidity risk management and profitability measurement. These systems address key limitations of legacy FTP frameworks, such as static curves, behavioural assumptions and manual adjustments.

How AI Transforms Traditional FTP

Traditional FTP	AI-Driven FTP
Static yield curves	Dynamic, real-time curves (updated via market feeds & ML forecasts)
Fixed deposit decay rates	Behavioural ML models predicting deposit stability
Manual liquidity adjustments	Automated liquidity risk pricing (NSFR/LCR-aware)
Rule-based pricing	Predictive pricing engines (optimizing customer-level FTP)

Core AI Technologies Used

Supervised Learning → For Deposit behaviour modeling.
Reinforcement Learning (RL) → For Dynamic FTP rate optimization.
NLP → Extracting insights from regulatory texts (e.g., Basel IV).
Graph Analytics → Mapping interbank funding networks.

AI Applications in FTP

(A) Dynamic FTP Curve Generation

Data Inputs: SOFR/SWAP curves, bond spreads, wholesale funding costs.
AI Technique: Time-series forecasting (LSTMs, Prophet) to predict rate shifts.
Output: Real-time FTP curves adjusted for Term structure changes and Liquidity crunch signals (Regulatory policy shifts)

(B) Behavioural Modeling for Non-Maturity Deposits (NMDs)

Problem: Legacy FTP assumes fixed decay rates, for example, 5% annual runoff.
AI Solution:

Train ML models on historical deposit data (balances, customer demographics, rate sensitivity).

Predict stability using:

Survival analysis (for deposit longevity) and

Clustering (segmenting sticky vs. volatile deposits)

(C) Liquidity Risk Pricing with Reinforcement Learning

Problem: Static liquidity premiums misprice contingent risks like undrawn credit lines.
AI Solution:

Reinforcement Learning agents simulate stress scenarios like 2008-style withdrawals.

Optimize FTP premiums to maintain LCR/NSFR compliance.

Output:

Dynamic liquidity spreads

(D) Customer-Level FTP Optimization

Problem: Uniform FTP rates ignore customer-specific risks.
AI Solution:

Predictive analytics score customers on:

Deposit stickiness

Loan prepayment risk

Output: Granular FTP rates

Case Study: AI-FTP at a Top 10 US Bank

Challenge:

Legacy FTP mispriced $50B in commercial deposits, leading to $120M/year in lost NII.

AI Fix:

Trained gradient-boosted trees (XGBoost) on 5Y deposit data → identified 20% of “sticky” deposits misclassified as volatile.
RL-adjusted liquidity premiums reduced LCR shortfalls by 15%.
Result: +$80M annual NII boost from repriced deposits.

Implementation of AI Driven FTP

Phase 1: Data Foundation

Integrate: Core banking, market data, Basel III liquidity reports.
Build: Feature store for ML (e.g., deposit runoff histories).

Phase 2: Model Development

Model Type	Purpose	Technique
Deposit Stability	Behavioural FTP rates	XGBoost + Survival Analysis
Dynamic Curves	Real-time FTP rates	LSTM + Kalman Filters
Liquidity RL Agent	NSFR-aware pricing	Q-Learning

Phase 3: Deployment

API layer feeds AI-FTP rates to:

Loan origination systems

ALM hedging desks

Regulatory reports (LCR/NSFR)

Balance Sheet Management Tools

1. Discounted Cash Flow Model– applies the DCF principle to estimate and match expected cash inflows from assets and expected cash outflows from liabilities by discounting these cash flows to their present value,

2. Capital Asset Pricing Model -CAPM is a financial model used to estimate the expected return on an investment, based on its systematic risk relative to the overall market.

3. Black-Scholes Model – The Black-Scholes Model (BSM) is a mathematical model used to calculate the theoretical price of European-style options.

4. Bouaziz-Briys & Crouhy Model – The BBC Model is a modification of the traditional Black-Scholes option pricing framework, specifically tailored to address limitations of constant interest rates and volatility. It is often applied in the context of bond options, credit risk and term structure modeling.

5. Hull-White Model – The Hull-White Model is a widely used one-factor interest rate model that extends the Vasicek model to fit the initial term structure of interest rates. It is widely applied in pricing interest rate derivatives, risk management, and bond valuation.

6. Ikeda & Kunitomo Model – The Ikeda–Kunitomo model is an advanced trinomial tree framework developed to improve the efficiency and accuracy of pricing options, especially American-style options and path-dependent derivatives.

7. Reiner & Rubinstein Model – Reiner & Rubinstein Model is used in the pricing of barrier options, a type of path-dependent exotic option. Their most notable contribution is the closed-form pricing formulas for barrier options under the Black-Scholes framework.

8. Turnbull & Wakeman Model – The TW Model is an analytical approximation used to price Asian options. It provides a computationally efficient alternative to Monte Carlo simulations, which are often slow for Asian option pricing.

9. Margrabe Model – It is a closed-form solution for pricing exchange options – options that allow the holder to exchange one asset for another at expiration. It extends the Black-Scholes model to two risky assets and is widely used in real options, FX derivatives, and commodity trading.

10. Trinomial Trees -The standard trinomial tree (STT) is a binomial-like framework used for pricing equity options that extends the traditional binomial tree model by incorporating three potential price movements at each node: an upward movement, a downward movement, and a horizontal movement (no change in price).

11. Secured Overnight Financing Rate Market Model – It is a term structure framework designed to model the stochastic evolution of SOFR and its derivatives, such as SOFR futures, swaps, caps/floors, and swaptions. It extends traditional LIBOR Market Models (LMM) to the risk-free rate (RFR) regime, accounting for SOFR’s unique features.

12. Sensitivity gap and Tenor gap

13. DV01

14. Duration and Modified Duration

15. Convexity

16. Greeks

17. CE, EE, PFE and EL

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