Most practitioners who end up in ALM arrive sideways. You came from the trading desk, or the investment portfolio, or credit risk, or the Fed exam team. Nobody handed you a manual that said: here is the job, here is what you are actually trying to do, here is how you will be judged when rates move 300 basis points in eighteen months. This module is that manual.
Before any models, any scenarios, any regulatory frameworks — we establish what ALM is, why it exists, what the job is trying to accomplish, and how it connects to every major decision a bank makes. If you are already in the role, this gives you language for things you know intuitively. If you are new to it, this is the orientation that most training programs skip entirely.
We ground everything in what actually happened at real banks between 2008 and 2025 — because the history of US banking over that period is essentially a master class in what happens when ALM goes right, when it goes wrong, and when it gets caught flat-footed by a rate environment nobody fully anticipated.
The textbook definition: ALM is the discipline of managing the mismatch between a bank's assets and its liabilities in terms of maturity, repricing, and cash flow, to optimize net interest income while keeping interest rate risk, liquidity risk, and capital within acceptable limits. Technically correct. Also the kind of sentence that tells you almost nothing about what the job feels like at 8am on a Monday when the Fed just cut rates 50 basis points and your ALCO chair wants to know what it means for the NII forecast.
Here is a better framing: the ALM manager's job is to make sure the bank earns a consistent, predictable spread across rate environments — without taking on risks that could threaten the franchise. Everything else — the models, the scenarios, the hedges, the regulatory filings, the ALCO presentations — is in service of that one objective.
The word consistent matters. Banks are not hedge funds. A bank that earns a spectacular NIM in rising rate environments but gets crushed when rates fall has not done good ALM — it has made an unmanaged rate bet that happened to pay off. The best ALM functions deliver stable, predictable earnings through rate cycles while positioning the bank to benefit modestly from favorable environments. That balance is the core judgment call of the job.
Banks do something economically unusual: they borrow short and lend long. A depositor can withdraw their checking account tomorrow. The bank lent that money out as a 30-year mortgage. The bank is earning a spread today — but it has created a maturity mismatch that becomes dangerous when interest rates move unexpectedly.
This is not theoretical. Between June 2022 and July 2023, the Federal Reserve raised the federal funds rate from effectively zero to 5.25–5.50% — the fastest tightening cycle in forty years. Banks that had loaded up on fixed-rate securities at 1.5–2% yields in 2020 and 2021 found themselves holding assets earning well below the cost of their deposits. The spread compressed. In some cases it inverted.
The most dramatic example was Silicon Valley Bank. By end of 2022, SVB held approximately $120 billion in long-duration fixed-rate securities — primarily 10-year-plus agency MBS — funded largely by uninsured corporate deposits. When rates rose, the securities portfolio accumulated over $15 billion in unrealized losses. When depositors began withdrawing in March 2023, SVB had to sell securities at a loss to fund outflows. The resulting $1.8 billion realized loss triggered a bank run that withdrew $42 billion in a single day. SVB failed within 48 hours.
SVB is the extreme case. But the dynamic — assets that reprice slowly, liabilities that reprice quickly, a mismatch that becomes costly when rates move fast — played out in milder form across virtually every bank in America between 2022 and 2024. The banks that managed it best had maintained appropriate duration limits, hedged exposed positions, and avoided loading up on duration at historically low yields. The ones that managed it worst had concentrated duration risk, thin hedge books, and deposit bases more prone to repricing than their models had assumed.
On any given week, the ALM manager is doing some combination of these things.
The NII simulation model is the primary tool of ALM. It takes the current balance sheet, applies assumptions about how assets and liabilities will reprice under different rate scenarios, and produces an estimate of net interest income over the next 12–24 months under each scenario. The output is typically a sensitivity table: NII in a base case following the forward curve, NII in an up-200bp shock, NII in a down-100bp shock, and several non-parallel scenarios.
The ALM manager does not just run the model — they own the assumptions behind it. What is the assumed deposit beta for commercial checking accounts? What prepayment speed applies to the mortgage portfolio? How quickly do new loans come onto the books in the dynamic scenario? These assumptions are where most of the analytical judgment in ALM lives, and they are the first thing a regulator or model validator will question.
The Asset-Liability Committee typically meets monthly. The ALM manager builds the presentation: rate environment update, NII sensitivity results, deposit trends, liquidity metrics, hedge portfolio summary, investment portfolio performance, capital ratios, limit monitoring. This is where ALM connects to senior management and ultimately the Board.
After JPMorgan's ALCO discussions in late 2021, CFO Jeremy Barnum began publicly discussing the firm's balance sheet positioning on earnings calls in 2022 — explaining why JPMorgan had not aggressively extended duration despite pressure to put excess deposits to work. That kind of clear, public communication about positioning flows directly from ALCO. It is one of the most visible outputs of a well-functioning ALM governance structure.
For most banks, the investment securities portfolio is the second-largest asset category after loans, typically 20–30% of total assets. The ALM manager or a closely related investment portfolio function decides what to buy, when to buy it, and how it interacts with the overall interest rate risk position.
Between 2020 and 2022, Bank of America added approximately $350 billion to its securities portfolio, buying heavily in the 10-year-plus maturity range. When rates rose in 2022, that portfolio accumulated over $100 billion in unrealized losses — the largest AOCI hit in the US banking industry. BofA's management team spent the next two years on earnings calls explaining the decision to hold those securities to maturity rather than restructure, and the ongoing NIM impact of carrying large quantities of below-market-rate assets through 2023, 2024, and into 2025. The securities were only gradually replaced as they matured.
Many large banks use interest rate derivatives — primarily pay-fixed interest rate swaps — to modify the repricing profile of the balance sheet without changing the underlying loans or deposits. Wells Fargo discloses its hedge portfolio in detail each quarter, showing how swaps modify the bank's natural NII sensitivity. Reading those footnotes carefully is one of the most educational exercises an ALM practitioner can do — it shows exactly what problem the hedges are solving and what residual risk remains.
The Asset-Liability Committee includes the CFO (often chair), CRO, Treasurer, heads of Retail and Commercial Banking, the Chief Investment Officer, sometimes the CEO. It reports to the Board Risk Committee. ALCO meets monthly at most banks; in stress environments it meets more frequently.
The most consequential ALCO decisions are the occasional strategic calls: should we extend asset duration to pick up yield? Should we add pay-fixed swaps to reduce asset sensitivity? Should we reclassify AFS securities to HTM to protect capital ratios from further AOCI deterioration? These decisions involve real trade-offs between near-term earnings, long-term positioning, regulatory optics, and franchise risk. The ALM manager's job is to make sure ALCO has the best possible information to make those calls — and to have a well-reasoned view.
JPMorgan entered 2022 in a deliberately cautious duration position. Unlike peers, they had not aggressively extended the maturity of the investment portfolio in 2020 and 2021 despite having hundreds of billions in excess deposits. CFO Jeremy Barnum acknowledged on multiple earnings calls that the bank was forgoing near-term NII by not buying long-duration assets at 1.5–2% yields. When the Fed started hiking in March 2022, JPMorgan's relatively short-duration portfolio repriced upward faster than peers. The 2022 AOCI hit was real but manageable — roughly $15 billion. NII ex-Markets grew from $44 billion in 2021 to $65 billion in 2023. The ALCO call to accept lower NII in 2021 in exchange for better positioning proved to be correct.
Bank of America took a different approach. Between 2020 and 2022 it added approximately $350 billion to its investment portfolio at yields of 1.5–2.5%. By end of 2022 the AFS portion had accumulated over $100 billion in unrealized losses. The HTM portfolio carried an additional $100 billion-plus in unrecognized losses. NIM recovered more slowly than JPMorgan's because the below-market-rate securities were still on the books, maturing gradually. BofA's NIM expansion story through 2024 was essentially a story of waiting for the bond portfolio to age — not a failure of management, but a multi-year consequence of decisions made in the zero-rate environment.
The Fed raised from 1.00% to 5.25% in 25bp increments at 17 consecutive meetings. Banks had over a year of forward notice. Deposit betas averaged 30–40% for savings accounts. NIM initially expanded then compressed in 2006 as deposit betas caught up and the curve flattened. This cycle set baseline beta estimates that most models relied on for the next fifteen years — and those estimates proved far too low for what came next.
After seven years at zero, deposit betas were extraordinarily low early in this cycle — below 5% at many banks for the first 100bp of hikes. With $2 trillion+ in excess reserves, there was no competitive pressure to raise rates. NIM expanded materially. The lesson: after prolonged zero-rate periods, deposit pricing power is high. Betas stay low until the excess liquidity is gone.
From 0.25% to 5.50% in 16 months. Deposit betas eventually reached 50–70% at large banks — far faster than models calibrated on 2015–2018 had assumed. Huntington Bancshares entered 2022 expecting cumulative betas in the mid-to-high 20s; by mid-2023 they were tracking above 40% for interest-bearing deposits. Digital banking and abundant MMF alternatives changed the competitive dynamic in ways most behavioral models had not captured. The lesson: always stress-test deposit assumptions against the most aggressive historical cycle, not just the most recent one.
Every technical concept in the remaining modules is a tool in service of the objective described above. Gap analysis measures the repricing mismatch. NII simulation quantifies what that mismatch costs or earns across scenarios. Deposit modeling estimates how liabilities will behave. Hedging reduces exposures the ALCO decides are too large. FTP prices risks into business line decisions. Liquidity management ensures there is always enough cash regardless of what markets do.
Each track builds the toolkit. This module establishes why the toolkit exists and what it is building toward.