Course syllabus
Finance and AI: the course outline
This is the outline for the course. It states what the course covers, what you should be able to do at the end of it, how far it goes into each method, what it deliberately leaves out, and what to read. The slides for each day are on the slides page.
Course description
Finance comes first. The course does not open with a model or a library. It opens with a balance sheet and a week in March, and asks what has to be true for a loss to reach lending, and for a payment to fail while the assets are still sound. Only once those mechanisms are on the table does it ask what a machine could usefully add.
What it adds is deliberately narrow. There is one prediction problem and one transparent probability rule, specified completely before it is fitted and tested at genuinely later dates. There is one evidence workflow, built against a filing read by hand so that the automated version has a benchmark to match. The recurring discipline is that a prediction is not a diagnosis, a fluent answer is not a sourced answer, and neither a model nor a language model holds the authority to act.
Course aims
The course follows one argument across four days: a financial mechanism is established, then measured, then used to predict, then required to produce evidence that a responsible person can check. Days 1 and 2 are finance. They ask when an asset loss becomes a credit contraction, and how an institution with valuable assets can still fail to pay. Days 3 and 4 take those mechanisms and ask what they license: one dated probability that ranks cases for review, and one evidence packet in which every claim carries its source and its qualification.
The four days form one argument, in the order mechanism, measurement, prediction, evidence, accountable judgement. The course closes on the sentence it has spent four days earning: “AI is most useful when finance defines the question and verification constrains the answer.”
Learning outcomes
On successful completion, you will be able to:
- write a bank balance-sheet identity, and compute how far an asset loss travels into equity through leverage and duration;
- distinguish a capital shortfall from a liquidity shortfall, and match each to the instrument that repairs it and the party that bears the loss;
- derive a debt limit from an incentive constraint, and explain why lower net worth can reduce equilibrium lending rather than only a ratio;
- derive why demandable claims provide liquidity insurance and create a run equilibrium, and compute the withdrawal threshold;
- separate a common shock from contagion, and name the transmission margin any contagion claim requires;
- specify a prediction problem completely, including population, information cutoff, target, measurements, evaluation and permitted use, before any model is fitted;
- fit and read one transparent probability rule, and test it in financial time without moving future information backwards;
- turn a frozen risk signal into a dated evidence packet in which every claim carries its support, its calculation and its qualification;
- state what a model or a language model may and may not authorise, and who retains responsibility for a consequential decision;
Teaching method
Four three-hour lectures, one each day. Each day opens on a real institution and a puzzle it poses, develops the mechanism that resolves it, and closes on a conditional answer rather than a slogan. Every deck carries a technical appendix with the derivations in full, hyperlinked from the lecture frames, so the argument can be followed at two levels of detail.
Numerical examples are worked on the slides. Where a number comes from an institution it is sourced; where it is generated from the equations on the slide it is labelled as an illustration.
Course sessions
| Day | Topic |
|---|---|
| Day 1 | When does an asset loss become a credit contraction? |
| Day 2 | Liquidity, runs, and systemic fragility |
| Day 3 | From financial mechanisms to one defensible risk signal |
| Day 4 | From a frozen risk signal to verifiable financial evidence |
Session overview
When does an asset loss become a credit contraction?
The day opens on a puzzle: Bear Stearns reported $395bn of assets against $384bn of liabilities, so a loss of about three per cent would exhaust its equity. Leverage turns a small asset mark into a large equity loss, and duration decides how large that mark is. But accounting alone does not determine lending. The lecture builds an agency model in which the intermediary must stay credible to its own funders, derives a debt limit from an incentive constraint, and then closes the model with a funding-supply schedule so that lower net worth reduces equilibrium credit rather than merely reducing a ratio. It ends on capital repair: shrink, issue, or bail in, and which diagnosis each instrument actually fits.
Running case: Bear Stearns, 2007 to 2008. Sections: Losses and the equity buffer; Why intermediary net worth matters; The incentive constraint and debt limit; Equilibrium amplification; Capital repair and policy.
Liquidity, runs, and systemic fragility
Day 1 asked whether promised claims are supportable over the life of the assets. Day 2 changes the clock and asks whether payments due now can be met without destroying long-run value. Silicon Valley Bank received $54bn of outbound wire requests in a single day. The lecture separates solvency from liquidity as two different inequalities, derives the Diamond and Dybvig run equilibrium and its withdrawal threshold, then adds what the classical model leaves out: duration losses, concentrated uninsured funding, secured borrowing and execution speed. It closes on contagion channels and on which intervention repairs which margin, and who bears the loss.
Running case: Silicon Valley Bank, March 2023. Sections: Liquidity and maturity transformation; Liquidity insurance and the run equilibrium; Modern run technology; Contagion and systemic fragility; Liquidity policy and loss allocation.
From financial mechanisms to one defensible risk signal
Bank surveillance uses quantitative signals to direct scarce analyst attention. The question is whether dated public information can help allocate that attention before a material funding event occurs. The lecture fixes the empirical contract first: the population, the public-information cutoff, the target, the measurements, the evaluation and the permitted use, all before any weight is learned. It then builds one transparent probability rule from four financial measurements, derives its loss and its single signed error, and tests it in financial time against two serious baselines. The authorised output is a review priority, not a diagnosis.
Running case: A point-in-time panel of US bank holding companies. Sections: The prediction decision; One dated bank-quarter; One probability rule; Future performance; Evidence review.
From a frozen risk signal to verifiable financial evidence
A probability contains an ordering, not a financial explanation. Day 4 inherits the frozen alert and asks what dated evidence supports, contradicts or qualifies the hypotheses behind it. The lecture reads one real filing by hand first, reproducing a scope-qualified uninsured-deposit ratio and reconciling carrying value against fair value, so that a manual benchmark exists before anything is automated. Only then does it introduce retrieval, narrow calculation tools, the SEC filing API and a bounded controller. Generation begins after the support set is assembled, and abstention is the correct answer when decisive evidence is absent.
Running case: Silicon Valley Bank's 2022 Form 10-K. Sections: Question and evidence; Worked financial record; Grounded language assistance; APIs and bounded tools; Evaluation and authority.
How far the course goes
Each day works one mechanism to the end rather than surveying several. This table separates what you derive or compute yourself from what you learn to read.
| Day | You derive or compute | You learn to read |
|---|---|---|
| Day 1 | Compute leverage, a duration mark, capital-ratio repair, a debt limit and an equilibrium response | Read a 10-K balance sheet and a stress-test result |
| Day 2 | Solve the planner allocation, derive a withdrawal threshold, compute repo capacity and contagion losses | Read a supervisory review and a resolution announcement |
| Day 3 | Build a dated bank-quarter row, fit a logistic probability rule, compute log loss and one gradient step, and score capture and precision at a fixed review capacity | Read model-risk validation guidance |
| Day 4 | Reproduce a scope-qualified ratio from a filing, build a retrieval and counterevidence plan, and score an assisted evidence packet claim by claim | Trace an API contract and a bounded controller |
What the course leaves out
Days 1 and 2. One cumulative argument, not a survey. There is no general history of banking crises, no catalogue of every 2023 failure, no complete global-games derivation, no full network-clearing model, and no legal treatment of resolution regimes.
Days 3 and 4. One structured-data application and one research workflow, developed from beginning to end. There is no survey of model families, no neural network architectures or backpropagation, no reinforcement learning or high-frequency trading, and no generic prompt-engineering taxonomy.
Throughout. The lectures contain no lab instructions. Numerical examples are generated from the equations on the slides and are labelled as illustrations unless an institutional source is named.
Sources and reading
The lectures cite their sources on the slide that uses them. The items below are the principal ones; the public documents link directly.
- Diamond, Douglas W. and Philip H. Dybvig, Bank Runs, Deposit Insurance, and Liquidity. Journal of Political Economy 91(3), 1983. The run model Day 2 derives.
- Holmström, Bengt and Jean Tirole, Financial Intermediation, Loanable Funds, and the Real Sector. Quarterly Journal of Economics 112(3), 1997. The agency structure behind the Day 1 debt limit.
- Bernanke, Ben, Mark Gertler and Simon Gilchrist, The Financial Accelerator in a Quantitative Business Cycle Framework. Handbook of Macroeconomics, 1999. Amplification from net worth to activity.
- Baron, Matthew, Emil Verner and Wei Xiong, Banking Crises Without Panics. Quarterly Journal of Economics 136(1), 2021. Bank-equity declines predict contractions across 46 countries, 1870 to 2016.
- Board of Governors of the Federal Reserve System, Review of the Federal Reserve's Supervision and Regulation of Silicon Valley Bank. April 2023. The supervisory record behind the Day 2 case. Read online
- Federal Deposit Insurance Corporation, Dissecting Depositor Flight: An Analysis of the Spring 2023 Bank Failures. May 2026. The deposit-flow evidence Day 2 uses. Read online
- Federal Reserve Board, Bank Holding Company Supervision Manual, section 4090.0. Surveillance and Risk Assessment. The decision context Day 3 works inside. Read online
- Gaul, Lewis, Jonathan Jones and Pinar Uysal, Forecasting High-Risk Composite CAMELS Ratings. Federal Reserve International Finance Discussion Papers 1252, 2019. Why one transparent logit is enough for the Day 3 task.
- SVB Financial Group, Annual Report on Form 10-K for the year ended 31 December 2022. Filed 24 February 2023. The filing Day 4 reads by hand. Read online
- National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework: Generative AI Profile, NIST AI 600-1. July 2024. The governance vocabulary behind the Day 4 controls.
