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Scope: Artificial Intelligence, Machine Learning, and Databases as relevant to Statistical Machine Learning and model calibration for retail banking ALM simulation. Includes data sources such as Market data, Econometric data, Accrual portfolio data, balance sheet data, external sources/packages/tools.

People: DeWitt,   Stonebreaker,  Andrew Ng, Norvig,  Andrew Lo, …

Organizations: FDIC, Sifma,  US Fed CCAR,, Apache Spark, …

Courses: Stanford Machine Learning, Stanford CS221: Artificial Intelligence: Principles and Techniques,  …

Open Problems Jan 2018:

  1. Selection of market, econometric, and historical account data for cashflow model calibration.
  2. Calibrating a credit card cashflow model – single account vs. aggregate.
  3. Calibrating a deposit cash flow  model – single account vs. aggregate.
  4. Quantify the expected error going from aggregate to single security.

Bib Evaluation Stack:


Bibliography v1.0:

The bank capital allocation process transforms an initial bank balance sheet, composed of a set of ALM securities that retain and produce cashflows, to a new target bank balance sheet. These ALM securities might be as simple as U.S. Treasury bonds, bank deposits, or credit card accounts. The characteristic cashflow quantities, timing, and duration depend on the securities, market data levels, and the behavior of the retail bank customer holding the securities. For example, a UST floater costs par/market value upfront, returns par at a predetermined maturity date, and periodically (semiannually) pays a contractually dictated coupon whose size depends on market data levels. The deposit may incrementally accept cash deposits and withdrawals starting at the inception of the deposit account. The deposit account may not have a contractual maturity, but as long as the account is active the bank must contractually pay interest on the funds held in the deposit account. Moreover U.S. deposit cash is typically protected from default by FDIC insurance up to a predetermined level (250K USD). The credit card account represents a credit line offered to a customer from the inception of the account. The credit card cash flows are the drawdowns against the line of credit, and the customer pay down against previous drawdowns. The draw down amount of the credit line may be periodically adjusted to account for the interest accrued to the bank on the previous credit line drawdowns. There may be no specific maturity date for the credit card account. Under adverse circumstances the customer may be late on paying scheduled pay downs or even default on the entire previously cash amount drawn on the credit line. So broadly, the capital allocation process tracks the largely deterministic UST cash flows; the variable and possibly perpetual deposit cashflows; and the variable, non-maturing cashflows with possible default to transform an inception balance sheet and new business to a target balance sheet. For a large US bank balance sheet there could be 100MM deposit accounts and another 100MM credit card accounts. That is the data set.

The first Machine Learning problem is calibrating a series of security level cashflow models to calculate the expected cashflows on 1. the inception balance sheet and 2. the new investment business. The second Machine Learning problem is to identify the required market data, econometric data, and customer data to fit the realized cashflow timing with minimized error. We want a current fit as well as a forward expected fit for the purpose of Monte Carlo simulation.

Federal Reserve. (2017, Feb.). 2017 Supervisory Scenarios for Annual Stress Tests Required under the Dodd-Frank Act Stress Testing Rules and the Capital Plan Rule. Retrieved from Press Releases:

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Pearl, J. (2000). Causality: Models, Reasoning, and Inference. UK: Cambridge University Press.

Raschka, S., & Mirjalili, V. (2017). Python Machine Learning (Second ed.). Packt.

Sugiyama, M. (2015). Introduction to Statistical Machine Learning. Morgan Kaufmann.

Witten, I., Frank, E., & Hall, M. (2011). Data Mining: Practical Machine Learning Tools and Techniques (Third ed.). Morgan Kaufmann.



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