  This paper examines deposits of individuals ("retail") and large companies
("wholesale") in the U.S. banking industry, and how these deposit types are
impacted by macroeconomic factors, such as quantitative easing (QE). Actual
data for deposits by holder are unavailable. We use a dataset on banks'
financial information and probabilistic generative model to predict industry
retail-wholesale deposit split from 2000 to 2020. Our model assumes account
balances arise from separate retail and wholesale lognormal distributions and
fit parameters of distributions by minimizing error between actual bank metrics
and simulated metrics using the model's generative process. We use time-series
regression to forward predict retail-wholesale deposits as function of loans,
retail loans, and reserve balances at Fed banks. We find increase in reserves
(representing QE) increases wholesale but not retail deposits, and increase in
loans increase both wholesale and retail deposits evenly. The result shows that
QE following the 2008 financial crisis benefited large companies more than
average individuals, a relevant finding for economic decision making. In
addition, this work benefits bank management strategy by providing forecasting
capability for retail-wholesale deposits.
