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This paper examines deposits of individuals ("retail") and large companies("wholesale") in the U.S. banking industry, and how these deposit types areimpacted by macroeconomic factors, such as quantitative easing (QE). Actualdata for deposits by holder are unavailable. We use a dataset on banks'financial information and probabilistic generative model to predict industryretail-wholesale deposit split from 2000 to 2020. Our model assumes accountbalances arise from separate retail and wholesale lognormal distributions andfit parameters of distributions by minimizing error between actual bank metricsand simulated metrics using the model's generative process. We use time-seriesregression 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 inloans increase both wholesale and retail deposits evenly. The result shows thatQE following the 2008 financial crisis benefited large companies more thanaverage individuals, a relevant finding for economic decision making. Inaddition, this work benefits bank management strategy by providing forecastingcapability for retail-wholesale deposits.

Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits