Cash Management Forecasting Using Long Short-Term Memory (LSTM) Networks

Abstract

LSTM neural networks project retail and corporate banking cash management. Historical transaction sequences, seasonality, and macroeconomic factors like interest rate variations and GDP growth are used to predict short- and medium-term cash flow. While ARIMA and Prophet presume linear dependencies and limited temporal memory, LSTM captures nonlinear dynamics and long-range temporal connections in financial time series. We train and validate the model using high-frequency transaction datasets and compare it to standard forecasting approaches using RMSE, MAE, and MAPE. Forecast consistency and accuracy improve liquidity optimization, intraday treasury allocation, and risk-adjusted decision-making. In complicated banking environments, deep recurrent architectures may automate treasury forecasting and improve institutional cash management.