Abstract

In today’s technical era, the financial organizations have great challenges to optimize the cash management process. Maintaining minimum cash leads to customer frustration. At the same time, upholding excess cash is a loss to the organization. Hence, soft computing based cash management solutions are required to maintain optimal cash balance. An Artificial Neural Network (ANN) is one such technique which plays a vital role in the fields of cognitive science and engineering. In this study, a novel ANN-based cash Forecasting Model (ANNCFM) has been proposed to identify the cash requirement on daily, weekly and monthly basis. The six cash requirement parameters: Reference Year (RY), Month of the Year (MOY), Working Day of the Month (WDOM), Working Day of the Week (WDOW), Salary Day Effect (SDE) and Holiday Effect (HDE) were fed as input to ANNCFM. Trials were carried out for the selection of ANNCFM network parameters. It was found that number of hidden neurons, learning rate and the momentum when set to 10, 0.3 and 0.95, respectively yielded better results. Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) were used to evaluate the performance of the proposed model. MSE that was less than 0.01 proves the capability of the proposed ANNCFM in estimating the cash requirement.

Keywords:

ANN, ANNCFM, back-propagation, cash forecasting,

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