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.
Highlights
Forecasting cash demand needs to be more accurate for any financial organization, including banks (Fraydoon et al, 2010; Kumar and Walia, 2006; Alli et al, 2013)
An earlier cash requirement study was made using a feed forward neural network with back propagation for short term data of two months (Kumar and Walia, 2006). Another comparative study was made in the cash anticipation using a classic time series model and artificial neural networks (Fraydoon et al, 2010)
Design of proposed ANN-based cash Forecasting Model (ANNCFM) architecture: The process of designing a neural network in many fields resulted in a satisfactory performance, but building a Corresponding Author: A
Summary
Forecasting cash demand needs to be more accurate for any financial organization, including banks (Fraydoon et al, 2010; Kumar and Walia, 2006; Alli et al, 2013). An earlier cash requirement study was made using a feed forward neural network with back propagation for short term data of two months (Kumar and Walia, 2006). Another comparative study was made in the cash anticipation using a classic time series model and artificial neural networks (Fraydoon et al, 2010). The daily cash requirement models for a bank were optimized with particle swarm and compared with the least square method for short term data (Alli et al, 2013). The main objective of the paper is to design, develop and test a unique supervised method to forecast the cash requirement for banks from their historical data
Sign-in/Register to view highlights & summary 
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call