Abstract
Cash forecasting plays a vital role in any financial organization to maintain the optimal cash balance to satisfy the customer needs on a daily basis without any delay. In the traditional approach statistical methods were used for cash forecasting. Banks have great challenges to avoid the surplus cash as well as to keep adequate cash to meet the customer demand. An intelligent model is needed to identify the cash requirement using cognitive approach. Hence, an evolutionary computing using the hybrid swarm system was introduced for the cash management of a bank. In this study, cash prediction models were developed from the historic short term data and long term data. In order to find the daily cash requirement of financial organization an intelligent hybrid model composed of an Artificial Neural Network (ANN) and a Particle Swarm Optimization (PSO) was introduced. The proposed methodology was capable of training and adjusting the ANN parameters through PSO to improve the efficiency of the cash management model. In a PSO-based ANN model, PSO searches for a set of best weights and biases for an ANN to minimize the error were evaluated using Mean Square Error (MSE). The experimental analysis was made for the selected parameters to maintain the optimal cash. The proposed ANNPSO model has proven its accuracy with the best MSE of short term data was 0.0035 and for long term data was found at 0.0029.
Highlights
LITERATURE REVIEWForecasting cash demand needs to be more accurate for any financial organization, including banks (Prem and Ekta, 2006)
The earlier research was made for financial organization such as Artificial Neural Network (ANN) based cash forecasting using back propagation for short term data (Fraydoon et al, 2010), Comparative analysis was made between least square method and Particle Swarm Optimization (PSO) (Alli et al, 2013), Optimization of cash management model using computational intelligence using back propagation leads to trap in local minima (Ramya and Alli, 2015) since there is a need to improve the performance of the system by minimizing the error for both long term and short term data a new hybrid approach was introduced in this study to forecast the cash requirement for a bank from the historic data using training feed forward neural network with PSO
The CASH FORECASTING (CF)-FNN-PSO was developed to maintain the right amount of cash to provide effective customer support services using evolutionary computing technique were implemented for two different bank branches, short term data of a particular branch of the State Bank of India (Prem and Ekta, 2006) and for long term data of City Union Bank data set for three years has been collected and simulated using MATLAB
Summary
LITERATURE REVIEWForecasting cash demand needs to be more accurate for any financial organization, including banks (Prem and Ekta, 2006). The earlier research was made for financial organization such as ANN based cash forecasting using back propagation for short term data (Fraydoon et al, 2010), Comparative analysis was made between least square method and PSO (Alli et al, 2013), Optimization of cash management model using computational intelligence using back propagation leads to trap in local minima (Ramya and Alli, 2015) since there is a need to improve the performance of the system by minimizing the error for both long term and short term data a new hybrid approach was introduced in this study to forecast the cash requirement for a bank from the historic data using training feed forward neural network with PSO.
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