Hybrid Swarm Optimization for Cash Management Using Evolutionary Computing

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.