2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 11 , Issue 2 , PP: 53-66, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Yogesh Khandokar 1 *
Doi: https://doi.org/10.54216/AJBOR.110204
For the purposes of maintaining a healthy liquid balancing and maximizing cash flow, accurate cash forecasting is very necessary for banking operations. In order to overcome the shortcomings of conventional forecasting techniques, such as linear regression, which do not take into account dynamic elements like pay impacts and vacations, this research, offers a Cash Management Model (PSO-CMM) that is based on Particle Swarm Optimization. Taking into account a number of characteristics, such as working days, holiday impacts, and pay patterns, PSO-CMM improves its coefficients for cash prediction. This allows for both short-term and long-term predictions. By swarm intelligence, the model is able to improve the accuracy of its predictions, hence providing greater resilience to continuously modifying surroundings. In addition to the development of linear and hybrid models that combine PSO with artificial neural networks (ANNs), the incorporation of adaptive computing approaches to improve weights is one of the most important advances. Furthermore, in order to prevent local optimums and to promote universal convergence, erratic patterns were incorporated in the most sophisticated systems. The results of this evaluation revealed a significant rise in the accuracy of cash projections. This study presents a comprehensive methodology for predicting cash requirements, which makes it possible for micro financial organizations to get useful insights and improves their operating effectiveness in situations that are always changing. When compared with Normal Data, the suggested PSO-CMM method's overall accuracy is around 91%.
LCD , MGDM , OLS , NLS , OR , MSE , RMSE , MAD
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