2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 11 , Issue 2 , PP: 67-81, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Manjeet rege 1 *
Doi: https://doi.org/10.54216/AJBOR.110205
The Artificial Neural Network-based Cash Forecasting Model (ANN-CFM) is introduced in this part as one way of mitigating the vices that are characterised with linear approach to financial management. This paradigm is quite helpful when the analysis is focused on non-linear and, generally, troublesome data. ANN-CFM, therefore, simultaneously takes both the linear and non-linear information for improving on the cash forecasting. Due to this fact, it is able to realise and leverage over advantage from the computational competence that neural systems provide. The hidden, output and input layers use randomised initial biased and weights. These include biases together with weighting that is altered regarding a standard basis with the use of a learning strategy to try to find the greatest cash needs. This design is actually composed of three various layering. This is exactly what the ANN-CFM is capable of dealing with and it accepts inputs, both for LT and ST forecasting. Among these inputs, you have factors of the working days, wages’ impact, and the impacts of holidays. The ANN-CFM is a system that revolutionises the way a human would perform his/her decisions and is a highly parallelized and efficient analytical tool for large data. As a result, this results in enhancement of precision to that which is predicted. The kind of architecture used in the system is feed forward neural network, which uses back propagation to help in reducing the numbers of errors that prevail at the time of prediction. In this part, extensive application of ANN, including its ability to learn in environments that may be constantly evolving is also highlighted. Thus, this innovative approach allows for sure receipt of accurate solutions for the management of these funds by companies operating in the financial industry. Comparing it with Normal Data, it is clear that the ANN-CFM technique proposed here provides an overall accuracy of approximately 95%.
ANN-CFM , ANN , PSO , FFNN , C-PSO , MLP
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