Neural Network-Based Financial Modeling for Cash Flow Prediction and Revenue Forecasting
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V8I2P104Keywords:
Neural Networks, Financial Modeling, Cash Flow Prediction, Revenue Forecasting, Deep Learning, Predictive Analytics, Financial Decision SupportAbstract
Financial planning, cash flow management and decision-making in contemporary organizations can be done well through proper forecasting of cash flows and revenues. The standard financial forecasting procedures are typically not able to represent the relationship which is nonlinear and dynamic, changing market drivers, seasonal variations of the markets as well as dynamic pattern of the trend of the business. Through this research paper we will present a financial modeling framework developed to operate on a neural network created to boost cash flow forecasts and revenue forecasts by utilizing past financial records, transaction data, operation indicators and market related variables. The proposed system will integrate the data pre-processing, feature selection, neural training, temporal pattern analysis, and predictive output generation to expose underlying financial patterns and predictive future financial performance. The framework will assist in making an informed financial planning and risk management system based on its learning of complex relationships among revenues streams, operating expenses, receivables, payables and cash balances that help enable the work of better finance managers. Quality of data, model decipherability, predictability and responsiveness to new business settings are also the focus of the strategy. This is proven by the fact that it could be used in retail, banking, manufacturing and service industries meaning that it can be used by any organization that wishes to have data-based financial insight. The research has laid the building blocks of applying neural network methods to financial decision support systems through which it is possible to get a better insight into future cash flows, revenue variations, and possible liquidity inhibiting factors. The suggested framework is part of intelligent financial management since the model helps to make timely, consistent, and informed decisions in an organization.
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