Neural Networks in Accounting: Bridging Financial Forecasting and Decision Support Systems

The rapid evolution of financial markets and technological advancements has significantly impacted the field of accounting, creating a demand for innovative approaches to financial forecasting and decision making. Our research addresses contemporary socio-economic needs within the accounting domain, particularly the growing reliance on automation and artificial intelligence (AI) to enhance the accuracy of financial projections and improve operational efficiency and proposes a theoretical and empirical framework for applying neural networks to predict corporate profitability, using key accounting variables. The proposed model operates on two distinct levels. At the theoretical level, we defined the conceptual relationship between accounting constructs and profitability, proposing that shifts in financial metrics directly influence the net income. This relationship is grounded in established accounting theory and is operationalized through financial ratios and indicators, creating a clear, semantically linked framework. At the empirical level, these abstract concepts can be reified into measurable variables, where a multi-layered neural network can be deployed to uncover complex, nonlinear relationships between the input data and predicted profit. Through iterative training and testing, the model can provide plausible predictions, validated by historical financial data. We are taking time-honored accounting principles and combining them with cutting-edge technology to predict profitability in ways that have not been possible before. The hope is that by embracing this new approach, we can make financial predictions more accurate, support better strategic decision making, and, ultimately, help businesses navigate the complexities of modern financial markets. This research addresses the growing need for advanced financial forecasting tools by applying neural networks to accounting. By combining theoretical accounting principles with cutting-edge machine learning techniques, we aim to demonstrate that neural networks can bridge the gap between traditional accounting practices and the increasing demands for predictive accuracy and strategic decision making in a rapidly evolving financial environment.