Examining the Relationship between the Use of Artificial Intelligence in Accounting Systems and the Improvement of Cash Flow Forecasting Accuracy in Iranian Commercial Banks

Abstract

With the increasing complexity of financial operations and the intense competition in the banking sector, the adoption of advanced technologies, particularly Artificial Intelligence (AI), in accounting systems has gained significant importance. One of the critical areas in this field is cash flow forecasting, which serves as a vital indicator for performance evaluation and financial decision-making in banks. The present study aims to investigate the impact of applying AI algorithms in accounting systems on improving the accuracy of cash flow forecasting in Iranian commercial banks. The research employed a mixed-method approach based on real financial data from selected commercial banks during the period 2018–2023. Initially, historical data related to cash flows and accounting reports were collected and analyzed using traditional regression models. Subsequently, machine learning algorithms and deep neural networks were applied to the same data to identify differences in forecasting accuracy. The findings revealed that the use of AI significantly enhances the accuracy of cash flow forecasting compared to traditional methods. Moreover, the results indicated that factors such as input data quality, model design, and the integration level of accounting information systems play crucial roles in forecasting success. These results, while highlighting the necessity of investing in technological infrastructure and training specialized human resources, can assist policymakers and banking managers in enhancing financial transparency and risk management.