Forecasting bank cash flows using intelligent systems

Abstract

The object of the study is the processes of cash flow management in the banking system of Ukraine, which is characterized by high dynamism, increased risks caused by war and economic instability, as well as rapid adaptation to digital technologies and European standards. The article emphasizes the critical importance of effective cash flow management to maintain financial stability and ensure uninterrupted operations of the bank in the face of uncertainty.

Problem statement. The main problem studied in the article is the lack of efficiency of traditional methods of forecasting and managing cash flows in modern realities. These methods are unable to adequately process large amounts of data, take into account complex nonlinear dependencies, and respond quickly to unpredictable changes caused by both war and digital transformation. This creates liquidity risks, leads to suboptimal use of capital, and reduces the overall resilience of the banking system.

Unresolved aspects of the problem. Today, there are gaps in the integration of the latest intelligent systems directly into the bank's operational and strategic processes. There are still unanswered questions about how to turn highly accurate predictions obtained through machine learning into concrete, managerial decisions that will minimize risks.

Purpose of the article. The aim of the article is to develop comprehensive recommendations for improving cash flow management in a bank using intelligent systems. For this purpose, a three-dimensional approach is used, which combines Big Data analysis, improving the accuracy of forecasts using machine learning, and their integration into a management decision support system.

Presentation of the main material. The authors of the article use a three-dimensional coordinate system of “analysis-prediction-integration” to structure the research. Practical examples for forecasting liquidity, assessing borrowers' solvency, and the effectiveness of marketing campaigns are considered. The use of LSTM, SVM, Random Forest, and RNN models to improve forecasting accuracy is detailed. To integrate the forecasting results into the bank's risk management system, specific solutions are proposed, such as the use of automated dashboards, early warning systems, and dynamic scoring.

Conclusions. The recommendations proposed in this article allow banks to move from reactive to proactive cash flow management. This helps to significantly reduce operational risks, optimize capital, increase profitability and strengthen competitive positions. The practical value of the study lies in the provision of specific tools and scenarios for the implementation of intelligent systems in daily operations, which is extremely important for ensuring the financial stability of the Ukrainian banking system in the face of uncertainty.