Modern technologies such as AI and ML, along with the now widespread use of decision-making systems, pattern recognition and chatbots, could be used in financial departments for the task of analyzing and predicting liquidity levels with the goal of reducing the liquidity reserve buffer and using funds more efficiently.

In this paper, we explore applied examples of liquidity management with AI in banking and corporate treasuries. The liquidity forecast considered in this paper can be improved step by step by increasing the level of detail and granularity of the input data and adding macroeconomic factors to the analysis.

Overall, the use of AI can significantly improve the risk management situation, reducing the burden on risk management resources.