This research study aims to investigate the role of Artificial Intelligence (AI) in efficient management of public financial systems and treasury functions. AI involves a broad array of knowledge, including various concepts, methodologies, strategic tools, and diverse applications. It can be defined as the study of systems that gather inputs from the environment and respond through actions. Using AI in financial management and treasury presents distinct challenges and opportunities, as many treasury tasks have transitioned from physical to virtual processes, with automation advancing quickly. Financial and treasury teams are largely made up of knowledge workers who make decisions and perform analyses within dynamic frameworks. These frameworks must take into account both external and internal factors, as well as the effects of any actions on treasury outcomes. AI in finance and treasury functions closely mirrors the complexity of human nervous system, as it extends well beyond the basic automation. Like the nervous system, AI in these fields must process data rapidly and accurately, handling tasks such as data collection, classification, and integration into broader datasets. Today, neural networks within AI have advanced significantly and are widely applied across various treasury management areas, including early fraud detection, risk assessment, liquidity management, debt management, financial data quality control, extraction of hidden financial insights, accounting, and financial reporting. This review article aims to introduce readers to the various areas where AI can be applied in treasury operations, while also highlighting opportunities for enhancing accounting practices and driving digital transformation in treasury management. Additionally, it explores some potential research areas within these two fields.
