Artificial Intelligence in Finance, Cash Management, and Management Accounting:A Comprehensive Framework for Intelligent Financial Transformation

Abstract

This study proposes a comprehensive model of cooperation between Artificial Intelligence (AI) and finance-related processes, such as financial operations, cash management and management accounting, and presents the potential for intelligent financial transformation. This research utilizes a structured analytical method that differs from previous conceptual studies by analyzing the applications of machine learning, predictive analytics and natural language processing in improving financial decision making, financial operational efficiency and risk management. To build an integrated A.I.-based financial architecture, which links data sources, analytics engines and decision support systems together across all financial functions, the paper puts forward a proposal.The paper suggests an integrated financial architecture based on A.I. that integrates data sources, analytics engines and decision-support systems across finance functions. The framework is measured against certain key performance indicators (KPIs) such as prediction accuracy, process efficiency, and decision-making time. The results show that the use of AI yields substantial benefits in terms of improved forecast accuracy (20–30%) and a decrease in manual workload (up to 60%), as well as better financial visibility in real time. The study also highlights the critical implementation challenges, such as data quality, system integration, and governance constraints. The research makes both a theoretical contribution and a managerial contribution by making a link between conceptual AI models and the possible application in the financial sectors.