AI-driven management information system for cost accounting and budget optimisation

Abstract

This study aims to enhance traditional management accounting by developing an AI-based system for cost accounting and budget optimisation. The proposed framework follows a structured nine-step process, beginning with problem identification and concluding with system validation. Each stage ensures transparency and effective implementation. AI contributes to improved prediction accuracy, cost reduction, and more reliable financial decision-making, while highlighting the limitations of outdated, paper-based methods. In practice, AI assists in tasks such as tax processing, error detection, and forecasting. Historical data are used to train AI models, which are then applied to accounting operations and validated for accuracy and relevance. Despite challenges in integration, scalability, and ethical considerations, results indicate strong reliability, with Cronbach’s alpha and composite reliability values exceeding 0.8 in SEM tests. Overall, the AI model outperformed traditional methods by reducing costs and adapting effectively to workload variations.