Cloud Accounting, Artificial Intelligence, and Machine Learning in Digital Financial Applications: Implications for MSME Accounting Information in South Sumatra - Lesi Hartati; Haryono Umar; Lilis Puspitawati; Raja Haydar Alibi (Yayasan Ilomata, 2026)
Many MSMEs still do not understand the use of digital financial applications as a widespread issue and require optimal implementation of features available in artificial intelligence and machine learning to become drivers of accounting practices. Referring to the Technology Acceptance Model (TAM) theory, how someone accepts and uses information technology is influenced by two main factors, namely Perceived Usefulness, namely the belief that the use of technology will improve performance, productivity, effectiveness, and work results, second, perceived ease of use, namely the belief that technology can be used easily without requiring great effort, this requires cloud-based accounting to strengthen digital payments. This study was designed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical results of this study confirm that Cloud accounting has an impact of (β = 0.42) on digital financial applications, followed by Artificial Intelligence (β = 0.35) and Machine Learning (β = 0.28). Digital financial applications have a positive and significant impact on accounting information quality (β = 0.28). This suggests that digital financial applications can improve perceived usefulness and ease of use through automated transaction recording, real-time financial analysis, and fast and accurate financial reporting. These findings demonstrate that technology investment relies heavily on employee understanding and skills to improve organizational performance and enhance collaboration between users. This study has limitations due to its dynamic nature, which follows the development of digital financial applications, which are subject to change along with technological innovation, feature updates, and changes in user behavior, as well as the ability to predict future financial analysis.