머신러닝 기법을 이용한 지방회계정보 활용 방안

This study aims to explore ways to promote the utilization of local government accounting information, particularly in the context of the accrual-based double-entry accounting system that has been introduced since 2008. Although institutional, academic, and practical efforts have been made to enhance the use of such accounting information, concerns remain that its utilization has not been as active as initially expected. To address this issue, the study examines whether the application of machine learning techniques can provide alternative approaches to the traditional methods of analysis. The research conducts a review of the relevant literature and applies various empirical methodologies, including cross-sectional analysis, association analysis, and time-series forecasting, to investigate the usefulness of local government accounting information. The results indicate that machine learning techniques can complement conventional approaches by capturing complex patterns more effectively and offering new possibilities for financial analysis. This study contributes to the academic discussion by presenting a methodological alternative that integrates machine learning into the field of governmental accounting research, and it provides practical implications for enhancing the utilization of accrual-based accounting information in local governments.