Practical Experiment of Predicting Cash Flows with LSTM and Double-entry Bookkeeping Data

Double-entry bookkeeping data (DBD) systematically record the daily transactions of an organization and are the foundation of accounting information. Typically, they are more detailed and larger in scale than financial statements and feature cross-references between accounts. These characteristics make them beneficial for management. However, studies on quantitative evaluations of their usefulness in organizational management are scarce. This study investigates whether features derived from DBD by leveraging their characteristics can enhance future cash-balance predictions, with the primary focus on enhancing the cash management of organizations. To achieve this, DBD obtained from seven medical clinics were represented as graphs using their cross-references, and the graphs were then mapped into vector features based on graph similarity. The experimental results revealed that a long short-term memory model incorporating features unique to DBD obtained higher predictive accuracy than a simple long short-term memory model that only relies on past cash-balance time-series data. Additionally, an auto regressive integrated moving average model was more suitable for some clinics than the long short-term memory model. This can be attributed to the specific business characteristics and structural changes over time within each clinic. The main contribution of this study is the proposal and demonstration of a method that utilizes DBD, which are stored within organizations but often underutilized, to enhance cash management, thereby illustrating their significance. Based on our findings, business intelligence that provides predictive information using DBD could be developed by management professionals and companies offering accounting services.