Generating Synthetic Journal‐Entry Data Using Variational Autoencoder

ABSTRACT

In recent years, research studies have been conducted on analyzing journal‐entry data using advanced visualization techniques and machine learning models. However, because of their highly confidential nature, these data are not disclosed externally, which can limit research and business opportunities to analyze the rich organizational information they contain. To address these problems, this study utilized a variational autoencoder to generate synthetic journal‐entry data with statistical properties similar to those of actual data. The synthetic journal‐entry data we created adhered to the fundamental structure of double‐entry bookkeeping and were quantitatively evaluated for quality.