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Generating Synthetic Journal‐Entry Data Using Variational Autoencoder

Ryoki Motai et al.

Intelligent Systems in Accounting, Finance and Management2025https://doi.org/10.1002/isaf.70005article
AJG 1
Weight
0.37

What the paper says

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.

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https://doi.org/https://doi.org/10.1002/isaf.70005

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@article{ryoki2025,
  title        = {{Generating Synthetic Journal‐Entry Data Using Variational Autoencoder}},
  author       = {Ryoki Motai et al.},
  journal      = {Intelligent Systems in Accounting, Finance and Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1002/isaf.70005},
}

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Generating Synthetic Journal‐Entry Data Using Variational Autoencoder

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.