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Connecting the Dots: Graph Neural Networks for Auditing Accounting Journal Entries

Qing Huang et al.

Auditing: A Journal of Practice and Theory2026https://doi.org/10.2308/ajpt-2024-058article
AJG 3ABDC A*
Weight
0.50

What the paper says

SUMMARY Based on the double-entry bookkeeping mechanism, each transaction is recorded in at least two ledger accounts, with one debit and the other one credit. Journal entry data, in the context of accounting, contains a rich network of information that can be effectively translated into a graph. This study explores how to use graph neural networks to learn graph representations from journal entry data and to systematically understand the complex patterns and connections that exist in journal entries at the transaction level. The real-world application results demonstrate that the unsupervised graph neural network framework offers a promising methodology for detecting error and fraud in financial audits.1 Data Availability: Dataset A comes from the Ernst & Young Academic Resource Center (EYARC), https://www.eyarc.site/blog/peach-state-university-hotel-understanding-audit-analytics-case

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https://doi.org/https://doi.org/10.2308/ajpt-2024-058

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@article{qing2026,
  title        = {{Connecting the Dots: Graph Neural Networks for Auditing Accounting Journal Entries}},
  author       = {Qing Huang et al.},
  journal      = {Auditing: A Journal of Practice and Theory},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.2308/ajpt-2024-058},
}

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Evidence weight

0.50

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

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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.