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Doublethink in governmental accounting: development of an RPA to identify inconsistencies in financial reporting

Fundacao Getulio Vargas and Petroleo Brasileiro S/A (Petrobras) et al.

The International Journal of Digital Accounting Research2023https://doi.org/10.4192/1577-8517-v24_1article
ABDC B
Weight
0.67

What the paper says

Aiming to assess the reliability of governmental accounting under an armchair-audit approach, we develop a framework to compare the financial reports submitted by municipalities to two different agencies: the Ministry of Finance and the respective Court of Accounts. We developed a framework using concepts of RPA in conjunction with OCR to download, extract, organize, and finally compare the reliability of the financial reports submitted by the municipalities. The results indicate that a framework of RPA is helpful to automate many tasks necessary to armchair-audit municipalities' financial reports. The results also indicate that many Brazilian municipalities submit inconsistent data to the monitoring agencies, i.e., the Ministry of Finance and respective Court of Accounts. Additionally, our findings suggest that more computerized entities are less prone to present inconsistencies in their accounting data.

5 citations

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https://doi.org/https://doi.org/10.4192/1577-8517-v24_1

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@article{fundacao2023,
  title        = {{Doublethink in governmental accounting: development of an RPA to identify inconsistencies in financial reporting}},
  author       = {Fundacao Getulio Vargas and Petroleo Brasileiro S/A (Petrobras) et al.},
  journal      = {The International Journal of Digital Accounting Research},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.4192/1577-8517-v24_1},
}

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Doublethink in governmental accounting: development of an RPA to identify inconsistencies in financial reporting

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Evidence weight

0.67

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

F · citation impact0.82 × 0.4 = 0.33
M · momentum0.80 × 0.15 = 0.12
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.