A Novel Cost-sensitive Capsule Network for Audit Fraud Detection

Zhu, Feng; Ning, DJ; Wang, Yu; Liu, Shipeng

doi:10.1109/iucc-cit-dsci-smartcns55181.2021.00091

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Ramamurthy et al [23] then made improvements to this tool by modifying the mechanism of the recommendations of legal requirements and by obtaining textual representations from a BERT encoder. Zhu et al [32] introduced a costsensitive capsule network to identify abnormal instances from normal instances in audit data. Hillebrand et al [18] introduced an automated system called KPI-BERT to automatically extract KPIs from financial documents in German language and also linked these KPIs with a proper relationship to their respective numerical value.…”

Section: Related Workmentioning

confidence: 99%

Automatic Consistency Checking of Table and Text in Financial Documents

Ali

Deußer

Houben

et al. 2023

nldl

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A company's financial documents use tables along with text to organize the data containing key performance indicators (KPIs) (such as profit and loss) and a financial quantity linked to them. The KPI’s linked quantity in a table might not be equal to the similarly described KPI's quantity in a text. Auditors take substantial time to manually audit these financial mistakes and this process is called consistency checking. As compared to existing work, this paper attempts to automate this task with the help of transformer-based models. Furthermore, for consistency checking it is essential for the table's KPIs embeddings to encode the semantic knowledge of the KPIs and the structural knowledge of the table. Therefore, this paper proposes a pipeline that uses a tabular model to get the table's KPIs embeddings. The pipeline takes input table and text KPIs, generates their embeddings, and then checks whether these KPIs are identical. The pipeline is evaluated on the financial documents in the German language and a comparative analysis of the cell embeddings' quality from the three tabular models is also presented. From the evaluation results, the experiment that used the English-translated text and table KPIs and Tabbie model to generate table KPIs’ embeddings achieved an accuracy of 72.81% on the consistency checking task, outperforming the benchmark, and other tabular models.

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Section: Related Workmentioning

confidence: 99%