Towards Automated Auditing with Machine Learning

Sifa, Rafet; Ladi, Anna; Pielka, Maren; Ramamurthy, Rajkumar; Hillebrand, Lars; Kirsch, Birgit; Biesner, David; Stenzel, Robin; Bell, Thiago; Lübbering, Max; Nütten, Ulrich; Bauckhage, Christian; Warning, Ulrich; Fürst, Benedikt; Khameneh, Tim Dilmaghani; Thom, Daniel; Huseynov, Ilgar; Kahlert, Roland; Schlums, Jennifer; Ismail, Hisham; Kliem, Bernd; Loitz, Rüdiger

doi:10.1145/3342558.3345421

Cited by 29 publications

(6 citation statements)

References 7 publications

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“…For internal use, this method can be applied to a single document or entire document corpora using a web-based application and a REST API. This allows for pre-processing of documents that can then be used by developers and researchers to train and evaluate further models for machine learning on financial data (e.g., [27]).…”

Section: Discussionmentioning

confidence: 99%

Anonymization of German financial documents using neural network-based language models with contextual word representations

Biesner

Ramamurthy

Stenzel

et al. 2021

Int J Data Sci Anal

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The automatization and digitalization of business processes have led to an increase in the need for efficient information extraction from business documents. However, financial and legal documents are often not utilized effectively by text processing or machine learning systems, partly due to the presence of sensitive information in these documents, which restrict their usage beyond authorized parties and purposes. To overcome this limitation, we develop an anonymization method for German financial and legal documents using state-of-the-art natural language processing methods based on recurrent neural nets and transformer architectures. We present a web-based application to anonymize financial documents and a large-scale evaluation of different deep learning techniques.

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Section: Discussionmentioning

confidence: 99%

Anonymization of German financial documents using neural network-based language models with contextual word representations

Biesner

Ramamurthy

Stenzel

et al. 2021

Int J Data Sci Anal

Self Cite

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“…Furthermore, Sifa et al [24] developed a tool that uses machine learning techniques to automatically check whether the financial statements of a certain corporation are according to specific legal requirements. 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.…”

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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“…Lastly, our application lies in the field of automated financial analysis and auditing, a field that has been growing quickly in recent years. Specialized software, for example for text recommendation [15] and automatic anonymization [16] are entering the market and we aim to add further to this toolbox.…”

Section: Related Workmentioning

confidence: 99%

Solving Subset Sum Problems using Binary Optimization with Applications in Auditing and Financial Data Analysis

Biesner¹,

Sifa²,

Bauckhage³

2022

Preprint

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<div><br></div><div>Many applications in automated auditing and the analysis and consistency check of financial documents can be formulated in part as the subset sum problem: Given a set of numbers and a target sum, find the subset of numbers that sums up to the target. The problem is NP-hard and classical solving algorithms are therefore not practical to use in real applications. We tackle the problem as a QUBO (quadratic unconstrained binary optimization) problem and show how gradient descent on Hopfield Networks reliably finds solutions for both artificial and real data. We give an outlook for the application of specialized hardware and quantum algorithms.</div>

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Towards Automated Auditing with Machine Learning

Cited by 29 publications

References 7 publications

Anonymization of German financial documents using neural network-based language models with contextual word representations

Anonymization of German financial documents using neural network-based language models with contextual word representations

Automatic Consistency Checking of Table and Text in Financial Documents

Solving Subset Sum Problems using Binary Optimization with Applications in Auditing and Financial Data Analysis

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