Financial statement networks: an application of network theory in audit

Boersma, M.; Sourabh, Sumit; Hoogduin, Lucas A.; Kandhai, Drona

doi:10.21314/jntf.2018.048

Cited by 7 publications

(19 citation statements)

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“…These processes account for 95% of the monetary transfer, the remaining business processes are assigned to the Unclassified category. Boersma et al 15 show that this category could be quite sizable. In addition, the expert identified processes such that we have a total of 14 processes.…”

Section: Resultsmentioning

confidence: 99%

“…In other words, when we have debit (inflow) and credit (outflow) entries, we aggregate them to obtain the net position at time t. • Credit-Debit labels (CD)-naive model that operates only with in-and outflows, in other words, it does not use any information about the company's accounting flow structure but only whether an account is debited or credited. • Expert's labels-the financial statements network flow with flow labels annotated by an expert (see Appendix A in Boersma et al 15 ). Thus, compared to the CD-model we add additional information of the process context, For illustrative purposes, we picked three predictive models.…”

Section: Resultsmentioning

confidence: 99%

“…The dark blue represents outflows from financial accounts and the light blue represents inflows. The top left network is a new rendering of the same network as displayed in Boersma et al 15 .

Figure 2 The bipartite network extracted from the transaction data.…”

Section: Resultsmentioning

confidence: 99%

“…Expert’s labels —the financial statements network flow with flow labels annotated by an expert (see Appendix A in Boersma et al 15 ). Thus, compared to the CD-model we add additional information of the process context, e.g.…”

Section: Resultsmentioning

confidence: 99%

“…In order to construct predictive models for audit purposes, it is important to know the interaction between different components of the financial statements, which is referred to as the structural information of the company 8 – 10 , 13 , 14 . Chen et al 10 define a generic structural model whereas in our previous work 15 we proposed a network representation that extracts the structural information from transaction data. However, this results in high-dimensional networks which are difficult to interpret.…”

Section: Introductionmentioning

confidence: 99%

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Reducing the complexity of financial networks using network embeddings

Boersma

Maliutin

Sourabh

et al. 2020

Sci Rep

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Accounting scandals like Enron (2001) and Petrobas (2014) remind us that untrustworthy financial information has an adverse effect on the stability of the economy and can ultimately be a source of systemic risk. This financial information is derived from processes and their related monetary flows within a business. But as the flows are becoming larger and more complex, it becomes increasingly difficult to distill the primary processes for large amounts of transaction data. However, by extracting the primary processes we will be able to detect possible inconsistencies in the information efficiently. We use recent advances in network embedding techniques that have demonstrated promising results regarding node classification problems in domains like biology and sociology. We learned a useful continuous vector representation of the nodes in the network which can be used for the clustering task, such that the clusters represent the meaningful primary processes. The results show that we can extract the relevant primary processes which are similar to the created clusters by a financial expert. Moreover, we construct better predictive models using the flows from the extracted primary processes which can be used to detect inconsistencies. Our work will pave the way towards a more modern technology and data-driven financial audit discipline.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…The dark blue represents outflows from financial accounts and the light blue represents inflows. The top left network is a new rendering of the same network as displayed in Boersma et al 15 .

Figure 2 The bipartite network extracted from the transaction data.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reducing the complexity of financial networks using network embeddings

Boersma

Maliutin

Sourabh

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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Measure cross-sectoral structural similarities from financial networks

Boersma

Wolsink

Sourabh

et al. 2023

Sci Rep

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Auditing is a multi-billion dollar market, with auditors assessing the trustworthiness of financial data, contributing to financial stability in a more interconnected and faster-changing world. We measure cross-sectoral structural similarities between firms using microscopic real-world transaction data. We derive network representations of companies from their transaction datasets, and we compute an embedding vector for each network. Our approach is based on the analysis of 300+ real transaction datasets that provide auditors with relevant insights. We detect significant changes in bookkeeping structure and the similarity between clients. For various tasks, we obtain good classification accuracy. Moreover, closely related companies are near in the embedding space while different industries are further apart suggesting that the measure captures relevant aspects. Besides the direct applications in computational audit, we expect this approach to be of use at multiple scales, from firms to countries, potentially elucidating structural risks at a broader scale.

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