Materiality Maps – Process Mining Data Visualization for Financial Audits

Werner, Michael

doi:10.24251/hicss.2019.129

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…Complementing previous research, the author recently presented a new way of visualizing process mining results specifically for financial audits, in an aggregate manner, as materiality maps. Such maps provide an overview of an organization's identified processes and indicate which business processes should be considered for audits [67].…”

Section: Process Mining and Auditmentioning

confidence: 99%

Fraud Audit Based on Visual Analysis: A Process Mining Approach

et al. 2021

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Among the knowledge areas in which process mining has had an impact, the audit domain is particularly striking. Traditionally, audits seek evidence in a data sample that allows making inferences about a population. Mistakes are usually committed when generalizing the results and anomalies; therefore, they appear in unprocessed sets; however, there are some efforts to address these limitations using process-mining-based approaches for fraud detection. To the best of our knowledge, no fraud audit method exists that combines process mining techniques and visual analytics to identify relevant patterns. This paper presents a fraud audit approach based on the combination of process mining techniques and visual analytics. The main advantages are: (i) a method is included that guides the use of the visual capabilities of process mining to detect fraud data patterns during an audit; (ii) the approach can be generalized to any business domain; (iii) well-known process mining techniques are used (dotted chart, trace alignment, fuzzy miner…). The techniques were selected by a group of experts and were extended to enable filtering for contextual analysis, to handle levels of process abstraction, and to facilitate implementation in the area of fraud audits. Based on the proposed approach, we developed a software solution that is currently being used in the financial sector as well as in the telecommunications and hospitality sectors. Finally, for demonstration purposes, we present a real hotel management use case in which we detected suspected fraud behaviors, thus validating the effectiveness of the approach.

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Section: Process Mining and Auditmentioning

confidence: 99%

Fraud Audit Based on Visual Analysis: A Process Mining Approach

et al. 2021

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“…A subsequent paper proposed a bipartite model based on extreme value theory and Bayesian analysis to detect journal entries with a low probability of causing a material misstatement [26]. Another stream of literature focusses on business processes and the application of process mining to identify unusual or not well-controlled process instances from ERP system data [27,28]. Schreyer et al described one of the few attempts to apply deep learning in the context of auditing [29].…”

Section: Anomaly Detection In Accounting Data From An Audit Perspectivementioning

confidence: 99%

Autoencoder Neural Networks versus External Auditors: Detecting Unusual Journal Entries in Financial Statement Audits

Schultz¹,

Tropmann-Frick²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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With the increasing complexity of business processes in today's organizations and the ever-growing amount of structured accounting data, identifying erroneous or fraudulent business transactions and corresponding journal entries poses a major challenge for public accountants at annual audits. In current audit practice, mainly static rules are applied which check only a few attributes of a journal entry for suspicious values. Encouraged by numerous successful adoptions of deep learning in various domains we suggest an approach for applying autoencoder neural networks to detect unusual journal entries within individual financial accounts. The identified journal entries are compared to a list of entries that were manually tagged by two experienced auditors. The comparison shows high f-scores and high recall for all analyzed financial accounts. Additionally, the autoencoder identifies anomalous journal entries that have been overlooked by the auditors. The results underpin the applicability and usefulness of deep learning techniques in financial statement audits.

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