Materiality Maps: Process Mining Data Visualization for Financial Audits

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Financial audits are a safeguard to prevent the distribution of false information which could detrimentally influence stakeholder decisions. The increasing integration of computer technology for the processing of business transactions create new challenges for auditors who have to deal with increasingly large and complex data. Process mining can be used as a novel Big Data analysis technique to support auditors in this context. A challenge for using this type of technique is the representation of analyzed data. Process mining algorithms usually discover large sets of mined process variants. This study introduces a new approach to visualize process mining results specifically for financial audits in an aggregate manner as materiality maps. Such maps provide an overview about the processes identified in an organization and indicate which business processes should be considered for audit purposes. They reduce an auditor’s information overload and help to improve decision making in the audit process.
Original languageEnglish
Title of host publicationProceedings of the 52nd Hawaii International Conference on System Sciences
Number of pages10
Place of PublicationHonolulu
PublisherHawaii International Conference on System Sciences (HICSS)
Publication date2019
Pages1045-1054
ISBN (Print)9780998133126
DOIs
Publication statusPublished - 2019
EventThe 52nd Hawaii International Conference on System Sciences - Wailea, United States
Duration: 8 Jan 201911 Jan 2019
Conference number: 52
https://scholarspace.manoa.hawaii.edu/handle/10125/59440

Conference

ConferenceThe 52nd Hawaii International Conference on System Sciences
Number52
CountryUnited States
CityWailea
Period08/01/201911/01/2019
Internet address
SeriesProceedings of the Annual Hawaii International Conference on System Sciences
ISSN1060-3425

Keywords

  • Data visualization
  • Data analytics
  • Financial statement audit
  • Materiality
  • Process mining

Cite this

Werner, M. (2019). Materiality Maps: Process Mining Data Visualization for Financial Audits. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 1045-1054). Honolulu: Hawaii International Conference on System Sciences (HICSS). Proceedings of the Annual Hawaii International Conference on System Sciences https://doi.org/10125/59544
Werner, Michael . / Materiality Maps : Process Mining Data Visualization for Financial Audits. Proceedings of the 52nd Hawaii International Conference on System Sciences. Honolulu : Hawaii International Conference on System Sciences (HICSS), 2019. pp. 1045-1054 (Proceedings of the Annual Hawaii International Conference on System Sciences).
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Werner, M 2019, Materiality Maps: Process Mining Data Visualization for Financial Audits. in Proceedings of the 52nd Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences (HICSS), Honolulu, Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 1045-1054, The 52nd Hawaii International Conference on System Sciences , Wailea, United States, 08/01/2019. https://doi.org/10125/59544

Materiality Maps : Process Mining Data Visualization for Financial Audits. / Werner, Michael .

Proceedings of the 52nd Hawaii International Conference on System Sciences. Honolulu : Hawaii International Conference on System Sciences (HICSS), 2019. p. 1045-1054 (Proceedings of the Annual Hawaii International Conference on System Sciences).

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Werner M. Materiality Maps: Process Mining Data Visualization for Financial Audits. In Proceedings of the 52nd Hawaii International Conference on System Sciences. Honolulu: Hawaii International Conference on System Sciences (HICSS). 2019. p. 1045-1054. (Proceedings of the Annual Hawaii International Conference on System Sciences). https://doi.org/10125/59544