Event Knowledge Graphs for Auditing: A Case Study

Klijn, E. L., Preuss, D., Imeri, L., Baumann, F., Mannhardt, F., & Fahland, D. (2024). Event Knowledge Graphs for Auditing: A Case Study. In J. De Smedt, & P. Soffer (Eds.), Process Mining Workshops – ICPM 2023 International Workshops, 2023, Revised Selected Papers (pp. 84-97). (Lecture Notes in Business Information Processing; Vol. 503 LNBIP). https://doi.org/10.1007/978-3-031-56107-8_7


Due to its potential benefits, process mining has become more and more embedded in financial auditing as an analysis technique to support the auditor in their assessment of internal controls executed in financially relevant processes. However, standard process mining solutions for audit are developed under the pretense of a single case notion. As a result, an auditor is presented with models and data visualizations of the process that do not accurately reflect the underlying relationship between accounting and other relevant objects in the process, posing challenges for the auditor in obtaining a precise understanding of the process and related controls. In this case study together with EY, we aim to understand requirements for improving the application of process mining in audit. After first inventorizing the current limitations, we explore on a real-life audit use case provided by EY the benefits of graph-based event data representation using an event knowledge graph, especially considering accounting related objects and events. Discussing these results with auditing experts at EY revealed insights and requirements for a process mining analysis in the context of auditing not documented in the literature before.

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