Active Anomaly Detection for Key Item Selection in Process Auditing
Post, Ruben; Beerepoot, Iris; Lu, Xixi; Kas, Stijn; Wiewel, Sebastiaan; Koopman, Angelique; Reijers, Hajo
(2022)
Process Mining Workshops, volume 433, issue 1, pp. 167 - 179
Lecture Notes in Business Information Processing, volume 433, issue 1, pp. 167 - 179
3rd International Conference on Process Mining, ICPM 2021, volume 433, issue 1, pp. 167 - 179
(Part of book)
Abstract
Process mining allows auditors to retrieve crucial information about transactions by analysing the process data of a client. We propose an approach that supports the identification of unusual or unexpected transactions, also referred to as exceptions. These exceptions can be selected by auditors as “key items”, meaning the auditors wants
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to look further into the underlying documentation of the transaction. The approach encodes the traces, assigns an anomaly score to each trace, and uses the domain knowledge of auditors to update the assigned anomaly scores through active anomaly detection. The approach is evaluated with three groups of auditors over three cycles. The results of the evaluation indicate that the approach has the potential to support the decision-making process of auditors. Although auditors still need to make a manual selection of key items, they are able to better substantiate this selection. As such, our research can be seen as a step forward with respect to the usage of anomaly detection and data analysis in process auditing.
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Keywords: Anomaly Detection, Auditing, Domain Knowledge, Process Mining, Control and Systems Engineering, Management Information Systems, Business and International Management, Information Systems, Modelling and Simulation, Information Systems and Management
ISSN: 1865-1348
ISBN: 978-3-030-98580-6
978-3-030-98581-3
Publisher: Springer, Springer
Note: Publisher Copyright: © 2022, The Author(s).
(Peer reviewed)
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