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http://hdl.handle.net/1942/28124Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | JANS, Mieke | - |
| dc.contributor.author | HOSSEINPOUR, Mehrnush | - |
| dc.date.accessioned | 2019-05-03T10:22:37Z | - |
| dc.date.available | 2019-05-03T10:22:37Z | - |
| dc.date.issued | 2018 | - |
| dc.identifier.citation | International journal of accounting information systems, 32, p. 44-58 | - |
| dc.identifier.issn | 1467-0895 | - |
| dc.identifier.uri | http://hdl.handle.net/1942/28124 | - |
| dc.description.abstract | In the context of Continuous Auditing, different approaches have been proposed to incorporate data analytics to accomplish a continuous audit environment. Some work suggests the use of data mining, some the use of process mining; some work reports on concrete case studies, where other work presents a conceptual approach. In this paper, we present an actionable framework to address one specific level of continuous auditing: the transaction verification level. This framework combines the techniques of data mining and process mining on one hand, and includes the auditor as a human expert to deal with the typical alarm flood on the other hand. Further, different research opportunities are identified in this context. | - |
| dc.language.iso | en | - |
| dc.rights | 2018 Elsevier Inc. All rights reserved | - |
| dc.subject.other | Continuous auditing; Internal control testing; Process mining; Data mining; Active learning | - |
| dc.title | How active learning and process mining can act as Continuous Auditing catalyst | - |
| dc.type | Journal Contribution | - |
| dc.identifier.epage | 58 | - |
| dc.identifier.spage | 44 | - |
| dc.identifier.volume | 32 | - |
| local.bibliographicCitation.jcat | A1 | - |
| dc.description.notes | Jans, M (reprint author), Hasselt Univ, Martelarenlaan 42, B-3500 Hasselt, Belgium. mieke.jans@uhasselt.be | - |
| dc.relation.references | Adriansyah, A., Munoz-Gama, J., Carmona, J., van Dongen, B.F., van der Aalst, W.M.P., 2013. Alignment Based Precision Checking. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 137–149. Agrawal, R., Gunopulos, D., Leymann, F., 1998. Mining Process Models from Workflow Logs. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 467–483. AICPA, 2015. Rutgers and AICPA Unveil Data Analytics Research Initiative. Alles, M., Brennan, G., Kogan, A., Vasarhelyi, M.A., 2006. Continuous monitoring of business process controls: a pilot implementation of a continuous auditing system at Siemens. Int. J. Account. Inf. Syst. 7 (2), 137–161. Alles, M.G., Kogan, A., Vasarhelyi, M.A., 2008. Putting continuous auditing theory into practice: lessons from two pilot implementations. J. Inf. Syst. 22 (2), 195–214. Borthick, A.F., 2012. Designing continuous auditing for a highly automated procure-to-pay process. J. Inf. Syst. 26 (2), 153–166. Brown-Liburd, H., Issa, H., Lombardi, D., 2015. Behavioral implications of big data's impact on audit judgment and decision making and future research directions. Account. Horiz. 29 (2), 451–468. Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P., 2012. On the Role of Fitness, Precision, Generalization and Simplicity in Process Discovery. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 305–322. Calvanese, D., Montali, M., Syamsiyah, A., Aalst, W.M.P.v.d., 2015, Aug, Aug. Ontology-driven Extraction of Event Logs from Relational Databases. In: Business Process Management Workshops. Lecture Notes in Business Information Processing. Springer, Cham, pp. 140–153. Chan, D.Y., Vasarhelyi, M.A., 2011. Innovation and practice of continuous auditing. Int. J. Account. Inf. Syst. 12 (2), 152–160. Cook, J.E., Wolf, A.L., 1998. Discovering models of software processes from event-based data. ACM Trans. Softw. Eng. Methodol. (TOSEM) 7 (3), 215–249. de Leoni, M., van der Aalst, W.M.P., 2013. Aligning Event Logs and Process Models for Multi-perspective Conformance Checking: An Approach Based on Integer Linear Programming. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 113–129. De Medeiros, A.K.A., Weijters, A.J., van der Aalst, W.M.P., 2007. Genetic process mining: an experimental evaluation. Data Min. Knowl. Disc. 14 (2), 245–304. Debreceny, R., Gray, G.L., Tham, W.-L., Goh, K.-Y., Tang, P.-L., 2003. The development of embedded audit modules to support continuous monitoring in the electronic commerce environment. Int. J. Audit. 7 (2), 169–185. Depaire, B., Swinnen, J., Jans, M., Vanhoof, K., 2013. A process deviation analysis framework. In: Rosa, M., Soffer, P. (Eds.), Business Process Management Workshops: BPM 2012 International Workshops, Tallinn, Estonia, September 3, 2012. Revised Papers. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 701–706. Friedrich, F., Mendling, J., Puhlmann, F., 2011. Process model generation from natural language text. In: Mouratidis, H., Rolland, C. (Eds.), Advanced Information Systems Engineering: 23rd International Conference, CAiSE 2011, London, UK, June 20–24, 2011. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 482–496. González López de Murillas, E., Aalst, W.M.P., Reijers, H.A., 2015. Process mining on databases: unearthing historical data from redo logs. In: Motahari-Nezhad, R.H., Recker, J., Weidlich, M. (Eds.), Business Process Management: 13th International Conference, BPM 2015, Innsbruck, Austria, August 31–September 3, 2015, Proceedings. Springer International Publishing, Cham, pp. 367–385. González López de Murillas, E., Reijers, H.A., van der Aalst, W.M.P., 2016. Connecting databases with process mining: a meta model and toolset. In: Schmidt, R., Guédria, W., Bider, I., Guerreiro, S. (Eds.), Enterprise, Business-process and Information Systems Modeling: 17th International Conference, BPMDS 2016, 21st International Conference, EMMSAD 2016, Held at CAiSE 2016, Ljubljana, Slovenia, June 13–14,2016, Proceedings. Springer International Publishing, Cham, pp. 231–249. Groomer, S.M., Murthy, U.S., 1989. Continuous auditing of database applications: an embedded audit module approach. J. Inf. Syst. 3 (2), 53. Günther, C.W., Van Der Aalst, W.M.P., 2007. Fuzzy mining-adaptive process simplification based on multi-perspective metrics. In: Business Process Management. Springer, pp. 328–343. Hosseinpour, M., Jans, M., 2016. Categorizing identified deviations for auditing. In: Ceravolo, P., Guetl, C., Rinderle-Ma, S. (Eds.), Proceedings of the 6th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2016). number 1757 in CEUR Workshop Proceedings. pp. 125–129. http://ceur-ws.org/Vol- 1757. Huang, S.-M., Yen, D.C., Hung, Y.-C., Zhou, Y.-J., Hua, J.-S., 2009. A business process gap detecting mechanism between information system process flow and internal control flow. Decis. Support. Syst. 47, 436–454. Jans, M., 2017. Auditor Choices During Event Log Building for Process Mining. In: Working paper. Hasselt University. Jans, M., van der Werf, J.M., Lybaert, N., Vanhoof, K., 2011. A business process mining application for internal transaction fraud mitigation. Expert Syst. Appl. 38 (10), 13351–13359. Jans, M., Alles, M., Vasarhelyi, M., 2013. The case for process mining in auditing: sources of value added and areas of application. Int. J. Account. Inf. Syst. 14 (1), 1–20. Jans, M., Alles, M.G., Vasarhelyi, M.A., 2014. A field study on the use of process mining of event logs as an analytical procedure in auditing. Account. Rev. 89 (5), 1751–1773. Jans, M., Soffer, P., Jouck, T., 2017. Learning to Build a Valuable Event Log. In: Working paper. Hasselt University. Janssenswillen, G., Donders, N., Jouck, T., Depaire, B., 2017. A comparative study of existing quality measures for process discovery. Inf. Syst. 71, 1–15. M. Jans, M. Hosseinpour International Journal of Accounting Information Systems xxx (xxxx) xxx–xxx 14 Janssenswillen, G., Jouck, T., Creemers, M., Depaire, B., 2016. Measuring the quality of models with respect to the underlying system: an empirical study. In: La Rosa, M., Loos, P., Pastor, O. (Eds.), Business Process Management: 14th International Conference, BPM 2016, Rio de Janeiro, Brazil, September 18–22, 2016. Proceedings. Springer International Publishing, Cham, pp. 73–89. Krahel, J.P., Titera, W.R., 2015. Consequences of big data and formalization on accounting and auditing standards. Account. Horiz. 29 (2), 409–422. Kuhn Jr., J.R., Sutton, S.G., 2010. Continuous auditing in ERP system environments: the current state and future directions. J. Inf. Syst. 24 (1), 91–112. Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P., 2013. Discovering block-structured process models from event logs—a constructive approach. In: Application and Theory of Petri Nets and Concurrency. Springer, pp. 311–329. Li, P., Chan, D.Y., Kogan, A., 2016. Exception prioritization in the continuous auditing environment: a framework and experimental evaluation. J. Inf. Syst. 30 (2), 135–157. Lu, X., Nagelkerke, M.Q.L., van de Wiel, D., Fahland, D., 2015. Discovering interacting artifacts from ERP systems (extended version). In: BPM Reports. 1508. Lyytinen, K., Mathiassen, L., Ropponen, J., Datta, A., 1998. Automating the discovery of as-is business process models: probabilistic and algorithmic approaches. Info. Sys. Research 9 (3), 275–301. Moffitt, K.C., Richardson, V.J., Snow, N.M., Weisner, M.M., Wood, D.A., 2016. Perspectives on past and future AIS research as the Journal of Information Systems turns thirty. J. Inf. Syst. 30 (3), 157–171. Munoz-Gama, J., 2014. Conformance Checking and Diagnosis in Process Mining. PhD thesis. Universitat Politecnica de Catalunya - BarcelonaTech. Nápoles, G., Grau, I., Papageorgiou, E., Bello, R., Vanhoof, K., 2016. Rough cognitive networks. Knowl.-Based Syst. 91, 46–61. Nápoles, G., Falcon, R., Papageorgiou, E., Bello, R., Vanhoof, K., 2017. Rough cognitive ensembles. Int. J. Approx. Reason. 85, 79–96. Nápoles, G., Mosquera, C., Falcon, R., Grau, I., Bello, R., Vanhoof, K., 2018. Fuzzy-rough cognitive networks. Neural Netw. 97, 19–27. Nguyen, H., Dumas, M., Rosa, M., Maggi, F.M., Suriadi, S., 2014. Mining business process deviance: a quest for accuracy. In: Meersman, R., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (Eds.), On the Move to Meaningful Internet Systems: OTM 2014 Conferences: Confederated International Conferences: CoopIS, and ODBASE 2014, Amantea, Italy, October 27–31, 2014, Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 436–445. O’Donnell, E., Schultz Jr., J.J., 2003. The influence of business-process-focused audit support software on analytical procedures judgments. Audit. J. Pract. Theory 22 (2), 265–279. Pawlak, Z., 1998. Rough set theory and its applications to data analysis. Cybern. Syst. 29 (7), 661–688. Perols, J.L., Murthy, U.S., 2012. Information fusion in continuous assurance. J. Inf. Syst. 26 (2), 35–52. Pika, A., van der Aalst, W.M.P., Wynn, M.T., Fidge, C.J., ter Hofstede, A.H.M., 2016. Evaluating and predicting overall process risk using event logs. Inf. Sci. 352–353, 98–120. Rozinat, A., van der Aalst, W.M.P., 2008. Conformance checking of processes based on monitoring real behavior. Inf. Syst. 33 (1), 64–95. Schultz, M., 2013. Enriching Process Models for Business Process Compliance Checking in ERP Environments. In: Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 120–135. Shafer, G., Srivastava, R., 1990. The Bayesian and belief-function formalisms: a general perspective for auditing. Audit. J. Pract. Theory 9, 110–137. Sonnenberg, C., Brocke, J. v., 2014. The missing link between BPM and accounting: using event data for accounting in process-oriented organizations. Bus. Process. Manag. J. 20 (2), 213–246. Srivastava, R.P., Shafer, G.R., 1992. Belief-function formulas for audit risk. Account. Rev. 67 (2), 249–283. Swinnen, J., Depaire, B., Jans, M., Vanhoof, K., 2011. A process deviation analysis, a case study. In: Daniel, F., Barkaoui, K., Dustdar, S. (Eds.), Business Process Management Workshops. Lecture Notes in Business Information Processing 99. Springer Berlin Heidelberg, pp. 87–98. Titera, W.R., 2013. Updating audit standard-enabling audit data analysis. J. Inf. Syst. 27 (1), 325–331. van der Aa, H., Leopold, H., Reijers, H.A., 2015. Detecting inconsistencies between process models and textual descriptions. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (Eds.), Business Process Management: 13th International Conference, BPM 2015, Innsbruck, Austria, August 31–September 3, 2015, Proceedings. Springer International Publishing, Cham, pp. 90–105. van der Aalst, W.M.P., 2016. Process Mining: Data Science in Action. Springer, Heidelberg. van der Aalst, W.M.P., Buijs, J., Van Dongen, B., 2012. Towards improving the representational bias of process mining. In: Data-driven Process Discovery and Analysis. Springer, pp. 39–54. van der Aalst, W.M.P., de Beer, H.T., van Dongen, B.F., 2005. Process Mining and Verification of Properties: An Approach Based on Temporal Logic. In: Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 130–147. van der Aalst, W.M.P., Dongen, B.F.v., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M., 2003. Workflow mining: a survey of issues and approaches. Data Knowl. Eng. 47 (2), 237–267. van der Aalst, W.M.P., van Hee, K.M., van der Werf, J.M., Verdonk, M., 2010. Auditing 2.0: using process mining to support tomorrow's auditor. Computer 43 (3), 90–93. van der Aalst, W.M.P., Weijters, T., Maruster, L., 2004. Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16 (9), 1128–1142. Vasarhelyi, M., Alles, M., Kogan, A., 2004. Principles of analytic monitoring for continuous assurance. J. Emerging Technol. Account. 1, 1–21. Vasarhelyi, M.A., Halper, F.B., 1991. The continuous audit of online systems. Audit. J. Pract. Theory 10 (1), 110–125. Warren, J.J.D., Moffitt, K.C., Byrnes, P., 2015. How big data will change accounting. Account. Horiz. 29 (2), 397–407. Weerdt, J.D., Backer, M.D., Vanthienen, J., Baesens, B., 2012. A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37 (7), 654–676. Weijters, A.J.M.M., van Der Aalst, W.M.P., De Medeiros, A.K.A., 2006. Process mining with the heuristics miner-algorithm. In: BETA Working paper series TU/e 166, pp. 1–34. Werner, M., 2017. Financial process mining — accounting data structure dependent control flow inference. Int. J. Account. Inf. Syst. 25, 57–80. Yao, Y., 2009. Three-way Decision: An Interpretation of Rules in Rough Set Theory. In: Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 642–649. Zadeh, L., 1965. Fuzzy sets. Inf. Control. 8 (3), 338–353. http://www.sciencedirect.com/science/article/pii/S001999586590241X. | - |
| local.type.refereed | Refereed | - |
| local.type.specified | Article | - |
| dc.identifier.doi | 10.1016/j.accinf.2018.11.002 | - |
| dc.identifier.isi | 000468259600004 | - |
| item.validation | ecoom 2020 | - |
| item.fulltext | With Fulltext | - |
| item.accessRights | Open Access | - |
| item.fullcitation | JANS, Mieke & HOSSEINPOUR, Mehrnush (2018) How active learning and process mining can act as Continuous Auditing catalyst. In: International journal of accounting information systems, 32, p. 44-58. | - |
| item.contributor | JANS, Mieke | - |
| item.contributor | HOSSEINPOUR, Mehrnush | - |
| crisitem.journal.issn | 1467-0895 | - |
| crisitem.journal.eissn | 1873-4723 | - |
| Appears in Collections: | Research publications | |
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| How_active_learning_and_process_mining_can_act_as_Continuous_Auditing_catalyst.pdf | Peer-reviewed author version | 686.71 kB | Adobe PDF | View/Open |
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