Abstract
The purpose of this paper is to present continuous results of the research in financial data analysis. Many organizations face challenges by processing a colossal quantity of financial data for evaluation of the current state of the organization, for analysis of future strategies and other purposes. One of the possible ways to analyse financial data is to use process mining techniques. This paper proceeds with analysis and usage of financial data cubes dimensions using General Ledger information of particular organizations in the Netherlands. The research project is funded by European Regional Development Fund according to the 2014–2020 Operational Programme for the European Union Funds’ Investments under measure No. 01.2.1-LVPA-T-848 “Smart FDI”. Project no.: 01.2.1-LVPA-T-848–02-0004; Period of project implementation: 2020–06-01–2022–05-31.
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References
van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19345-3
Aalst, W.M.P.: Process cubes: slicing, dicing, rolling up and drilling down event data for process mining. In: Song, M., Wynn, M.T., Liu, J. (eds.) AP-BPM 2013. LNBIP, vol. 159, pp. 1–22. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02922-1_1
Aalst, W.V., Kees, M.V., Werf, J.M.V., Verdonk, M.: Finance process mining auditing 2.0: using process mining to support tomorrow’s auditor. Computer 43(3), (2010) http://www.padsweb.rwth-aachen.de/wvdaalst/publications/p593.pdf
Adriansyah, A., Buijs, J.C.A.M.: Mining process performance from event logs. In: La Rosa, M., Soffer, P. (eds.) Business Process Management Workshops, pp. 217–218. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36285-9_23
Abdulrahman, A.: Audit focused process mining: the evolution of process mining and internal control. PhD Thesis (2019). https://rucore.libraries.rutgers.edu/rutgers-lib/60514/PDF/1/play/
Das, K., Schneider, J.: Detecting anomalous records in categorical datasets. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge Discovery and Data Mining August 2007 pp.220–229 https://doi.org/10.1145/1281192.1281219
Earley, C.E.: Data analytics in auditing: opportunities and challenges. Bus. Horiz. 58, 493—500 (2015)
Debreceny, R.S., Gray, G.L.: Data mining journal entries for fraud detection: an exploratory study. Int. J. Acc. Inf. Syst. 11(3), 157–181 (2010)
Amani, F.A., Fadlalla, A.M.: Data mining applications in accounting: A review of the literature and organizing framework. Int. J. Acc. Inf. Syst. 24, 32–58 (2017)
Frederik, G., Guido, G.: Business process modeling: an accounting information systems perspective. Int. J. Acc. Inf. Syst. 15(3), 185–192 (2014). https://doi.org/10.1016/j.accinf.2014.08.001
Gehrke, N., Mueller-Wickop, N.: Basic principles of financial process mining a journey through financial data in accounting information systems. Association for Information Systems AIS Electronic Library (AISeL) (2010)
Gepp, A., Linnenluecke, M.K., O’Neill, T.J., Smith, T.: Big data techniques in auditing research and practice: current trends and future opportunities. J. Acc. Lit. 40, 102–115 (2018)
Gosselin, M.: An empirical study of performance measurement in manufacturing firms. Int. J. Product. Perform. Manag. 54(5/6), 419–437 (2005). https://doi.org/10.1108/17410400510604566
vom Brocke, J., Rosemann, M. (eds.): Handbook on Business Process Management 2. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-01982-1
Lopata, A., et al.: Financial data preprocessing Issues. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds.) ICIST 2021. CCIS, vol. 1486, pp. 60–71. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88304-1_5
Mamaliga, T.: Realizing a process cube allowing for the comparison of event data. Master Thesis. Eindhoven University of Technology (2013)
Mieke, J., Alles, M., and Vasarhelyi, M.: The case for process mining in auditing: sources of value added and areas of application. Int. J. Acc. Inf. Syst. 14(1), 1–20 (2013). https://doi.org/10.1016/j.accinf.2012.06.015.
OLAP Council, OLAP: On-Line Analytical Processing. http://www.olapcouncil.org/research/glossaryly.htm Accessed 21 Feb 2022
Werner, M., Gehrke, N., and Nuttgens, M.: Business process mining and reconstruction for financial audits. In: 45th Hawaii International Conference on System Sciences, pp. 5350–5359 (2012). https://doi.org/10.1109/HICSS.2012.141
Werner, M.: Financial process mining - accounting data structure dependent control flow inference. Int. J. Acc. Inf. Syst. 25, 57–80 (2017). https://doi.org/10.1016/j.accinf.2017.03.004
Acknowledgments
This paper presents the primary results of the research project “Enterprise Financial Performance Data Analysis Tools Platform (AIFA)”. The research project is funded by European Regional Development Fund according to the 2014–2020 Operational Programme for the European Union Funds’ Investments under measure No. 01.2.1-LVPA-T-848 “Smart FDI”. Project no.: 01.2.1-LVPA-T-848–02-0004; Period of project implementation: 2020–06-01 – 2022–05-31.
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Lopata, A. et al. (2022). Financial Process Mining Characteristics. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2022. Communications in Computer and Information Science, vol 1665. Springer, Cham. https://doi.org/10.1007/978-3-031-16302-9_16
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