Abstract
Today the challenge facing every company is the enormous quantity of data being captured, at yearly, monthly, weekly, daily and hourly levels and how this data may be used. Despite the amount of data often this data is limited regarding company processes and their analysis. This can be solved by preprocessing data, after its quality evaluation, for process mining activities. Prepared data can be used for data dimensions’ coverage and having dimension members filled financial analyst may analyze this data from different perspectives for discovery of certain patterns, anomalies and frauds. This paper presents primary results of data cube dimensions fill according data of real organizations General Ledger information. Provided examples help to illustrate the possibility cover majority dimension members and give material for further researches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aalst, W.V.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, 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.) BPM 2012. LNBIP, vol. 132, pp. 217–218. Springer, Heidelberg (2013)
Alrefai, A.: Audit focused process mining: the evolution of process mining and internal control. Ph.D. 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 (2007). 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. Account. 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. Account. Inf. Syst. 24, 32–58 (2017)
Gally, F., Geerts, G.: Business process modeling: an accounting information systems perspective. Int. J. Account. 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. In: 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. Account. Lit. 40, 102–115 (2018)
Mamaliga, T.: Realizing a process cube allowing for the comparison of event data. Master thesis. Eindhoven University of Technology (2013)
Mieke, J., Alles, M., Vasarhelyi, M.: The case for process mining in auditing: sources of value added and areas of application. Int. J. Account. Inf. Syst. 14(1), 1–20 (2013). https://doi.org/10.1016/j.accinf.2012.06.015
Werner, M., Gehrke, N., 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. Account. Inf. Syst. 25, 57–80 (2017). https://doi.org/10.1016/j.accinf.2017.03.004
Acknowledgments
This paper presents primary results of 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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Lopata, A. et al. (2021). Financial Data Preprocessing Issues. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2021. Communications in Computer and Information Science, vol 1486. Springer, Cham. https://doi.org/10.1007/978-3-030-88304-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-88304-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88303-4
Online ISBN: 978-3-030-88304-1
eBook Packages: Computer ScienceComputer Science (R0)