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28-07-2022

A Comprehensive Study on Implementing Big Data in the Auditing Industry

Authors: Salonee Patel, Manan Shah

Published in: Annals of Data Science | Issue 3/2023

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Abstract

In this study, the implementation of big data techniques in auditing processes is investigated, and it is determined that the practise is not as widespread as it is in other comparable industries. Firstly, we introduce bid data and its technological evolution to promote better understanding of its potential usage. Following that, the research discusses the importance of implementing big data techniques in auditing and also enumerates the existing big data tools that the corporates currently use. Next, we focus on the impact of implementing Big Data Analytics (BDA) in Auditing and the common problems of integrating BDA in the audit context. In addition, we go through existing research on big data approaches and analyse how big data is being used in auditing procedures. Further, the research focuses on the perspectives of the Big Four Auditing firms on the implementation of Big Data technologies in today's auditing environment and its future possibilities. Moreover, this study also looks at the growth of the bid data and auditing industries globally from a quantitative standpoint. Regression analysis is used in conjunction with a moving average estimate for both the industries for the year 2021. This was done in order to quantify the auditing industry's reliance on bid data technology for efficiency. After thorough research a conclusion was made that; in terms of the usage of valuable big data approaches, auditing lags behind the other industries. Possible explanation for a lower acceptance to big data technologies lies in the challenges of implementing bid data. A positive outlook towards bid data technologies can be expected after overcoming the challenges associated with its implementation. Hence, we recommend that further research in this area is necessary. The empirical findings of the study can be used to understand the challenges of implementing big data technologies and can be set as a research agenda for future research on the subject.

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Hazaea SA, Tabash MI, Khatib SFA, Zhu J-KAA (2020) The impact of internal audit quality on financial performance of Yemeni commercial banks: an empirical investigation. J Asian Financ Econ Bus 7:867–875. https://doi.org/10.13106/JAFEB.2020.VOL7.NO11.867CrossRef

Chae S-J, Nakano M, Fujitani R (2020) Financial reporting opacity, audit quality and crash risk: evidence from Japan. J Asian Financ Econ Bus 7:9–17. https://doi.org/10.13106/jafeb.2020.vol7.no1.9CrossRef

George D, Theofanis K, Konstantinos A (2015) Factors associated with internal audit effectiveness: evidence from Greece. J Account Tax 7:113–122. https://doi.org/10.5897/JAT2015.0182CrossRef

Carpenter R, McGregor D (2020) The implications, applications, and benefits of emerging technologies in audit. Bus Manag Rev 11:36–44. https://doi.org/10.24052/bmr/v11nu02/art-05CrossRef

Magablih AM (2019) Impact of using technology in auditing on reducing the fees of auditors offices and companies in Jordan. Int J Bus Manag 14:p1. https://doi.org/10.5539/IJBM.V14N8P1CrossRef

Ślusarczyk B (2018) Industry 40: Are we ready? Polish J Manag Stud 17:232–248. https://doi.org/10.17512/pjms.2018.17.1.19CrossRef

Veerankutty F, Ramayah T, Ali NA (2018) Information technology governance on audit technology performance among Malaysian public sector auditors. Soc Sci 7:124. https://doi.org/10.3390/SOCSCI7080124CrossRef

European Commission (2016) Big Data and artificial intelligence-eu-patient.eu. EU Policy Briefing for Patient Organisations. https://www.eu-patient.eu/globalassets/policy/epf---big-data-and-artificial-intelligence---eu-policy-briefing-for-patient-organisations-april-2020.pdf

Gepp A, Linnenluecke MK, O’Neill TJ, Smith T (2018) Big data techniques in auditing research and practice: current trends and future opportunities. J Account Lit 40:102–115. https://doi.org/10.1016/j.acclit.2017.05.003CrossRef

10.

Sharma N, Yadav S, Mangla M, et al (2020) Multivariate analysis of COVID-19 on stock, commodity & purchase manager indices: a global perspective. ResearchSquare 1–15

11.

Reichl A, Schneider G, Schliepdiek T, Reimuth O (2014) Carbon capture and storage for enhanced oil recovery: integration and optimization of a post-combustion CO2-capture facility at a power plant in Abu Dhabi. Soc Pet Eng Oil Gas Facil 1:112–124. https://doi.org/10.2118/171692-msCrossRef

12.

Cao R, Liang X, Ni Z (2012) Stock price forecasting with support vector machines. Springer, Berlin, pp 527–538

13.

Alles MG (2015) Drivers of the use and facilitators and obstacles of the evolution of big data by the audit profession. Account Horizons 29:439–449. https://doi.org/10.2308/acch-51067CrossRef

14.

Song XP, Hu ZH, Du JG, Sheng ZH (2014) Application of machine learning methods to risk assessment of financial statement fraud: evidence from China. J Forecast 33:611–626. https://doi.org/10.1002/for.2294CrossRef

15.

Amit S, Thambi G, Rao M (2017) Inter-comparison of artificial neural network algorithms for time series forecasting: predicting Indian financial markets. Int J Comput Appl 162:1–13. https://doi.org/10.5120/ijca2017913249CrossRef

16.

Singleton (2013b) What every IT auditor should know about transforming data for CAATS. ISACA 5:1–2

17.

Shen S, Jiang H, Zhang T (2012) Stock market forecasting using machine learning algorithms

18.

CapGemini (2019) Reinventing cybersecurity with artificial intelligence

19.

Hsu C-H, Manogaran G, Panchatcharam P, Vivekanandan S (2018) A new approach for prediction of lung carcinoma using back propagation neural network with decision tree classifiers. In: 2018 IEEE 8th international symposium on cloud and service computing (SC2). IEEE, pp 111–115

20.

Brown D, Brown ZE (2008) The relationship between investor attachment style and financial advisor loyalty. J Behav Financ 9:232–239. https://doi.org/10.1080/15427560802540100CrossRef

21.

Tong DYK, Sivanand CN (2005) E-recruitment service providers review: international and Malaysian. Empl Relat 27:103–117CrossRef

22.

West D, Mangiameli P, Rampal R, West V (2005) Ensemble strategies for a medical diagnostic decision support system: a breast cancer diagnosis application. Eur J Oper Res 162:532–551. https://doi.org/10.1016/j.ejor.2003.10.013CrossRef

23.

Hasan M, Maijoor S, Mock TJ et al (2005) The different types of assurance services and levels of assurance provided. Int J Audit 9:91–102. https://doi.org/10.1111/J.1099-1123.2005.00262.XCrossRef

24.

Coram P, Ferguson C, Moroney R (2006) The value of internal audit in fraud detection. In: Paper presented at the 2011 AFAANZ conference, vol 48, pp 1–32

25.

William JW (2013) Regulatory technologies, risky subjects, and financial boundaries: Governing “fraud” in the financial markets. Acc Organ Soc 38(6-7):544–558CrossRef

26.

Alles M, Gray GL, (2016) Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors. International J Acc Info Syst 22:44–59. https://doi.org/10.1016/j.accinf.2016.07.004CrossRef

27.

Gartner (2016) Definition of big data-gartner information technology glossary. https://www.gartner.com/en/information-technology/glossary/big-data

28.

IBR (2018a) Data Analytics: The future of Audit. https://www.ibr-ire.be/docs/default-source/fr/Documents/reglementation-etpublications/publications/brochures/generalites/IBR_DataAnalytics_18-0112_r04.pdf

29.

Cao M, Chychyla R, Stewart T (2015) Big Data Analytics in Financial Statement Audits. Accounting Horizons 29(2):423–429. https://doi.org/10.2308/acch-51068CrossRef

30.

Ramlukan (2015) How Big Data and Analytics Are Transforming the Audit. https://daily.financialexecutives.org/how-big-data-and-analytics-are-transforming-theaudit/

31.

Salijeni GM (2019) Big data analytics and the social relevance of auditing: an exploratory study. Thesis, 1–262. https://www.researchgate.net/figure/Conceptualising-BDA-in-Auditing-Alles-and-Gray-2016-p-45_fig1_331014291

32.

Dai J, Vasarhelyi MA (2016) Imagineering Audit 4.0. J Emerg Technol Acc 13(1):1–15. https://doi.org/10.2308/jeta-10494

33.

Solanki P, Baldaniya D, Jogani D et al (2021) Artificial intelligence: New age of transformation in petroleum upstream. Pet Res. https://doi.org/10.1016/j.ptlrs.2021.07.002CrossRef

34.

A Azaria A Ekblaw T Vieira A Lippman 2016 MedRec: using blockchain for medical data access and permission management. In: 2016 2nd international conference on open and big data OBD, 2016, 25–30. https://doi.org/10.1109/OBD.2016.11

35.

Shukla K, Kumar P, Mann GS, Khare M (2020) Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi. Sustain Cities Soc 54:101997. https://doi.org/10.1016/j.scs.2019.101997CrossRef

36.

Chaajer P, Kshirsagar A, Shah M (2021) A comprehensive study of artificial neural network (ANN) and support vector machines (SVM) and long short-term memory (LSTM) on stock forecasting. Decis Anal

37.

Ahn JJ, Oh KJ, Kim TY, Kim DH (2011) Usefulness of support vector machine to develop an early warning system for financial crisis. Expert Syst Appl 38:2966–2973. https://doi.org/10.1016/J.ESWA.2010.08.085CrossRef

38.

Zheng Z, Xie S, Dai H, et al (2017) An overview of blockchain technology: architecture, consensus, and future trends. In: Proceedings of the 2017 IEEE 6th International Congress on Big Data, 2017. pp 557–564. https://doi.org/10.1109/BigDataCongress.2017.85

39.

Apparao G, Singh A, Bl B et al (2009) Financial statement fraud detection by data mining. Int J Adv Netw Appl 159:159–163

40.

Simon S, Raoot A, Professor A (2012) Accuracy driven artificial neural networks in stock market prediction. Int J Soft Comput. https://doi.org/10.5121/ijsc.2012.3203CrossRef

41.

Albizri A, Appelbaum D, Rizzotto N (2019) Evaluation of financial statements fraud detection research: a multi-disciplinary analysis. Palgrave Macmillan UK

42.

Brown-Liburd H, Vasarhelyi MA (2015) Big Data and Audit Evidence. J Emerg Technol Acc 12(1):1–16CrossRef

43.

Tang J, Karim KE (2019) Financial fraud detection and big data analytics: implications on auditors’ use of fraud brainstorming session. Manag Audit J 34:324–337. https://doi.org/10.1108/MAJ-01-2018-1767CrossRef

44.

Earley CE (2015) Data analytics in auditing: opportunities and challenges. Bus Horiz 58:493–500. https://doi.org/10.1016/J.BUSHOR.2015.05.002CrossRef

45.

Kostic N, Tang X (2017) The future of audit: examining the opportunities and challenges stemming from the use of big data analytics and blockchain technology in audit practice. J Chem Inf Model 53:1689–1699

46.

Tang F, Norman CS, Vendrzyk VP (2017) Exploring perceptions of data analytics in the internal audit function. Behav Info Technol 36(11):1125–1136. https://doi.org/10.1080/0144929X.2017.1355014CrossRef

47.

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 Horizons 29:451–468. https://doi.org/10.2308/acch-51023CrossRef

48.

Appelbaum D (2016) Securing big data provenance for auditors: the big data provenance black box as reliable evidence. J Emerg Technol Acc 13(1):17–36. https://doi.org/10.2308/jeta-51473CrossRef

49.

FEI (2015) How big data and analytics are transforming the audit - FEI. https://daily.financialexecutives.org/how-big-data-and-analytics-are-transforming-the-audit/. Accessed 5 May 2022

50.

Krahel JP, Titera WR (2015) Consequences of big data and formalization on accounting and auditing standards. Account Horizons 29:409–422. https://doi.org/10.2308/acch-51065CrossRef

51.

Appelbaum D, Kogan A, Vasarhelyi M, Yan Z (2017) Impact of business analytics and enterprise systems on managerial accounting. Int J Account Inf Syst 25:29–44. https://doi.org/10.1016/j.accinf.2017.03.003CrossRef

52.

Cao M, Li A, Wei JZ (2003) Weather derivatives: a new class of financial instruments. SSRN Electron J. https://doi.org/10.2139/SSRN.1016123CrossRef

Title: A Comprehensive Study on Implementing Big Data in the Auditing Industry
Authors: Salonee Patel
Manan Shah
Publication date: 28-07-2022
Publisher: Springer Berlin Heidelberg
Published in: Annals of Data Science / Issue 3/2023
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-022-00430-8

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