Journal of Technologies Information and Communication

Journal of Technologies Information and Communication, 2020 - Volume 1 Issue 1, pp. 37-48

Published Online: 16 Jun 2020

Views: 128 | Downloads: 128

The way data is handled is radically changing the management and organizational processes. Nowadays the amount of information available is innumerable and is still growing, machines produce more useful information than humans and we can experience it through the Big Data phenomenon.This dissertation addresses the concept of Big Data and its applicability in financial auditing. The literature review allows us to analyse the advantages of applying Big Data in financial audit. Based on the empirical study developed, we will present the challenges that auditors will have to maximize these benefits in their audit process and the possible obstacles that auditors may face when using Big Data.The empirical study was based on qualitative and quantitative analysis, through inquiry and interviews. This allowed us to conclude that Big Data is fundamental in financial auditing, as it makes the auditor’s work more efficient, intelligent and effective, however our sample lead us also to the conclusion that auditors in Portugal are not prepared to incorporate Big Data in financial auditing.

Big Data Financial Audit

• [1] Appelbaum, D., Kogan, A., & Vasarhelyi, M. (2017). Big Data and Analytics in the Modern Audit Engagement: Research Needs. AUDITING: A Journal of Practice & Theory, pp. 1-27. Obtido em 30 de 06 de 2019, de https://www.aaajournals.org/doi/abs/10.2308/ajpt-51684
• [2] Lohr, S. (2012). Opinion | Big Data’s Impact in the World. The New York Times, 11(1), 1. Obtido em 24 de 06 de 2019, de https://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html
• [3] Lucas, S. (2012). SAP HANA. Obtido em 28 de 05 de 2019, de Blog do SAP: https://blogs.saphana.com/2012/08/21/beyond-the-balance-sheet-run-your-business-on-new-signals-in-the-age-of-big-data/
• [4] Dobbs, R., Manyika, J., Roxburgh, C., & Lund, S. (2011). Big Data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, p. 146. Obtido em 15 de 06 de 2019, de https://bigdatawg.nist.gov/pdf/MGI_big_data_full_report.pdf
• [5] Cukier, K., & Mayer-Schoenberger, V. (2013). The Rise of Big Data. Foreign Affairs, 28-40. Obtido em 21 de 07 de 2019, de https://www.foreignaffairs.com/articles/2013-04-03/rise-big-data
• [6] McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, p. 9. Obtido em 15 de 06 de 2019, de http://tarjomefa.com/wp-content/uploads/2017/04/6539-English-TarjomeFa-1.pdf
• [7] Tabesh, P., Mousavidin, E., & Hasani, S. (2019). Implementing Big Data strategies: A managerial perspective. Business Horizons, pp. 347-358. Obtido em 29 de 09 de 2019, de http://www.sciencedirect.com/science/article/pii/S000768131930028X
• [8] Alles, M., & Gray, G. (2016). Incorporating Big Data in audits. International Journal of Accounting Information Systems, 22(1), 44-59. Obtido em 11 de 05 de 2019, de http://www.sciencedirect.com/science/article/pii/S1467089516300811
• [9] Connolly, S. (2012). Hortonworks. Obtido em 15 de 06 de 2019, de 7 Key Drivers for the Big Data Marke: https://br.hortonworks.com/blog/7-key-drivers-for-the-big-data-market/
• [10] Davenport, T., Barth, P., & Bean, R. (2012). How ‘Big Data’ Is Different. Mitsloan - Management Review, 54(1), 1-5. Obtido em 26 de 05 de 2019, de https://www.hbs.edu/faculty/Pages/item.aspx?num=43026
• [11] Press, G. (2017). 6 Predictions For The $203 Billion Big Data Analytics Market. Obtido em 30 de 09 de 2019, de Forbes: https://www.forbes.com/sites/gilpress/2017/01/20/6-predictions-for-the-203-billion-big-data-analytics-market/
• [12] Mazzei, M., & Noble, D. (2017). Big Data dreams: A framework for corporate strategy. Business Horizons, pp. 405-414. Obtido em 30 de 09 de 2019, de http://www.sciencedirect.com/science/article/pii/S0007681317300101
• [13] Alles, M. (2015). Drivers of the Use and Facilitators and Obstacles of the Evolution of Big Data by the Audit Profession. Accounting Horizons, 29(2), 439-449. Obtido em 02 de 06 de 2019, de http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=103541033&site=ehost-live&scope=site
• [14] Titera, W. (2013). Updating Audit Standard--Enabling Audit Data Analysis. Journal of Information Systems, 27(1), 325-331. Obtido em 14 de 07 de 2019, de http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=88181973&lang=pt-pt&site=ehost-live&scope=site
• [15] 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. Accounting Horizons, 451-468. Obtido em 10 de 06 de 2019, de http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=103541029&site=ehost-live&scope=site
• [16] Bell, T., Peecher, M., & Solomon, I. (2005). The 21st Century Public Company Audit. Conceptual Elements of KPMG’s Global Audit Methodology, p. 96. Obtido em 14 de 07 de 2019, de http://www.business.illinois.edu/kpmg-uiuccases/monograph2.pdf
• [17] Yoon, K., Hoogduin, L., & Zhang, L. (2015). Big Data as Complementary Audit Evidence. Accounting Horizons, pp. 431-438. Obtido em 25 de 06 de 2019, de http://aaajournals.org/doi/10.2308/acch-51076
• [18] Humpherys, S., Moffitt, K., Burns, M., Burgoon, J., & Felix, W. (2011). Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems, pp. 585-594. Obtido em 21 de 06 de 2019, de http://www.sciencedirect.com/science/article/pii/S0167923610001338
• [19] Littley, J. (2012). KPMG LLP. Obtido em 16 de 06 de 2019, de Leveraging data analytics and continuous auditing processes: https://assets.kpmg/content/dam/kpmg/pdf/2016/05/Leveraging-Data-Analytics.pdf
• [20] Gray, G., & Debreceny, R. (2014). A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems, 15(4), 357-380. Obtido em 10 de 06 de 2019, de http://www.sciencedirect.com/science/article/pii/S1467089514000323
• [21] Dowling, C., & Stewart, L. (2007). Audit Support Systems and Decision Aids: Current. The University of Melbourne, p. 45.
• [22] Marques, P. M. (2016). Técnicas de Análise de Dados (Data Analytics) no contexto de uma auditoria financeira (PARTE I). Revisores e Auditores, p. 12. Obtido em 21 de 06 de 2019, de http://www.oroc.pt/fotos/editor2/Revista/73/Auditoria.pdf
• [23] Ucuzoglu, J. (2015). Deloitte United States. Obtido em 16 de 06 de 2019, de https://www2.deloitte.com/us/en/pages/audit/articles/deloitte-prepares-firm-for-audit-of-the-future-accounting-today-article.html
• [24] Baptista, N. (2017). SIPTA - Sistema Informático de Papéis de Trabalho de Auditoria Utilização de CAATTs online. Tecnologias da Informação - Auditoria, p. 6. Obtido em 29 de 09 de 2019, de http://www.oroc.pt/fotos/editor2/Revista/76/TI3.pdf
• [25] Conn, J. (2013). Modern Healthcare. Obtido em 24 de 06 de 2019, de https://www.modernhealthcare.com/article/20130615/MAGAZINE/306159976/pairing-up
• [26] Setty, K., & Bakhshi, R. (2013). What Is Big Data and What Does It Have to Do With IT Audit? ISACA JOURNAL, 3(1), 3. Obtido em 16 de 06 de 2019, de https://www.isaca.org/Journal/archives/2013/Volume-3/Documents/13v3-What-Is-Big-Data.pdf
• [27] Papagiannis, S. (2012). Staying Ahead of the Data Tsunami. Obtido em 24 de 06 de 2019, de https://www.canadianunderwriter.ca/features/staying-ahead-of-the-data-tsunami/
• [28] Selby, D. (2011). Can Financial Statement Auditors Identify Risk Patterns in IT Control Evidence. Accounting Faculty Publications. Richmond: Accounting Faculty Publications.
• [29] Gepp, A., Linnenluecke, M., O’Neill, T., & Smith, T. (2018). Big Data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature, pp. 102-115. Obtido em 30 de 09 de 2019, de http://www.sciencedirect.com/science/article/pii/S0737460718300090
• [30] Vieira, V., Pedrosa, I., & Soares, B. (2016). Big Data & Analytics - Requisitos Mínimos de Controlo: uma proposta de revisão da literatura Big Data & Analytics - minimum control requirements: a proposal on literature review. CISTI 2016 - 11.ª Conferência Ibérica de Sistemas e Tecnologias da Informação. Gran Canária. Obtido de https://www.researchgate.net/publication/304783071_Big_Data_Analytics_-_Requisitos_Minimos_de_Controlo_uma_proposta_de_revisao_da_literatura_Big_Data_Analytics_-_minimum_control_requirements_a_proposal_on_literature_review
• [31] Labrinidis, A., & Jagadish, H. (2012). Challenges and Opportunities with Big Data. ACM Digital Library, pp. 2032–2033. Obtido em 30 de 06 de 2019, de http://dx.doi.org/10.14778/2367502.2367572

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.