Ciência de dados aplicada à Auditoria Interna

Autores

  • Gustavo Fleury Soares École Internationale des Sciences du Traitement de L’Information (EISTI), França

DOI:

https://doi.org/10.36428/revistadacgu.v12i22.195

Resumo

O avanço da tecnologia de informação apresenta novas possibilidades e desafios as atividades de auditoria interna. A ciência de dados apresenta diversos conceitos e técnicas para extrair informacões e insights dos dados, objetivo desejado na auditoria interna. Este trabalho iniciou com as definições e interações das diversas especialidades de ciência de dados, inteligência artificial, mineração de dados e big data. Posteriormente, foi feita a revisão da literatura acadêmica contemporânea correlata, apresentando os principais métodos, benefícios e desafios para cada etapa da auditoria interna.

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Biografia do Autor

  • Gustavo Fleury Soares, École Internationale des Sciences du Traitement de L’Information (EISTI), França

    Mestre em Análise, Exploração e Optimização de Dados (Big Data) pela École Internationale des Sciences du Traitement de L’Information (EISTI), França. Especialista em Segurança em Rede de Computadores pela Universidade Católica de Brasília (UCB). Graduado em Engenharia Mecatrônica pela Universidade de Brasília (UnB). É Auditor Federal de Finanças e Controle com atuação no desenvolvimento de sistemas de TI para auxílio às atividades de auditoria.

Referências

AGGARWAL, C. C. Data Mining, The Textbook. 1a. ed. [S.l.]: Springer, 2015.

AICPA. Association of International Certified Professional Accountants. Understanding the Entity and Its Environment and Assessing the Risks of Material Misstatement, December 2018.

ALLES, M.; GRAY, G. L. Incorporating Big Data in audits: identifying inhibitors and a research agenda to address those inhibitors, Rutgers Business School, Newark, USA, July 2016.

ANICETO, M. C. Estudo Comparativo entre Técnicas de Aprendizado de Máquina para Estimação de Risco de Crédito, 2016.

APPELBAUM, D. Securing Big Data Provenance for Auditors: The Big Data Provenance Black Box , Rutgers, State University of New Jersey, USA, 2015.

APPELBAUM, D.; KOGAN, A.; VASARHELYI, M. A. Big Fata and Analytics in the Moderns Audit Engagement: Research Needs, State University of New Jersey, Newark, USA, 2017.

ASSOCIATION OF CERTIFIED FRAUD EXAMINERS. Global study onf occupational Fraud and Abuse. [S.l.]. 2018.

BACH, M. et al. Text Mining for Big Data Analysis in Financial Sector: A Literature Review, University of Zagreb, Croatia, 2019.

BAUDER, R.; KHOSHFOGTAAR, T. The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider Labels, Florida Atlantic University, USA, 2017.

BERTIN, J. Semiology of Graphics: Diagrams, Networks, Maps. Redlands, CA, USA: Esri Press, 2010.

BLAND, J. M.; ALTMAN, D. Statistics Notes: Transforming Data, Dep. of Public Health Science, London, UK, 1996.

BOSKOU, G.; KIRKOS, E.; SPATHIS, C. Assessing Internal Audit with Text Mining, Institute of Thessaloniki, Macedonia, Greek, 2018.

BRANDAS, C.; MUNTEAN, M.; DIDRAGA, O. Intelligent Decision Support in Auditing: Big Data and Machine Learning Approach, West University of Timisoar, Romania, 2018.

BUSSAB, W. D. O.; MORETTIN, P. A. Estatística Básica. 6a. ed. São Paulo: Saraiva, 2010. 1-5 p.

BYRNES, P. E. Developing Automated Applications for Clustering and Outlier Detection: Data Mining Implications for Auditing Practice, Rutgers, University of New Jersey, USA, 2015.

CAO, M.; CHYCHYLA, R.; STEWART, T. Big Data Analytics in Financial Statement Audits, Rutgers, The State University of New Jersey, USA, 2015.

CARVALHO, R. et al. Using Bayesian Networks to Identify and Prevent Split Purchases in Brazil, CGU, Brasília, Brasil., 2014.

CETAX. Data Science, Big Data, Data Analytics, 2019. Disponivel em: .

CHAN, D.; KOGAN, A. Chan, Data Analytics: Introduction to Using Analytics in Auditing, 2016, Rutgers, The State University of New Jersey, Newark, 2016.

CHEN, C.-H.; HARDLE, W.; UNWIN, A. Handbook of Data Visualization. Berlin, Germany: Springer, 2008.

CHEN, H.; CHIANG, R.; STOREY, V. Business Intelligence and Analytics: From Big Data to Big Impact, University of Arizona, Tucson, USA, 2012.

DAI, J. Three Essays on Audit Technology: Audit 4.0, Blockchain, and Audit App, Rutgers, The State University of New Jersey, USA, 2017.

DHAR, V. Data Science and Prediction, Stern School of Business, New York University, May 2012. Disponivel em: .

DOMINGOS, P. A Few Useful Things to Know about Machine Learning, University of Washington, Seattle, USA, 2012.

FAYYAD, U.; SHAPIRO, G.; SMYTH, P. Knowledge Discovery and Data Mining: Towards a Unifying Framework. University of California, Irvine, USA: [s.n.], 1996.

FISHER, I.; HUGHES, M.; GARNSEY, M. State University of New York, USA. Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research, 2016.

GABER, M.; LUSK, E. Analytical Procedures Phase of PCAOB Audits: A Note of Caution in Selection The Forecasting Model, The State University of New York, Plattsburgh, USA, 2017.

GARTNER. Big Data. Gartner, 2001. Disponivel em: . Acesso em: 2019.

GEPP, A. et al. Big Data Techniques in Auditing Research and Pratice: Current Trends and Future Opportunities, Bond University, Gold Coast, Australia, 2018.

HAJEK, P.; HENRIQUES, R. Mining corporate annual reports for intelligent detection of financial statement fraud – A comparative study of machine learning methods, University of Pardubice, Czech Republic, 2017.

HAN, J.; KAMBER, M.; PEI, J. Data Mining Concepts and Techniques. 3. ed. [S.l.]: Elsevier, 2012.

HUI, E. G. M. Learn R for Applied Statistics, Data Visualization. Berkeley, CA, USA: Apress, 2018. 129-172 p.

IIA. International Standards for the Professional Practive of Internal Auditing, Altamonte Springs, USA, 2012.

IIA. Data Analytics, London, UK, 2017.

KELLEHER, D. J.; TIERNEY, B. Data Science (Essential Knowledge Series). New York: The MIT Press, 2018.

KEMPER, B. Principles of Exploratory Data Analysis in Problem Solving: What Can We Learn from a Well-Known Case?, University of Amsterdam, Netherlands, 2009.

KIERAN, H. Data Visualization: A Practical Introduction. [S.l.]: Princeton University Press, 2018. Disponivel em: .

LEITE, J.; SILVA, A. Computing prediction intervals with CAATTs, Caceres, Spain, 2018.

LESKOVEC, J.; RAJARAMAN, A.; ULLMAN, J. Mining of Massive Datasets. Stanford University, California, USA: [s.n.], 2014.

LI, S. Time Series Analysis and Forecasting with Python. KDNuggets, Jul 2018. Disponivel em: . Acesso em: May 2019.

LIU, Q. The Application of Exploratory Data Analysis in Auditing, The State University of New Jersey, 2014.

MARTINEZ, J. M. O método científico na investigação de fraudes e irregularidades, Grant Thornton, Spain, 2014.

MASSARO, M.; DUMAY, J.; GUTHRIE, J. On the shoulders of giants: undertaking a structured literature review in accounting, Universitá Ca'Foscari Venezia, Italy; Macquarie University, Australia., 2016.

MONTESDEOCA, M.; MEDINA, A.; SANTANA, F. Research Topics in Accounting Fraud in 21st Century: A State of Art., Instituto Univesitário de Ciencias y Tecnologias Cibernéticas, University of Las Palmas de Gran Canaria, Espanha., January 2019.

NASCIMENTO, R. et al. Mineração de Dados na Identificação de Empresas Irregulares Quanto ao Pagamento de Impostos, Escola Politécnica de Pernambuco, Recife, Brasil, 2018.

PACHECO JR., J. C. Modelos para Deteção de Fraudes Utilizando Técnicas de Aprendizado de Máquinas, 2019.

REZAEE, Z.; DORESTANI, A.; ALIABADI, S. Application of Time Series Analyses in Big Data: Practical, Research, and Education Implications, Journal of Emerging Technologies in Accounting. 15., 2017.

REZAEE, Z.; DORESTANI, A.; ALIABADI, S. Universtiy of Memphis, USA. Application of Time Series Analyses in Forensic Accounting, 2018.

SALES, L.; CARVALHO, R. Measuring the Risk of Public Contracts Using Bayesian Classifiers, CGU e Universidade de Brasília, Brasil., 2016.

SANCHEZ, C. P.; MONELOS, P. L.; LOPEZ, M. R. Does external auditing provide insights to detecting and evaluating financial distress? A comparative analysis of econometric models and artificial intelligence, 2012.

SAYAD, S. An Introduction to Data Science. https: //www.saedsayad.com/, 2019.

SHMAIS, A. A.; HANI, R. Data Mining for Fraud Detection., Prince Sultan University, Saudi Arabia, 2010. Disponivel em: .

SIGKDD CURRICULUM COMMITTE. Data Mining Curriculum: A proposal, 2006.

SUN, T.; SALES, L. Predicting Public Procurement Irregularity: An Application of Neural Networks, Rutgers University, Newark, USA, 2018.

SUN, T.; VASARHELYI, M. Rutgers, New Jersey, USA. Embracing Textual Data Analytics in Auditing with Deep Learning, 2018.

TUKEY, J. Exploratory Data Analysis. London: Addison-Wesley, 1977.

VANBUTSELE, F. The Impact of Big Data on Financial Statement Auditing, Business Economics, Universiteit Gent, Belgique, 2018.

YEE, O. S.; SAGADEVAN, S.; MALIM, N. Credit Card Fraud Detection Using Machine Learning As Data Mining Technique, Universiti Sains Malaysia, Penang, Malaysia, 2018.

YOON, K. Big Data as Audit Evidence: Utilizing Wheather Indicators, Rutgers University, Newark, USA, 2016.

ZHENG, J. Data Visualization in Business Intelligence, Kennesaw State University, Georgia, USA, 2017.

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Publicado

2020-12-30

Como Citar

Ciência de dados aplicada à Auditoria Interna. Revista da CGU, [S. l.], v. 12, n. 22, p. 196–208, 2020. DOI: 10.36428/revistadacgu.v12i22.195. Disponível em: https://revista.cgu.gov.br/Revista_da_CGU/article/view/195.. Acesso em: 27 fev. 2024.