Ciencia de datos aplicada a Auditoría Interna
DOI:
https://doi.org/10.36428/revistadacgu.v12i22.195Resumen
El avance de la tecnología de la información presenta nuevas posibilidades y desafíos para las actividades de auditoría interna. La ciencia de datos presenta varios conceptos y técnicas para extraer información y conocimientos de los datos, un objetivo deseado en la auditoría interna. Este trabajo comenzó con las definiciones e interacciones de las diferentes especialidades de ciencia de datos, inteligencia artificial, minería de datos y big data. Posteriormente, se realizó una revisión de la literatura académica contemporánea relacionada, presentando los principales métodos, beneficios y desafíos para cada etapa de la auditoría interna.
Descargas
Referencias
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.
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2020 Revista da CGU
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
La Revista da CGU sigue a la Licencia Creative Commons Atribución 4.0 Internacional (CC BY), que permite que los trabajos publicados se usen y compartan, siempre dando crédito a los autores y origen. Los contenidos publicados hasta 2019 tienen permiso genérico de uso y distribución con indicación obligatoria de autoría y origen.
La presentación de la propuesta implica un compromiso de no enviarla a otra revista y autoriza, si se aprueba, su publicación.Destacamos algunos puntos esenciales y no exhaustivos relacionados:
- La presentación de la propuesta también implica que el (los) autor (es) está (n) de acuerdo con la publicación, sin resultar en remuneración, reembolso o compensación de ningún tipo.
- Los textos publicados son responsabilidad de los autores y no representan necesariamente la opinión de la revista.
- La responsabilidad por cualquier plagio es responsabilidad del (de los) autor (es).
- La persona responsable de la presentación declara, bajo las sanciones de la Ley, que la información sobre la autoría del trabajo es completa y correcta.
También se destacan los elementos relacionados con nuestras Políticas editoriales, en particular con la Misión y Alcance de la revista, la Ética de Publicación, el Proceso de Evaluación por Pares y la Política de Acceso Abierto.
