A adoção de ferramentas tecnológicas em projetos de auditoria contínua

Andrade, Augusto Cezar Romeiro de; Penha, Renato; Silva, Luciano Ferreira da; Bizarrias, Flavio Santino; Kniess, Claudia Terezinha

Augusto Cezar Romeiro de Andrade E-mail: andradesud@yahoo.com.br

1 – Augusto Cezar Romeiro de Andrade

Institution: University Nove de Julho

São Paulo, São Paulo, Brazil

Master in Project Management from the University Nove de Julho

Definition of research problem

Development of hypoteses or research questions (empirical studies)

Development of theoritical propositions (theoretical work)

Theoritical foundation / literature review

Definition of methodological procedures

Data collection

Statical analysis

Analysis and interpretation of data

Critical revision of the manuscript

Manuscript writing

Universidade Nove de Julho, São Paulo, SP, Brazil

http://orcid.org/0000-0002-8847-722X

Renato Penha E-mail: rp.renatopenha@gmail.com

2 – Renato Penha

Institution: University Nove de Julho

São Paulo, São Paulo, Brazil

Ph.D. from the Universidade Nove de Julho. Professor of the Postgraduate Program in

Project Management at University Nove de Julho

Definition of research problem

Development of hypoteses or research questions (empirical studies)

Development of theoritical propositions (theoretical work)

Theoritical foundation / literature review

Statical analysis

Manuscript writing

Universidade Nove de Julho, São Paulo, SP, Brazil

http://orcid.org/0000-0002-1431-2860

Luciano Ferreira da Silva E-mail: lf_silvabr@yahoo.com.br

3 – Luciano Ferreira da Silva

Institution: University Nove de Julho

São Paulo, São Paulo, Brazil

Ph.D. from the Universidade Nove de Julho. Professor of the Postgraduate Program in

Project Management at University Nove de Julho

Definition of research problem

Development of hypoteses or research questions (empirical studies)

Definition of methodological procedures

Data collection

Statical analysis

Analysis and interpretation of data

Critical revision of the manuscript

Manuscript writing

Universidade Nove de Julho, São Paulo, SP, Brazil

http://orcid.org/0000-0001-6482-8729

Flavio Santino Bizarrias E-mail: flavioxsp@hotmail.com

4 – Flavio Santino Bizarrias

Institution: Superior School of Advertising and Marketing

Porto Alegre, Rio Grande do Sul, Brazil

Ph.D. from the Universidade Nove de Julho. Professor of the Postgraduate Program in

Project Management at Universidade Nove de Julho

Definition of research problem

Development of hypoteses or research questions (empirical studies)

Development of theoritical propositions (theoretical work)

Data collection

Analysis and interpretation of data

Escola Superior de Propaganda e Marketing, Porto Alegre, RS, Brazil

http://orcid.org/0000-0001-5574-7820

Claudia Terezinha Kniess E-mail: kniesscl@gmail.com

5 – Claudia Terezinha Kniess

Institution: Federal University of São Paulo

São Paulo, São Paulo, Brazil

Ph.D. from the Universidade Federal de Santa Catarina. Adjunct Professor at the Federal

University of São Paulo – Paulista School of Politics, Economics and Business (EPPEN/

UNIFESP). Professor of the Postgraduate Program in Management and Regional

Development at Universidade de Taubaté. Professor and researcher at Universidade

São Judas Tadeu.

Definition of research problem

Development of hypoteses or research questions (empirical studies)

Development of theoritical propositions (theoretical work)

Data collection

Analysis and interpretation of data

Universidade Federal São Paulo, São Paulo, SP, Brazil

http://orcid.org/0000-0002-1961-2037

E-mail: andradesud@yahoo.com.br

E-mail: rp.renatopenha@gmail.com

E-mail: lf_silvabr@yahoo.com.br

E-mail: flavioxsp@hotmail.com

E-mail: kniesscl@gmail.com

The authors have stated that there is no conflict of interest.

Edited by

Jordana Marques Kneipp

Inclusion Criteria	Reason for Inclusion
Articles that conceptualize the constructs studied	Allow to address the purposes of the research: to understand the constructs studied according to the works.
Articles that address the relationships between the constructs studied	Allow to contemplate alignment of the works: understanding the interdependencies and relations between the constructs.
Published articles	Offer greater rigor in the arguments and theoretical contributions studied.
Exclusion Criteria	Reason for Deletion
Articles focusing on Quality, Marketing, Health, Finance, or other purposes outside the given constructs.	Exclude articles that are not focused on the questions that will offer insights to meet the research objectives.
Articles without relevant theoretical foundation or low relation to the constructs.	One of the purposes of the study is to obtain future research perspectives, through existing theoretical knowledge in a framework, for which theoretical assumptions are prerequisites.

Grupo Entrevistado

Interviewed Group

Specific Objective of the interviews

Professional Internal Audit Managers

Explore knowledge by looking at respondents’ experiences in each construct of this research (Creswell, 2017).

Identify the adoption of PAC, the technological tools adopted, and challenges faced throughout the project. Understand the factors required in this type of project.

Profissionais Gestores de TI

Determine the particularities of technological tools in relation to the use in PCAs.

Point out the complexities for adoption, as well as point out the main benefits of using each of them for CA.

Technological Tools	Description	Authors
Analytics	These are systems for data extraction, mining, and analysis. DA tools are configured with data modeling and analysis techniques based on statistics, mining, and research, through analytical procedures, applied in various business operations. It serves as a resource for accessing data from the systems of the audited processes in an independent manner and allows patterns and correlations to be identified through the data. These tools optimize the analysis process and increase the scope of validations, allowing their capabilities to generate patterns that identify anomalies or deviations. These factors are at the heart of automating audit testing and are fundamental to CA.	Russom (2011Russom, P. (2011). Big data analycts - TDWI best practices report. Introduction to Big Data Analytics. TDWI Best Practices Report, Fourth Quarter, 19(4), 1–34.); Jan et al., (2013); Vasarhelyi et al., (2012Vasarhelyi, M. A., Alles, M., Kuenkaikaew, S., & Littley, J. (2012). The acceptance and adoption of continuous auditing by internal auditors: A micro analysis. International Journal of Accounting Information Systems, 13(3), 267–281. doi: https://doi.org/10.1016/j.accinf.2012.06.011 https://doi.org/10.1016/j.accinf.2012.06... ); Rezaee et al. (2017) Feung & Thiruchelvam (2020Feung, J. L. C., & Thiruchelvam, I. V. (2020). A framework model for continuous auditing in financial statement audits using big data analytics. International Journal of Scientific & Technology Research, 9, 3416-3434.); Joshi & Marthandan (2020Joshi, P. L., & Marthandan, G. (2020). Continuous internal auditing: can big data analytics help?. International Journal of Accounting, Auditing and Performance Evaluation, 16(1), 25-42. doi: https://doi:10.1504/ijaape.2020.10676 https://doi:10.1504/ijaape.2020.10676... )
RPA - Robotics Process Automation	RPA is a technology that automates standardized system-based, rules-based activities using scripts and Artificial Intelligence (AI). Process-oriented, it automates repetitive tasks based on parameterized rules. However, it can develop to be data driven. Once you develop analysis or verification routines using analytics systems or scripts, with a repetitive nature, you can configure an RPA system, or as it is called Robotics, to perform such a task. This means that the RPA will execute in a programmed way the extraction or execution routine of the scripts or designed analyses, generating results, and presenting them in a depository or dashboard with pre-defined indicators. This automation step is found in advanced levels of CA projects.	Parati et al. (2008Parati, N., Malik, L., & Joshi, A. G. (2008, July). Artificial intelligence-based threat prevention and sensing engine: Architecture and design issues. In 2008 First International Conference on Emerging Trends in Engineering and Technology (pp. 304-307). IEEE.); Jans et al. (2013); Gotthardt et al. (2020Gotthardt, M., Koivulaakso, D., Paksoy, O., Saramo, C., Martikainen, M., & Lehner, O. (2020). Current state and challenges in the implementation of smart robotic process automation in accounting and auditing. Journal of Finance and Risk Perspectives.)
AI - Artificial Intelligence	A solution that brings together various intelligent technologies and techniques such as data mining, artificial neural networks, fuzzy logic, and expert systems that can be integrated with traditional procedures and statistical methods to analyze sensor-collected data, recognize patterns, filter, and correlate events to support security and deflection prevention. These techniques enhance systemic detection and prevention capabilities to correlate events generated by a diverse set of modern tools used for network management and monitoring. This technology in IA can be an ally to the use of RPA due to its threat detection and prevention mechanisms where two factors form the critical points; one is the intelligent inspection of analysis parameters for complete inspection of data stream transactions. And the second is the first intelligent reaction against atypical activities and process anomalies that operates with blocking or constraining actions, which if not practiced immediately may be too late. Also considered in advanced stages of CA projects, based on predictive and reactive reaction to risk events.	Parati et al. (2008Parati, N., Malik, L., & Joshi, A. G. (2008, July). Artificial intelligence-based threat prevention and sensing engine: Architecture and design issues. In 2008 First International Conference on Emerging Trends in Engineering and Technology (pp. 304-307). IEEE.); Gotthardt et al. (2020Gotthardt, M., Koivulaakso, D., Paksoy, O., Saramo, C., Martikainen, M., & Lehner, O. (2020). Current state and challenges in the implementation of smart robotic process automation in accounting and auditing. Journal of Finance and Risk Perspectives.)
ML - Machine Learning	Systems based on automatic criteria, develop rules to explain the historical data they collect. The rules can be configured as overlapping in specificity, due to incomplete information in the history. ML is a sub-field of Artificial Intelligence in which algorithms automatically adjust their models while handling data. ML systems are trained by absorbing learning from data and decisions and can make simple decisions and classifications. ML refers to computer algorithms that analyze data to learn how to perform tasks. Internal applications in CA projects could improve risk management, with fraud detection and compliance with violations of regulatory requirements. These results can be used as charts or warning indicators, basically automatic results that drive decisions, another factor sought in CA projects.	Parati et al. (2008Parati, N., Malik, L., & Joshi, A. G. (2008, July). Artificial intelligence-based threat prevention and sensing engine: Architecture and design issues. In 2008 First International Conference on Emerging Trends in Engineering and Technology (pp. 304-307). IEEE.); Kogan et al. (2014Kogan, A., Alles, M. G., Vasarhelyi, M. A., & Wu, J. (2014). Design and evaluation of a continuous data level auditing system. Auditing-A Journal of Practice & Theory, 33(4), 221–245. doi: https://doi.org/10.2308/ajpt-50844 https://doi.org/10.2308/ajpt-50844... ); Yu et al. (2019Yu, Y., Hou, J., & Li, H. (2019). Study on continuous internal audit system modeling and application. ACM International Conference Proceeding Series. doi: https://doi.org/10.1145/3358331.3358364 https://doi.org/10.1145/3358331.3358364... )

#	Interviewed	Job Title	Experience time (years)	Area of Expertise	Duration
1	E1	IA Manager	20	AI	31’
2	E2	IA Manager	12	AI	26’
3	E3	IA Manager	16	AI	48’
4	E4	CI and IA Coordinator	14	AI	32’
5	E5	IA Manager	23	AI	43’
6	E6	IA Superintendent	36	AI	59’
7	E7	IA Manager	16	AI	25’
8	E8	IT Manager	16	TI	44’
9	E9	IT Coordinator	17	TI	32’
10	E10	IT Manager	20	TI	47’
11	E11	IT Manager	20	TI	25’
12	E12	Data Analytics Coordinator	11	TI	26’
13	E13	IT Manager	24	TI	21’
14	E14	IT Coordinator	10	TI	21’
			Total		08:00

#	Factors	What to consider
1	Technological Transformation	Adopt more systemic and comprehensive approach. Developments to automate audit review procedures. Define tools that link the process control foundation to organizational structures and systems data.
2	Project Governance	Establish project governance to play the role of facilitating agent in front of the project, to capture resources and remove project barriers.
3	Requirements Definition (Goal)	Define in advance the requirements and what is expected from the project. Prepare a needs matrix and project objectives.
4	IT Competencies	Have people on the project team who are knowledgeable about technology, systems, and technological tools. Count on people who know the process and have mapped the systems involved.
5	Understanding the Business Process	Map the processes that will be evaluated. Know the rules behind the business to develop the project. Understand and identify the existing controls, project the possible deviations and the way to validate the compliance with each established rule.
6	Information Systems Knowledge	Determine the IT systems and enable the connection with CA techniques. Map the organization’s information systems and their data flow.
7	Data Availability and Extraction	Evaluate data availability by considering whether the input data required for CAP implementation can be obtained from the current system or whether additional system development is required.
8	Technological tools	Identify which tools are suitable for the project, considering the maturity level of technology use in the organization in line with the defined project requirements.
9	Data Analytics	Adopt tool for data extraction, mining, and analysis. DA tools set up the data base independently.

#	Challenges	Description	Authors	Factors to Consider
1	Complexity of technology use	There is a limitation in the projects when auditors are faced with IT issues. Especially in the configuration and adoption of tools it is perceived the lack of skill with the theme.	Pwc (2019Pwc (2019). Competências digitais aumentam a importância e a contribuição da auditoria interna. Estudo sobre a prática da auditoria interna 2019. https://www.pwc.com.br/pt/auditoria/assets/imagens/2019/papel-auditoria-interna-19.pdf https://www.pwc.com.br/pt/auditoria/asse... ); Joshi & Marthandan (2020Joshi, P. L., & Marthandan, G. (2020). Continuous internal auditing: can big data analytics help?. International Journal of Accounting, Auditing and Performance Evaluation, 16(1), 25-42. doi: https://doi:10.1504/ijaape.2020.10676 https://doi:10.1504/ijaape.2020.10676... ).	IT Competence
2	Core IT skills	The fundamental skills required of auditors usually revolve around accounting, economics, and auditing. There is a shortage of IT and IT project skills.	Li, Huang, & Lin (2007Li, S.-H., Huang, S.-M., & Lin, Y.-C. G. (2007). Developing a continuous auditing assistance system based on information process models. Journal of Computer Information Systems, 48(1), 2–13.); Mokhitli & Kyobe (2019Mokhitli, M., & Kyobe, M. (2019, March). Examining factors that impede internal auditors from leveraging information technology for continuous auditing. In 2019 Conference on Information Communications Technology and Society (ICTAS) (pp. 1-6). IEEE.).	IT Competency e Business Process Understanding
3	Clear definition of requirements and objectives	Lack of clarity of project objective and development requirements. Absence of pre-established definitions for CA adoption.	Little explored themes in the literature of this research.	Requirements Definition (Goal)
4	Investments for developments or acquisition of tools	Adopting tools can be costly for some organizations. Geographically dispersed entities may have increased adoption costs due to the need to expand resources.	Farkas & Murthy, 2014; Baksa & Turoff (2010Baksa, R., & Turoff, M. (2010). The current state of continuous auditing and emergency management’s valuable contribution. ISCRAM 2010 - 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings.).	Project Governance
5	Lack of standardization of systems and data	Use of legacy systems, lack of system integration, limited infrastructure, and varied data formats are barriers that directly impact the project.	Constan & Nastase, 2018; Cardoni et al., 2020; Feung & Thiruchelvam (2020Feung, J. L. C., & Thiruchelvam, I. V. (2020). A framework model for continuous auditing in financial statement audits using big data analytics. International Journal of Scientific & Technology Research, 9, 3416-3434.).	Data Extraction and Availability
6	Structure and maintain a collaborative environment and scenario (Process and IT)	Lack of participation, interaction, or resource allocation of professionals from other business and IT managers can make the project unfeasible.	CBOK, 2015Cangemi, M. P. (2015). Staying a step ahead: Internal audit’s use of technology, The Global Internal Audit Common Body of Knowledge, CBOK 2015 Practitioner Survey, the IIA research Foundation (IIARF).	Project Governance
7	Maintenance of tool settings and conditions	Ensure that the technological tools of the CA project are working to their full potential and are well configured. Changing parameters and conditions of the systems.	Gotthardt, Koivulaakso, Paksoy, Saramo, Martikainen, & Lehner (2020Gotthardt, M., Koivulaakso, D., Paksoy, O., Saramo, C., Martikainen, M., & Lehner, O. (2020). Current state and challenges in the implementation of smart robotic process automation in accounting and auditing. Journal of Finance and Risk Perspectives.); Feung & Thiruchelvam (2020Feung, J. L. C., & Thiruchelvam, I. V. (2020). A framework model for continuous auditing in financial statement audits using big data analytics. International Journal of Scientific & Technology Research, 9, 3416-3434.)	Technological Transformation Technology Tools
8	Lack of Project Governance	Define the proper structure for the project, establish the project governance agents to manage and obtain the necessary resources for the project.	Temas pouco explorados na literatura desta pesquisa.	Project Governance

[1] E-mail: andradesud@yahoo.com.br

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A adoção de ferramentas tecnológicas em projetos de auditoria contínua

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