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Current Issue, Volume 18

Auditing with smart contracts
Andrea M. Rozario and Miklos A. Vasarhelyi
Published February 2018
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Blockchain-based smart contracts are emerging as a disruptive force that may change the way financial statement audits are performed and delivered. With their potential ability to autonomously execute audit procedures on behalf of the auditor and disclose the results of these audit procedures, blockchain-based smart contracts have the potential to improve audit quality and meet the information demands of various vested parties for more timely and transparent audit reporting. This paper proposes the application of smart contracts to auditing as an enabler for improved audit data analytics and close to real-time audit reporting.
 

Digital economy and corruption perceptions: A cross-country analysis
Mohammad I. Merhi and Punit Ahluwalia
Published March 2018
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The motivation of this study is the lack of empirical evidence on the ability of Information Technology related factors to reduce corruption. This paper proposes a conceptual model that depicts the relationships between macro-level technology related factors and corruption perceptions. Data are collected from reputable organizations such as EIU and Transparency International. The sample used is sixty-nine countries, making the results more generalizable than those of single case studies or smaller sample size. The hypotheses are confirmed using PLS analytical procedures and the findings are reported. We discuss the results and their implications for researchers and practitioners.
 

Embracing Textual Data Analytics in Auditing with Deep Learning
Ting Sun and Miklos A. Vasarhelyi
Published April 2018
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While the massive volume of text documents from multiple sources inside and outside of the company provides more information for auditors, the lack of efficient and effective technology solutions hampers the full use of text data. Powered by the emerging data analytics technology of deep learning, the value of the text can be better explored to deliver a higher quality of audit evidence and more relevant business insights. This research analyzes the usefulness of the information provided by various textual data in auditing and introduces deep learning, an evolving Artificial Intelligence approach. Furthermore, it provides a guide for auditors to implement deep learning techniques with pre-developed tools and open-source libraries.
 

Identifying Influencing Factors of Audit Risk Model: A Combined Fuzzy ANP-DEMATEL Approach
Morteza Shafiee Sardasht and Elham Rashedi
Published May 2018
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In the professional circles, factors affecting audit risk are treated independently; however, a more objective approach in assessing detection risk should be involved the relationships among the audit risk factors. This study introduces a framework based on a fuzzy multi-criteria decision support to identify the influencing factors may affect the audit risk model considering the interdependencies among them. We first takes advantages of a fuzzy screening method to screen forty more practical factors may affect the audit risk model out of 58 potential ones. Then, we applied a combined approach of Analytic Network Process (ANP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to weight the factors and prioritize them considering their inner and outer dependencies complemented with a case study of Iran. To be more compatible with ambiguities related to human beings and make more useful decisions in the real world, the fuzzy set theory is used. The combined approach used in this study providing correct, precise weight for each factor can explore a more rigorous framework for decision-making in risk assessment by integrating interdependent relationships within and among audit risk factors.
 

Developing a Cognitive Assistant for the Audit Plan Brainstorming Session
Qiao Li and Miklos A. Vasarhelyi
Published July 2018
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With technological advances, audit firms have been able to increase the level of decision support embedded within the firms’ audit support tools (Dowling et al., 2008). Large audit firms have been investing substantive resources into the utilization of Artificial Intelligence (AI) to take advantage of their past audit experience and industry knowledge (Kokina and Davenport, 2017; Appelbaum, 2017). During the initial stages of an audit, the engagement team will meet to update their understanding of the client through brainstorming sessions. Audit firms are required by SAS No. 99 and SAS No. 109 to use brainstorming sessions to evaluate risk factors and discuss the susceptibility of the entity’s financial statements to material misstatement, either as a result of error or fraud (AICPA, 2012). At present, the most commonly used decision support tool for audit plan brainstorming is the checklist (Bellovary and Johnstone, 2007), which has shown limitations (Dowling and Leech, 2007; Seow, 2011; Landis, 2008). To improve audit plan effectiveness, this study proposes an audit domain cognitive assistant system to provide interactive decision support for information retrieval and risk assessment in audit brainstorming session. Cognitive Assistants are speech-enabled technologies that can understand voice commands, recognize conversation’s context, and answer questions in a personable manner (Garrido et al., 2010; Myers et al., 2007).
 

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