The presence of Artificial Intelligence (AI) in the disruptive era is considered to be a solution for the inability of the audit to detect fraud and issue an audit opinion. The purpose of this study is to prove the potential of Artificial Intelligence (AI) to detect fraud and issue auditor opinion. This study uses the Theoretical Framework method and uses documentation techniques for previous research and by combining competencies and capabilities between the availability of big data, the use of data mining, Artificial Neural Network and involving the final analysis through the calculation of total irregularities obtained by using Fuzzy. The results of this paper reinforce the potential for the existence of Artificial Intelligence to be able to detect fraud, and publish audit opinions independently. The author hopes to provide a broader picture of the Artificial Intelligence framework to maximize its potential to help auditors detect and issue audit opinions.
The purpose of this study is to examine the influence of Internal Control Disclosure Index (ICDI) and discretionary accruals (as a proxy for financial reporting quality). This study uses a data collection of Indonesian Stock Exchange listed banking sector companies’ financial statement and annual reports in 2016-2019. Sample was chosen using purposive sampling method. The analysis method used in this study is using STATA. The results of this study indicate that internal control disclosure there is significantly related to financial reporting quality. This research contributes empirically to the development of the literature as well as practically becomes a consideration for relevant management in making corporate decisions regarding the disclosure of their internal controls.
Keywords: Internal Control Disclosure; Financial Reporting Quality; Banking.
The geographic proximity has faced challenges with the existence of COVID-19 pandemic since 2019. This study aimed to scrutinize the effect of geographic proximity and audit quality by using COVID-19 pandemic as a moderating variable to compare before and after the situation. The samples of this study were Indonesian listed companies from 2018 to 2020 in Indonesia Stock Exchange. This study overcame self-potential selection bias and analyzed using Coarsened Exact Matching and Heckman's 2-Stage Least Square (Heckman 2-SLS). This study found that geographic proximity is significantly associated with the post-pandemic period, whereas geographic proximity did not cause problems before the pandemic. We also provided sufficient evidence of (Coarsened Exact Matching) CEM and Heckman 2-Stage Least Square which found that there were significant and appropriate results of our initial testing. The study implications relate to the auditor to consider pandemic as a factor that has effect on the audit fee.
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