Audit Quality in the Era of Data Analytics: Methods, Risks, and Standards Implications

Audit quality is often described as the auditor's capacity to identify and disclose significant misstatements, emphasizing the importance of competence and independence for reliable assurance results. (DeAngelo, ۱۹۸۱) Research on archival auditing indicates that the quality of audits systematically differs among auditors, clients, and engagement contexts, necessitating precise measurement and analysis of audit-quality indicators. (DeFond & Zhang, ۲۰۱۴) Recent research combines audit quality indicators and emphasizes ongoing difficulties in translating observable inputs and outputs into reliable assurance measures. (Knechel et al., ۲۰۱۳) Data analytics is transforming audit practices by broadening the possible evidence pool and facilitating more detailed risk evaluation and testing beyond conventional sampling methods. (Earley, ۲۰۱۵) The use of computer-assisted techniques by auditors relies on their perceived usefulness, the conditions of implementation, and the users' capabilities, potentially resulting in varying audit-quality improvements across different engagements. (Bierstaker et al., ۲۰۱۴) Research on continuous assurance indicates that analytics and automation can transform audit tasks into exception-based monitoring and enable quicker detection of anomalies. (Vasarhelyi et al., ۲۰۱۲) Big data can act as additional audit evidence, yet its dependability and significance rely on data origin, synergy with conventional evidence, and suitable documentation and assessment. (Yoon et al., ۲۰۱۵) Concurrently, audits driven by analytics present additional risks like data integrity issues, model risk, and governance deficiencies that necessitate more defined standards and enhanced validation methods to maintain audit quality. (Alles und Gray, ۲۰۱۶)

کلیدواژه ها:

Audit quality ، Audit data analytics (ADA) ، Continuous assurance