The theory of Continuous Auditing was established by Vasarhelyi and Halper (1991). Continuous auditing entails the real-time monitoring and analysis of the entire population of records. The premise of this methodology is based on the concept of “audit by exception” where deviations (e.g. control variances) are flagged as alerts and forwarded to the responsible parties (e.g. management, internal auditors, business owners) for investigation. Continuous auditing has evolved from an academic theory to a process applied in practice (Vasarhelyi et al., 2012). With the advent of technology, continuous auditing has gained popularity, a survey conducted in 2006 by PricewaterhouseCoopers finds that about 50% of U.S firms currently use a continuous auditing
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Six Sigma asserts these activities have to be investigated as they could represent issues in a business process. Based on these assumptions, a high Z-score in the data would be an indicator of unexpected abnormalities. Thus, this research can assess the effectiveness of data-driven IC by creating a volatility function to quantify the fluctuation of IC activities. The usage of the volatility feature is prevalent in the area of finance and economics. The most famous is the Black-Scholes model, whose generalization is a local volatility model, and the volatility is constant (Black, Scholes, 1973). In capital market, investors capture different volatility features to control risk and acquire arbitrage opportunity (Dupire, 1994). According to Dechow, Sloan (1996) the volatility of accruals is a signal and measurement to claim the earning’s quality. And furthermore, Francis (2012) showed that audit-related factors are systematically associated with the quality of earnings on audit engagements, after controlling for other (nonaudit) factors that may affect earnings quality. This finding clearly illustrates that audit quality has a direct relationship with the fluctuation of the business processes of the earning's formation. The assessment of IC is an important part of audit
Six Sigma asserts these activities have to be investigated as they could represent issues in a business process. Based on these assumptions, a high Z-score in the data would be an indicator of unexpected abnormalities. Thus, this research can assess the effectiveness of data-driven IC by creating a volatility function to quantify the fluctuation of IC activities. The usage of the volatility feature is prevalent in the area of finance and economics. The most famous is the Black-Scholes model, whose generalization is a local volatility model, and the volatility is constant (Black, Scholes, 1973). In capital market, investors capture different volatility features to control risk and acquire arbitrage opportunity (Dupire, 1994). According to Dechow, Sloan (1996) the volatility of accruals is a signal and measurement to claim the earning’s quality. And furthermore, Francis (2012) showed that audit-related factors are systematically associated with the quality of earnings on audit engagements, after controlling for other (nonaudit) factors that may affect earnings quality. This finding clearly illustrates that audit quality has a direct relationship with the fluctuation of the business processes of the earning's formation. The assessment of IC is an important part of audit