Magazine article The CPA Journal

Using CAATTs in Preliminary Analytical Review to Enhance the Auditor's Risk Assessment

Magazine article The CPA Journal

Using CAATTs in Preliminary Analytical Review to Enhance the Auditor's Risk Assessment

Article excerpt

Risk-assessment standards are requiring businesses to adjust their audit approach to a risk-based methodology. This can be a daunting challenge for auditors who have become accustomed to traditional substantive audit approaches for small businesses. Developing a basis for making a risk assessment becomes paramount to performing a high-quality risk-based financial statement audit. The risk-assessment standards require that auditors perform risk- assessment procedures during planning, such as a preliminary analytical review and obtaining an understanding of the entity and its internal controls. Computer-assisted audit techniques and tools (CAATT) can play a role in enhancing the effectiveness and efficiency of risk-assessment procedures. The key to effectively and efficiently leveraging software applications when assessing risk is to use the software to improve the quality of the audit evidence that forms the basis of the auditor's judgments about the financial statement risk.

Using Business Analytics Software

Traditional CAATTs have largely been the realm of data-extraction software that allows an auditor to efficiently manages large sets of data and effectively stratify it for testing. These CAATTs are primarily used in performing substantive tests, performing tests of details, and responding to specific risks. Business analytics software, however, can play a significant role in the audit engagement when it is used to assist the auditor in performing the preliminary analytical reviews in the risk-assessment process. Comprehensive analytics can provide one of the best sources of audit evidence to support an auditor's risk assessment. Ultimately, the result of the risk-assessment process will drive the overall audit approach, so effective risk-assessment procedures are the foundation for a high-quality financial statement audit. Effective analytics will not only help identify audit areas that present higher risks, they can also be the basis for assessing certain audit assertions as lower risk.

The availability of business analytics software tools has grown over the past several years. ProfitCents, iLumen, and ProSystem fx Profit Driver are examples of business analytics software tools. The features, pricing, and support for these different applications can vary widely. "Tools for Financial Analysis: Boost Your Consulting Practice to a Higher Level," by James Estes, Richard S. Savich and Maya Ivanova, in the November 2007 Journal of Accountancy, included a survey of business analytics tools and is a good starting point for potential buyers.

Comprehensive analytics typically include developing expectations from multiple sources to help identify unusual or unexpected relationships. These expectations may include period-on-period variance analysis, regression analysis, ratio analysis, industry comparisons, as well as budget-to-actual and other predictive tests. A good analytics software tool should make it easy for an auditor to develop these expectations by automating the calculations and comparisons so that the auditor can focus on evaluating the relationships. These analytics are used for identifying both inherent and control risks in the engagement. For example, if a company's actual sales are significantly greater than the calculated trend and its gross margin percentage exceeds the typical industry range, then an auditor would likely identify these as flags for an inherent revenue- recognition risk, such as a bill-and-hold scheme, and as a risk of ineffective internal controls over cutoff procedures.

Most of the analytical review techniques that auditors can apply during the planning stage are simple when compared to the more-complex procedures performed when using data extraction and analysis software. With data-extraction software, the objective of the analysis is to parse volumes of data to identify records that meet specific criteria, such as stratification of accounts-receivable aging balances, and transactions meeting certain authorization thresholds. …

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