The audit world is changing. Technology has transformed business processes and created a wealth of data that can be leveraged by accountants and auditors with the requisite mindset. Data analysis can enable auditors to focus on outliers and exceptions, identifying the riskiest areas of the audit. The authors introduce the process, with a review of some emerging approaches and compilation of useful resources for auditors new to the topic.
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The advent of inexpensive computational power and storage, as well as the progressive computerization of organizational systems, is creating a new environment in which accountants and auditors must practice. This article aims at introducing basic data analysis concepts to enable accounting professionals to understand how to navigate within this new environment. Specifically, the focus will not be on auditing and accounting standards and their current required procedures, but rather on what the profession can progressively achieve with data analytics. Most analytical procedures, in the right circumstances, may be applicable to the entire audit process, from risk assessment to test of details. What follows is a step-by-step overview (Exhibit 1) of best practices for the process of applying analytics, with an emphasis on audit by exception (ABE).
The Steps in the Process
Flowcharting the process.
Understanding the elements of a certain cycle or application is essential for selecting data and understanding risk. Many tools are available for flowcharting, such as Tableau Public, QlickSense, and RapidMiner, all of which are free. Flowcharting is also possible in Microsoft Excel or PowerPoint. Exhibit 2 shows a sample flowcharting process taken from an insurance company.
Choosing and extracting the data.
With the risks in mind, the next step is to choose the data fields to be extracted and examined. This type of analysis is not very different from what would be done on a traditional audit. A progressively increasing number of audit apps are being sold or shared that can serve to simplify the audit task (e.g., http://www.capterra.com/audit-software/). Unfortunately, providers have not yet standardized around the AICPA’s Audit Data Standards (ADS) or any other common standard. Nevertheless, many audit software providers (e.g., ACL and CaseWare) have extensive libraries of scripts that can be adapted to various data formats, as well as extraction software that allows for access to traditional data and enterprise resource planning (ERP) systems (e.g., SAP and Oracle).
Understanding the population.
It is very important for the sake of completeness to understand the nature, distribution, and limitations of the population to be tested. Understanding the scope and limitations of the data is imperative, as it enables an accountant to choose the most appropriate and effective analytical technique.
Understanding the fields with descriptive statistics.
The examination of key fields for their characteristics and statistical parameters (e.g., maximum, minimum, median, variance) and data availability (e.g., missing values) is probably the most important initial task, but one that is often underappreciated or even neglected.
Exploratory data analysis.
Modern tools of visualization (e.g., Tableau or Excel) allow for data exploration that helps auditors carefully choose where to place their analytic efforts and which assertions to test. Auditors can focus more extensive testing on the areas highlighted as highest risk.
Choice of analytic methods and alternative approaches.
A great number of analytic methods have been applied to audits in a research mode (Deniz Appelbaum, Alexander Kogan, and Miklos Vasarhelyi, Analytics for External Auditing: A Literature Review, Rutgers CARLab, 2016) and are being progressively adopted by CPA firms. Exhibit 3 provides examples of several analytic methods. Given this variety of choices, auditors need to know the data as intimately as possible, as well as understand the specific analytic task, in order to reduce the pool of potential analytical methods.
Any significant deviations should be investigated by auditors.
Confirmatory data analysis and finding outliers.
Having identified the riskiest areas of the audit, an auditor should next use some of the techniques discussed above to evaluate the data. These techniques are used first to infer analytic models to provide audit benchmarks or expectations; the actual values are then compared with the benchmarks. Any significant deviations should be investigated by auditors. For example, regression analysis can be used to derive a model for the revenue account based on archival data. The values calculated by this model should be compared against the actual revenue amounts, and any significant differences investigated.
Evaluating results evaluation and integrating with traditional findings.
Ideally, the outliers should be segregated from the population for more detailed audit examination, as discussed above. In such an audit by exception (ABE) approach (Exhibit 4), an auditor’s attention is more focused on the problematic transactions rather than a traditional sample pool (which may or may not identify problematic transactions). Theoretically, ABE provides a more efficient and effective approach for identifying questionable numbers.
Because this examination process is not sample-based but exception-based, it represents a significant departure from the currently prevalent audit practice of statistical sampling. The main difference between the ABE and a sample-driven audit is how the subset to be examined is obtained. Both approaches start with the entire population, but an ABE tests every transaction and ultimately focuses only on those transactions that present problems (Exhibit 5), whereas a statistical sample does not test every transaction, as the sample purportedly represents the diversity and content of the entire population. If, however, the error-prone transactions as determined by the ABE tests represent, for example, less than .15% of the population, a sample of 60 transactions may or may not include even one data point that is significantly deviant, whereas every one of these .15% outlier transactions would be flagged for detailed testing by an ABE.
Nevertheless, many auditors and accountants may not initially feel comfortable with conducting an ABE of 100% of the population, unless this ABE examination were to be accompanied by a traditional statistical sample. The results of the ABE would then be examined in detail, just as currently the samples pulled are tested, with the findings compared and reported.
It is worth remembering that sampling became an accepted audit practice during a time when data sets were expanding in size but auditors were still examining transactions manually. Detailed examinations of entire datasets were infeasible at that time. Now that automated audit software capable of testing datasets rapidly with minimal manual involvement from the auditor exists, this obstacle is no longer an issue.
Although many of them have not yet been included auditors’ daily repertoire nor codified in audit standards, there are many emerging data analytics approaches that could assist with the audit process. Some of these are shown in Exhibit 3. The most promising of these approaches are described below.
Carefully validated and highly accurate predictive analytic models for aggregated accounting numbers can be used by auditors to reduce the time-consuming effort of disaggre-gated testing if the predicted values and the values of management assertions are sufficiently close.
The large audit firms are investing significant resources into the use of artificial intelligence to take advantage of their past experiences and industry knowledge. For example, data from working papers can be used to create automatic protocols for certain audit judgments, such as bad debt estimation, lease classification, and identification of abnormal contracts. Deep learning uses this knowledge in tandem with more advanced methods, such as neural networks, to represent the deeper structure of events and conditions in multiple layers of the neural network. Another term associated with deep learning is “cognitive computing,” a blend of automation and human interpretation. Deep learning requires tremendous computational storage and power, however, since the learning occurs by combining human expertise with enormous amounts of data. Many businesses outsource deep learning projects to contractors and research centers, such as IBM Watson. It is conceivable that in the near future an “Auditor Watson” could exist that would assist accounting firms with financial and operational audits.
The recent development of the virtual currency Bitcoin has been facilitated by a technology known as blockchain that can keep data public and replicates many transactions in a network using encryption methods. This methodology may presage a fundamental change in methods of data storage and validation. Smart contracts associated with blockchain might be able to automatically execute contract features without human intervention. For example, the contract between the auditor and the firm may dictate that if an outlier is larger than 100% of the median value of the transactions, it must be stopped and examined by human eyes; blockchain could theoretically flag such outliers and refer them to an auditor.
The emergence of big data, and the mixing of large corporate datasets and external, unstructured data, allows for highly promising machine understanding of text that may one day provide great validation for management-supplied numbers and support new audit products, such as continuous auditing and monitoring from external data. Of note is the fact that three of the largest audit firms have employed legal discovery tools or developed methods to text mine information from converted PDF documents to create deep learning inputs.
Tools and Information Sources
More than 700 firms audit public companies, and many more audit or examine other entities. Smaller firms do not have the extensive financial and human resources that larger ones have, and thus may not be able to leverage data analytics technology to the same extent. There are, however, many sources of free software and educational materials that are currently available. A selection of these resources, in addition to commercially available tools, is listed below.
The open source R software has one of the largest library of applications available. Free software such as R and Weka are used nationwide in university courses and by some research and technology firms, but are somewhat frowned upon by accounting firms because they are not validated. These concerns are not without merit, since open source software can be clumsier and less user friendly than proprietary software, but their utility should not be ignored. In addition, while a basic knowledge of statistics and information technology is becoming essential for all accountants, other, more specialized functions can be contracted to other experts, perhaps online.
Proprietary tools such as Audit Command Language (ACL) and Interactive Data Extraction and Analysis (IDEA), as well as generic statistical software such as Statistical Analysis System (SAS) and Statistical Package for the Social Sciences (SPSS), are frequently used by large businesses and large firms. Furthermore, the capabilities and scope of these packages are constantly evolving, requiring that accountants and auditors have sufficient knowledge of analytics.
Large firms typically retrain their professionals through internal courses about their own approaches to auditing and are progressively trying to introduce audit analytics into this process. Four decades ago, each one of the then–Big Eight had its own IT audit packages, but today the Big Four use vendor-provided software such as ACL and IDEA. This convergence will likely also take place with the emerging statistical and visualization toolsets being developed.
A major difference in today’s environment is the power of group sourcing and the diffusion of the Internet. Powerful education mechanisms are emerging, ranging from free public resources to online Masters of Accountancy programs in audit analytics, some of which are financed by major firms (“KPMG, Villanova, Ohio State Launch First-Of-Its-Kind Data and Analytics Master’s Degree to Prep Data-Age Auditors,” KPMG, Aug. 4, 2016, http://bit.ly/2jWihzN).
The advent of data analytics and big data is not a fad; it is a real phenomenon driven by new technologies being adopted by many businesses.
A Growing Phenomenon
The advent of data analytics and big data is not a fad; it is a real phenomenon driven by new technologies being adopted by many businesses. Accountants and auditors are currently very far behind the curve. The profession will inevitably be forced to modernize audit approaches by corporate processes that are not auditable by traditional methods, accounting packages that can perform without manual intervention, and pressure from clients for more value in the audit engagement.
This article provides a general introduction to modern analytic methods and sources of information and education for accountants. Further resources can be found at http://raw.rutgers.edu/CPAjrefs.html.
FOR FURTHER WATCHING
Introduction to Audit Analytics:
Special Topics in Audit Analytics:
Information Risk Management:
Tutorials for Risk Management: