Introduction to Data Analysis for Auditors and Accountants


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.

EXHIBIT 1

Applying Analytics Step by Step
– Flowcharting the process
Understanding the elements of a certain cycle or application is essential for selecting data and understanding risk.

EXHIBIT 2
Sample Flowchart of an Insurance Claim Process
– 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.
– 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 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.
– Choice of analytic methods and alternative approaches
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.

EXHIBIT 3
Analytic Methods and Tools
– 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. Any significant deviations should be investigated by auditors.
– 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.

EXHIBIT 4
Audit by Exception (ABE) Approach
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.

EXHIBIT 5
Outliers Identified to Be Examined
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.
Emerging Approaches
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.
– Predictive analytics
– Deep learning
Deep learning requires tremendous computational storage and power, however, since the learning occurs by combining human expertise with enormous amounts of data.
– Blockchain/Smart contracts
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.
– Text mining
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.

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.