In this summary, we will cover:

  • What data analytics entails
  • How data analytics has been able to improve audit
  • Why data analytics is important for financial statement audit


  • Data analytics involves analyzing data with the aim of helping auditors draw meaningful conclusions.
  • Data testing, identification of anomalies points auditors towards items needed for further investigation.
  • Data analytics also provides audit evidence through comprehensive analysis of an organization’s general ledger system.
  • Data analytics has helped auditors in risk identification and assessment, understanding business properly, making a proper judgment.
  • Data analytics is necessary for analyzing financial statement audit because it helps auditors gain deep insight.


Data analytics (DA) has the capacity to transform the processes/ways financial statements are conducted. This has, therefore, made them more effective and more efficient in analyzing data. DA enables auditors to identify financial reports, operational business risks, and fraud testing in a better way even though integrating data analytics and auditing is still at its infancy.

Data analytics and auditing

According to AICPA 2015 (Audit analytics and continuous audit: looking toward the future) DA, as applied to financial statement auditing, is the art and science of discovering and analyzing patterns. It identifies anomalies and extracts other useful information in data related to the subject matter of an audit through analysis, modeling and visualizing for the purpose of planning or performing the audit. Therefore, an auditor uses data analytics to achieve certain auditing standard objectives, including;

  • Understanding business and making business decisions
  • Obtaining assurance that the financial statements are free from material misstatement due to error or fraud.
  • Enabling auditors to express opinions on whether the financial statements are fairly presented.

Data analytics has remained relevant to accounting firms because of its potential to significantly improve audit procedures such as:

  • Identifying fraud risk
  • Understanding relationships between related parties and transactions
  • Carrying out external procedures on high-risk items for confirmation
  • Auditing accounting estimates
  • Concluding and reporting
  • Material/event identification etc.

Most companies/firms use and also get involved in data analytics because of competition. Small accounting firms might be facing competition from bigger accountancy firms that happen to be using this new technology to offer more cost-effective auditing and other services. Auditors sometimes use data analytics to examine data in order to make conclusions on the information they contain, and this is done increasingly with the aid of specialized software.

Applying data analytics to financial statement audit

There are different ways DA can be applied to financial statement audit they are;

  • Testing for fraud: as a result of fraud risks and fraudulent entries, auditors are expected to test the general ledger and adjustments made in preparing the financial statements for appropriateness of journal entries. This can be done with the use of journal entry-testing software systems but many of these systems produce false positives leaving the auditors to find out which ‘positives’ are having problems.
  • Internal control: there is also the need to test the operating effectiveness of internal control. Internal control procedures like; account reconciliations, cross-checking that price on an invoice come from an approved price list, ensuring that information that is transferred from one system to another can be reperformed on a hundred percent basis than for it to be continuously performed.
  • Understanding the entity and assessing the risk; DA helps the auditor in understanding the entity, by properly assessing and identifying the risks of material misstatement. For instance, a visualization tool and other technique help the auditor to identify the patterns, plan the audit and understand the business. DA also helps the auditor understand how to compare data across multiple keywords.
  • Performing substantive analytical procedures; here, DA helps the auditor’s exercise of professional judgment to review accounting data, to identify unusual items to test (scanning) by suggesting hypothesis on the relationship between data variables.

In conclusion, DA is however not static because it handles the mechanics of analyzing data and presenting results so that the auditors can make easy judgments. Meanwhile, the DA is known, for its inability to make judgments like the auditors but a new set of computers known as cognitive computers are capable of a higher-order cognitive process. These computers have been said to be destined for a vital role in accounting and auditing profession because they help human experts to make better decisions by penetrating the complexities of the data.


AICPA (2015) ‘Audit analytics and continuous audit: Looking toward the Future’. NewYork.