Auditing Section Research Summaries Space

A Database of Auditing Research - Building Bridges with Practice

This is a public Custom Hive  public

research summary

    Big data analytics in financial statement audits.
    research summary posted September 11, 2015 by Jennifer M Mueller-Phillips, tagged 08.0 Auditing Procedures – Nature, Timing and Extent, 08.09 Impact of Technology on Audit Procedures, 10.0 Engagement Management 
    1274 Views
    Title:
    Big data analytics in financial statement audits.
    Practical Implications:

    This article provides a concise introduction to Big Data analytics by providing examples of Big Data success stories in non-audit fields and drawing auditing parallels. It then indicates several characteristics of Big Data which should be considered when implementing Big Data analytics, specifically as they relate to audit procedures.

    Citation:

    Cao, M., R. Chychyla, and T. Stewart. 2015. Big data analytics in financial statement audits. Accounting Horizons 29 (2): 423-429.

    Keywords:
    Big data, analytical methods, auditing
    Purpose of the Study:

    The authors provide examples of Big Data analytics in other fields and suggest analogous auditing applications. They then briefly discuss characteristics of Big Data analytics that are specifically of relevance for the audit setting.

    Design/Method/ Approach:

    This study uses examples of Big Data in other industries to provide guidance for auditors on implementing Big Data audit analytics. There is no original analysis or unique data.

    Findings:

    The authors outline several examples of implementation of Big Data analytics in other fields and draws parallels to the audit world.

    • Using Google’s “Profile of Mood States” based on millions of tweets to predict shifts in the Dow Jones Industrial Average. The audit parallel: Using similar tools to predict bankruptcy or assess overall financial health of a firm to identify engagements/litigation risk.
    • Walmart uses sales transaction data to predict which items (surprisingly, Strawberry Pop-Tarts) have increased sales in response to dangerous weather patterns. The audit parallel: using sales trend data to identify problematic segments in scoping.
    • Ayata’s Prescriptive Analytics uses data from oil and gas drilling sites, such as images, video, sound, text, and numbers to predict optimal drilling sites. The audit parallel: Using new types of data for audit evidence to confirm existence of events and validate reporting elements.
    • The Los Angeles police department uses data from crime scenes to predict the most likely timing and location of crimes in order to deploy officers. The audit parallel: identifying fraud risks and focusing audit effort toward fraud detection.

    The authors then identify characteristics of Big Data that need to be considered when implementing analytics. They note that Big Data analytics are fundamentally different from procedures based on sampling since all data can be used. They note that Big Data helps determine that things are associated with one another, but not necessarily that one thing causes another. Lastly, they note that a key benefit to Big Data is that analytics can be continuously updated.

    Category:
    Auditing Procedures - Nature - Timing and Extent, Engagement Management
    Sub-category:
    Impact of Technology on Audit Procedures Confirmation – Process and Evaluation of Responses