The Data Analytics Transformation: Evidence From Auditors, CFOs, and Standard-Setters

• In Event: 3.04 Data Analytics and Norm Sensitivity

Abstract

We investigate the use of data analytics internally by companies’ finance
and accounting functions to prepare the financial statements and to detect fraud, as well
as external auditors’ use of data analytics during the financial statement audit. Relying on
socio-technical theory, we examine how each of these groups use data analytics, how that
usage affects their interactions with each other (i.e., client-auditor interactions), and the
effect of rules/regulations on their use of data analytics. As such, we conducted 58 semistructured
interviews with prominent professionals from 15 companies, eight public
accounting firms, and six standard-setters/regulators. Our sample also includes 12 clientauditor
pairs (i.e., CFOs and their respective audit partners). Our findings suggest that
most companies and their auditors have made changes to the financial reporting and audit
processes to incorporate data analytics, with each group most often noting improved
financial reporting quality or audit quality as a key benefit. Despite the benefits, both
groups reported challenges that come with using data analytics, including finding
employees with the right skillset, overcoming the financial cost, dealing with the lack of
regulation/standards, and obtaining the data needed for analytics. Further, we leverage
our client-auditor pairs to examine the effects of data analytics on the client-auditor
relationship. Both parties believe the use of analytics has strengthened their relationship.
However, we identify potential future conflicts regarding the audit fee model, as well as
regulator concern over the increased business insights auditors are providing their clients
as a result of analytics. Our study makes several important contributions to practice and
theory, as we are among the first to empirically examine companies’ and audit firms’
recent and significant investment in developing data analytic tools.

Authors

• Ashley Austin, University of Richmond

• Margaret H Christ, University of Georgia

• Tina Carpenter, The University of Georgia

• Christine Marie Sims, University of Georgia