The Impact of Data Analytics on Auditors' Judgments and Decisions

• In Event: 3.04 Data Analytics and Norm Sensitivity

Abstract

A substantial amount of accounting academic research has examined auditors’ use of analytical procedures. Recent technological advances have facilitated the development and use of more sophisticated analytical procedures, now commonly referred to as data analytics. The AICPA, CPA Canada, IAASB and PCAOB have all expressed interest in incorporating more data analytics into the audit process. While data analytics offer great promise in identifying audit-relevant information, identifying such information does not guarantee that auditors’ will incorporate that information into their decision making process. This study examines whether two input to data analytics, the type of data analytical model (anomaly vs. predictive) and the type of data analyzed (financial vs. nonfinancial), impacts auditors’ decisions. Results of this study show that the type of data analytical model used and data analyzed jointly impact auditors’ change of budgeted audit hours for substantive testing. Specifically, when financial data is analyzed auditors increase budgeted audit hours more when such data is analyzed by predictive models than anomaly models. This finding is reversed when nonfinancial data is analyzed, as auditors increase budgeted audit hours more when anomaly models are used as compared to predictive models.

Author

• Jared Koreff, Trinity University