Multidimensional audit data selection (MADS): A framework for using data analytics in the audit data selection process

TY - JOUR

T1 - Multidimensional audit data selection (MADS)

T2 - A framework for using data analytics in the audit data selection process

AU - No, Won Gyun

AU - Lee, Kyungha

AU - Huang, Feiqi

AU - Li, Qiao

N1 - Publisher Copyright: © 2019, American Accounting Association. All rights reserved.

PY - 2019

Y1 - 2019

N2 - Advances in data analytics techniques allow auditors to process the entire population of transaction data to identify outliers (i.e., unusual/suspicious transactions) that are more likely to be subject to misstatement. However, these techniques often generate a large number of outliers, making it impractical for auditors to investigate them in their entirety when performing substantive tests. This study proposes a Multidimensional Audit Data Selection (MADS) framework that provides a systematic approach for auditors to use data analytics in the audit data selection process. The framework also addresses a common obstacle of applying data analytics to the entire population of data—dealing with a potentially large number of outliers. By identifying problematic items from the entire population using data analytics and then applying prioritization methodologies to the resulting items, this framework allows auditors to focus on items with a higher risk of material misstatement and ultimately enhance the effectiveness of the audit.

AB - Advances in data analytics techniques allow auditors to process the entire population of transaction data to identify outliers (i.e., unusual/suspicious transactions) that are more likely to be subject to misstatement. However, these techniques often generate a large number of outliers, making it impractical for auditors to investigate them in their entirety when performing substantive tests. This study proposes a Multidimensional Audit Data Selection (MADS) framework that provides a systematic approach for auditors to use data analytics in the audit data selection process. The framework also addresses a common obstacle of applying data analytics to the entire population of data—dealing with a potentially large number of outliers. By identifying problematic items from the entire population using data analytics and then applying prioritization methodologies to the resulting items, this framework allows auditors to focus on items with a higher risk of material misstatement and ultimately enhance the effectiveness of the audit.

KW - Audit sampling

KW - Data analytics

KW - Information overload

KW - Outlier detection

UR - http://www.scopus.com/inward/record.url?scp=85073504066&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85073504066&partnerID=8YFLogxK

U2 - 10.2308/acch-52453

DO - 10.2308/acch-52453

M3 - Article

SN - 0888-7993

VL - 33

SP - 127

EP - 140

JO - Accounting Horizons

JF - Accounting Horizons

IS - 3

ER -