Automating the Audit Procedures by Nuriddin Tojiboyev

Kogan, Alles, Vasarhelyi, Wu (2014)

Levels of Audit Procedure Automation Extent of Audit Procedure Automation can be at several levels Higher levels of Automation → ability to work with Different Systems Higher levels of Automation → require Data Standardization Data Standardization can also be automated at a Client Level Manual Client Level Firm Level Challenges to Automated Systems Adaptability Current Differences among Audit Environments of Clients Future Changes in Audit Environments of Clients Our goal is to push the level of every audit procedure as rightwards as possible considering benefits and costs of doing so.

Automation of the Audit Procedures Standardized Data Formats Formalized Audit Procedures Automated Audit Procedures Audit Procedures Data Needs of Procedures Steps of Procedures At the end, we Formalized Audit procedures that run on standardized audit data and Standardized Data Formats for each client that satisfies the data needs of formalized procedures. These model design (“sketch”) is not dependent on any client specific data. Two main question: Can we formalize all the procedures? Can we standardize all the data we need for procedures? Or Can Standardized Audit Data provide all data we need to perform Formalized Audit procedures?

Continuous Audit with Standardized Data Anomalies – A Library of Analytical Tools Anomalies – B Anomalies – C Exceptions – A Library of Filters Exceptions – B Exceptions – C Formatted Data - A Formatted Data - B Formatted Data - C Decision on Additional Procedures Audit Data Standards Company A Data Company B Data Company C Data

Library of Audit Procedures Hierarchical Structure Firm Level Procedures GL 1. Check_for_gaps_in_JE_ID 2. Check_for_Duplicate_Entries 3. Check_for_Round_Dollar_Entries 4. Check_for_Post_Date_Entries … AR AP MTP Client Level Procedures Library of Automated Audit Procedures Company B Company A GL AR AP … MTP Company C GL AR AP … MTP

Mapping the Client Dataset to the Data Standard Fields Direct Transformation → feeding without any manipulation Modified Transformation → feeding with some manipulation Missing Data → Standard required field does not exist in client system. This may result in failure to implement some automated procedures. Additional Data → Client system has a useful field not required by standard. This may indicate the possibility of additional procedures that can be automated at client level.