1. Automating the Audit Procedures
by Nuriddin Tojiboyev

2. Kogan, Alles, Vasarhelyi, Wu (2014)

3. 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.

4. 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?

5. 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

6. 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

7. 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.