DA has been a significant area of investment for audit firms, mainly in the performance of consulting services, and more recently in audit services. Many companies collect huge amounts of data about their customers, external environment, competitors... and often don't know how to take the next step of analyzing and applying the data to run their businesses. However, in a profession where the legal responsibility and audit environment are highly regulated by the law, this means that auditing firms will have to be careful when boldly investing in
projects in the future that provides audit services (Liddy, 2014; Lombardi, Bloch, and Vasarhelyi, 2014).
Researchers should additionally investigate how the adoption of DA impacts the business risk of the audit firm itself from a liability standpoint (Gray &
Debreceny, 2014) or from the standpoint of being subject to regulatory sanctions, which could range from fines to being driven out of the audit business altogether.
Research into investor expectations, juror decision-making, and board of directors‟
perceptions of the level of assurance provided in DA audits versus traditional audits remain important avenues for future study. Another area that academics may be able to address is how the client‟s transaction information can be analyzed to detect errors in accounts. For example, Vandervelde et al.(2008) modeled the relations between accounts to determine if account errors could be detected, and found that the relatedness of accounts is an important factor that auditors need to consider.
Relatedness refers to how accounts are recorded in the client‟s records to ultimately create financial statements. Accounting is based upon double-entry bookkeeping, where accounts are debited on one side and corresponding accounts are credited on the other side to create a symmetrical entry or balance. Certain debit-credit relationships make sense, like debit to an asset account and a corresponding credit to a revenue account. However, some relationships might appear unusual, such as a debit to an expense account and a credit to an owner‟s equity account. DA tools could flag these suspicious account relationships so that auditors can investigate them further. As DA tools are being developed by firms, modeling relevant features similar to those in Vandervelde et al. (2008) would be very informative. In addition, researchers should examine whether economic data and social media data from external sources can be modeled to make predictions about factors that impact a client‟s business, which will aid auditors in planning and making business risk assessments.
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APPENDIX A: The process of implementing a typical contract at AASC Auditing Firm
Source: AASC Auditing Firm
APPENDIX B: Illustrative Data of Company A for the year 2021
1. Balance Sheet (before audit)
2. Balance Sheet (after audit)
3. Income Statement (before audit)
4. Income Statement (after audit)
5. Excerpt of Journal of entries 2021
APPENDIX C: Illustrative Data of Company B for the year 2021
1. Balance Sheet (before audit)
2. Balance Sheet (after audit)
3. Income Statement (before audit) 4. Income Statement (after audit)