Case Analysis – Subsequent Event Disclosure

Case Analysis – Subsequent Event Disclosure

            The assessment of purchasing orders enabled auditors to discover that one worker of a client had used three of the customer’s POs to obtain merchandise for personal gains. The POs had led to a potential loss to Express Inc. The analytical data procedure, in this case, would require auditors to use the data of the company’s revenue trends, information on invoices, and other details regarding the three transactions to address this issue effectively (Groomer & Murthy, 2018). The information on these items will enable the auditor’s trace and discover deviations, inconsistencies, and patterns that lead to the anomalies in the firm’s transactions.

Data analytics entail software such as Sisense, looker Zoho Analytics, Yellowfin, IBM Watson, and Domo, among others. This software accesses the information from the above-listed data for effective auditing (Jeyapaul, Panchal & Lillie, 2016). Besides curbing further loses, the data analytics will help Express Inc. to protect its clients from POs fraud.  This aspect will enhance the customer’s trust in the firm.  On this point, it is essential for the firm to disclose the data analytics information in the client’s financial statement, as it would boost the company’s reputation and client’s confidence. Notably, the data analysis and audit information help to focus on the possible risks of subsequent transactions (Gepp, Linnenluecke, Neill, & Smith, 2018). Hence it enhances the understanding of risks associated with clients and POs, which heightens the effectiveness of the company.

Although disclosing some client financial information after data analytics may violate privacy and confidentiality rules, the managers need to weigh the essence of the procedure before informing clients. Completeness and integrity of the information extracted from a client may also be challenging to access; therefore, as a manager, one needs to disclose the POs fraud discovered during auditing after gathering abundant evidence.

References

Groomer, S. M., & Murthy, U. S. (2018). Continuous auditing of database applications: An embedded audit module approach. Continuous Auditing, 105-124.

Gepp, A., Linnenluecke, M. K., O’Neill, T. J., & Smith, T. (2018). Big data techniques in auditing research and practice: Current trends and future opportunities. Journal of Accounting Literature, 40, 102-115.

Jeyapaul, J., Panchal, P. S., & Lillie, B. J. (2016). U.S. Patent No. 9,269,061. Washington, DC: U.S. Patent and Trademark Office.