FGV, Controladoria Geral do Estado da Paraíba

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READ THE TEXT AND ANSWER QUESTIONS 16 TO 20:

Audit data analytics, machine learning, and full population testing Technologies are evolving at an unprecedented pace and pose significant challenges and opportunities to companies and related parties, including the accounting profession. In today’s business environment, it is inevitable for companies to react quickly to changing conditions and markets. Many companies are seeking better ways to utilize emerging technologies to transform how they conduct business. We live in an age of information explosion, with technologies capable of making revolutionary changes in various industries and reshaping business models. At present, many companies view data as one of their most valuable assets. They amass an unprecedented amount of data from their daily business operation and strive to harness the power of data through analytics. Emerging technologies like robotic process automation, machine learning, and data analytics also impact the accounting profession. It is important for the profession to understand the impacts, opportunities, and challenges of these technologies.

Specifically, in audit and assurance areas, data analytics and machine learning will lead to many changes in the foreseeable future. Audit sampling is one such potential change. The use of sampling in audits has been criticized since it only provides a small snapshot of the entire population. To address this major issue, this study introduces the idea of applying audit data analytics and machine learning for full population testing through the concept of “audit-by-exception” and “exceptional exceptions.” In this way, the emphasis of audit work shifts from “transaction examination” to “exception examination” and prioritizes the exceptions based on different criteria. Consequently, auditors can assess the associated risk based on the entire population of the transactions and thus enhance the effectiveness and efficiency of the audit process. Adapted from the introduction to a study published in: https://www.sciencedirect.com/science/article/pii/S240591882200006X