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