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

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

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

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

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

As I was driving, the snow had started falling in earnest. The light was flat, although it was midmorning, making it almost impossible to distinguish the highway. I turned on the radio to help me concentrate on the road ahead; the announcer was talking about the snow. “The state Highway department advises motorists to use extreme caution and to drive with their headlights on to ensure maximum visibility.” He went on. “The state highway supervisor just called to say that one of the plows almost hit a car because the person driving hadn’t turned on his ligths.” I checked, almost reflexively, to be sure that my headlights were on.

How can information serve those who hear or read it in making sense of their own worlds? How can it enable them to reason about what they do and to take appropriate actions based on that reasoning? My experience with the radio illustrates two different ways of providing the same message: the need to use your headlights when you drive in heavy snow. The first offers dispassionate information; the second tells the same content in a personal, compelling story. The first disguises its point of view; the second explicitly grounds the general information in a particular time and place. Each means of giving information has its role, but I believe the second is ultimately more useful in helping people make sense of what they are doing. When I heard the story about the plow, I made sure my headlights were on.

In what is written about teaching, it is rare to find accounts in which the author’s experience and point of view are central. A point of view is not simply an opinion; neither is it a whimsical or impressionistic claim. Rather, a point of view lays out what the author thinks and why. The problem is that much of what is available in professional development in languageteacher education concentrates on telling rather than on point of view. The telling is prescriptive, like the radio announcer’s first statement. It emphasizes what is important to know and do, what is current in theory and research, and therefore what you — as a practicing teacher — should do. But this telling disguises the teller; it hides the point of view that can enable you to make sense of what is told.

Donald Freeman. Series Editor’s preface. In: P. R. Moran. Teaching culture: perspectives in practice. Boston (MA): Heinle, 2001 (adapted).