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As environmental, social, and governance (ESG) reporting has become a mainstream channel for companies to communicate their commitment to sustainability issues, the need for reliable and transparent ESG reports is increasing. However, research on ESG assurance is still in its early stages. ESG assurance poses more challenges than traditional financial auditing due to the diverse subjects and types of information in ESG reports. This paper proposes using artificial intelligence (AI) technologies and exogenous data as solutions. It discusses how AI can enhance the efficiency and effectiveness of ESG assurance by assessing vast and extensive data. This paper also explores AI's application throughout the general ESG assurance process and contributes to the discussion on providing high-quality ESG assurance services. Additionally, it provides practical implications for auditors, regulators, and stakeholders. [ABSTRACT FROM AUTHOR]

The press and pundits have extensively discussed the dangers of artificial general intelligence (AGI) and the various ethical and functional effects of current emerging technologies. This paper attempts to focus on AI as applied to accounting and auditing and its realities. It shows studies using machine learning and natural language processing, including generative efforts, and robotic and extended process automation. It concludes that a very promising set of applications is emerging, but there is little immediate danger of being replaced by machines to most in the profession, although the impacts are going to be substantive. These applications primarily utilize individual or combined AI functions, and the realization of true intelligence remains a distant goal. JEL Classifications: M41; M42; C45. [ABSTRACT FROM AUTHOR]

This think piece looks at the traditional reporting for intangibles and concludes that the measuring and reporting of intangibles needs a structural rethinking to incorporate 21st-century technology involving new forward-looking information and methods to provide information that is consistent with current capabilities (e.g., apps, bots, multiple databases, artificial intelligence). Traditional measurement methods, first published by Fra Luca Pacioli, satisfied business needs for centuries, but they limit modern external stakeholders' ability to evaluate and compare firms' current performance or predict their future performance. The traditional concepts of articulation, consolidation, and valuation of intangibles are inadequate, and sometimes blatantly misleading. Further, empirical research has ignored the emergence of new circumstances in business operations and accounting technology. The goal of this think piece is to discuss the traditional backward-looking approach to financial reporting for intangibles and to outline some considerations for developing a new system. [ABSTRACT FROM AUTHOR]

This commentary discusses how large language models like ChatGPT hold transformative potential in accounting, including education, research, and professional auditing. In the educational sphere, the advent of ubiquitous artificial intelligence (AI) tutors could potentially solve Bloom's Two Sigma Problem, heralding a new era of personalized learning. Accounting research stands to benefit immensely, particularly in tasks that rely heavily on natural language processing. In the professional auditing domain, the capabilities of ChatGPT to create broad outlines of risks inherent in certain accounts and assertions can enable engagement teams to create more risk-responsive audit plans. However, although the advantages are remarkable, they are accompanied by potential pitfalls that necessitate cautious navigation. Even with these challenges, AI's impending transformation in personal and professional lives cannot be overlooked, as accounting stands on the brink of significant change. JEL Classifications: M40; M42; O33. [ABSTRACT FROM AUTHOR]

This paper advocates for a drastic transformation of government accountability and reporting. With the availability of Big Data and the advancement of technologies, the existing government reporting schema fails to meet the public's increasing demand for accountability. We discuss the need for the government to reform its reporting schema and prescribe potential paths toward a data-driven, analytics-based, real-time, and proactive reporting paradigm. We conceptualize an app-based continuous monitoring and reporting environment that is real-time, structured, future-oriented, and that incorporates non-financial information like ESG and infrastructure. This reformed reporting paradigm highlights the expected role of government reporting: to provide accountability to the public. [ABSTRACT FROM AUTHOR]

Machine learning is a subset of artificial intelligence, and it is a computational method that learns patterns from large and complex data. The learning processes enable us to make predictions for future events. In the accounting and assurance profession, machine learning is gradually being applied to various tasks like reviewing source documents, analyzing business transactions or activities, and assessing risks. In academic research, machine learning has been used to make predictions of fraud, bankruptcy, material misstatements, and accounting estimates. More importantly, machine learning is generating awareness about the inductive reasoning methodology, which has long been undervalued in the mainstream of academic research in accounting and auditing. The use of machine learning in accounting/auditing research and practice is also raising concerns about its potential bias and ethical implications. Therefore, this editorial aims to call the readers' attention to these issues and encourage scholars to perform research in this domain. [ABSTRACT FROM AUTHOR]

Summary: The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real‐life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine‐learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus. [ABSTRACT FROM AUTHOR]

While the massive volume of text documents from multiple sources inside and outside of the company provides more information for auditors, the lack of efficient and effective technology solutions hampers the full use of text data. Powered by the emerging data analytics technology of deep learning, the value of the text can be better explored to deliver a higher quality of audit evidence and more relevant business insights. This research analyzes the usefulness of the information provided by various textual data in auditing and introduces deep learning, an evolving Artificial Intelligence approach. Furthermore, it provides a guide for auditors to implement deep learning techniques with predeveloped tools and open-source libraries. [ABSTRACT FROM AUTHOR]

After decades of frustration with long "AI Winters," various business industries are witnessing the arrival of AI's "Spring," with its massive and compelling benefits. Auditing will also evolve with the application of AI. Recently, there has been a progressive evolution of technology aimed at creating "artificially intelligent" devices. Although this evolution has been permeated with false starts and exaggerated claims, there is some convergence on the fact that substantive progress has been obtained in the last few years with the adoption of deep learning in conjunction with much faster machines and dimensionally larger storage spaces (and samples). The area of auditing has lagged business adoption in the past (Oldhouser 2016), but is prime for partial automation due to its labor intensiveness and range of decision structures. Several accounting firms have disclosed substantive investments in the AI fields. This paper proposes various areas of AI-related research to examine where this emerging technology is most promising. Moreover, this paper raises a series of methodological and evolutionary research questions aiming to study the AI-driven transformation of today's world of audit into the assurance of the future. [ABSTRACT FROM AUTHOR]

This article focuses on the automated dynamic audit programme tailoring approach. The choice of audit planning and program tailoring as the arena for the application of expert system technology is a fortunate one. Audit practice tends to use predecessor working papers and company practice as well as auditor experience as the primary source of procedure determination. The area of procedure planning is rich in knowledge and experience. Knowledge bases can be drawn from actual working papers, audit guides, and the knowledge of individuals. The first generation of systems focused on problems of narrower domain and used an evolving software technology. The second generation is using a richer set of software tools and the background of awareness of successful applications of expert systems in many domains of knowledge. Experience with the first generation of systems indicates enthusiasm and investment in the products and subsequent lack of implementation on the firmwide basis for most of the products.

This paper proposes a new approach for modeling business processes using the Al-Planning paradigm. Based on the concepts of agents, actions, constraints and goals, the Al-Planning approach allows explicit representation of various types of internal controls. Different threats to business processes, such as fraud, can be modeled as planning tasks--that is, finding a sequence of actions that intend to achieve a defined set of goals. Applying modeled threats to models of business processes, a planning reasoner can generate hypothetical scenarios of exposures. Because of its ability to explicitly represent threats, controls and exposures, we argue that the Al-Planning approach is useful for business process modeling from the control perspective. [ABSTRACT FROM AUTHOR]