Screening the recent uses of Artificial intelligence in accounting firms: a scoping review

PURPOSE :The rapid growth of technology and data has fostered the application of artificial intelligence (AI) to accounting firms, but no comprehensive review exists to guide these efforts especially in the last five years. Our objective was to estimate the nature and extent of the build of research on AI for accounting and auditing areas.METHODS :We executed a scoping review, searching 4 literature databases with terms pertaining to AI and accounting firms. We performed title and abstract and then full-text screening using multiple tools. Studies had to involve research, include both AI and accounting firms, and be published in English.RESULTS :Of 2388 unique papers, 14 met eligibility criteria. Most of the published articles refer to conceptual analysis in the form of a literature review or a theoretical discussion on the theme (64%), the rest of the articles used a qualitative methodology (28%) except one article published using a quantitative method of analysis. Also, we observe an evolution of the total number of publications for the 14 articles included in the analysis from 2016 to 2020.CONCLUSIONS :Research on AI for accounting firms is at an early stage of maturity especially with the use of new AI technologies (e.g: Machine learning, deep learning.). For the field to progress, more researches and studies are needed

Artificial Intelligence and Accountants' Approach to Accounting Functions

Prior to the advent of Artificial Intelligence (AI), accounting functions were predominantly processed manually. The emergence, however, introduced the use of intelligent machines to perform functions cleverly as humans, which minimises reasonably the processing time of accounting transactions when compared to manual processes. This study investigated the relationship between Artificial Intelligence (AI) and Accountants’ Approach to Accounting Functions (AAAF). The study used the research design method through a structured questionnaire. The targeted population and the sample size was 205, which comprises accountants with experience in systems' application for accounting and other financial transactions' functions. A purposive sampling technique was adopted to determine the respondents. The results of the logit regression analysis revealed that with the t-calculated of 3.183 > t-tabulated of 0.002 at a 5% level of significance, artificial intelligence has a significant positive impact on accountants' approach to accounting functions. This implies that when AI is adopted, accountants will significantly change their approach to functional activities. The study recommended the need for accountants to be better equipped with diverse AI technologies and accounting software packages through training and retraining, to enhance their functional abilities, effectiveness and efficiency

The Impact of Artificial Intelligence on Accounting Education: A Review of Literature

This study explores the impact of artificial intelligence (AI) on accounting education through a semi-systematic review of the literature. The review found findings from 20 studies on the topic of AI and accounting education published in various journals, conference proceedings, and a book chapter. The findings reveal that scholars have expressed concerns about the impact of AI on accounting education for a significant period. Moreover, several themes emerge, including an interest in expert systems, an exploration of the application of AI in accounting education, and the call for accounting curricular reform. The study concludes that accounting educators must adapt their teaching methods and curricula to ensure that graduates are equipped with the necessary skills for a changing industry. Future research can concentrate on enhancing accounting curricula with the latest technological advancements, like AI, and exploring its potential impacts on the accounting industry, including risks, limitations, ethical implications, and its usefulness in accounting practices like financial reporting and auditing

Adoption of AI in the Auditing Practice: A Case study of a Big Four Accounting Firm

This paper explores and explains key factors affecting the adoption of Artificial Intelligence (AI) in the auditing practice by a Big Four accounting firm, through the lens of the technology-organisation-environment (TOE) framework. Using the case study method, we conducted semi-structured interviews with decision-makers of the firm, complemented by secondary data. The data analysis identified significant anomalies to existing theories, revealing the specificity of adopting AI in audits. The findings showed that the firm’s adoption process was influenced by technology affordance, technology barriers, communication process, linking agents, firm scope and readiness, regulatory environment, predicted industrial changes and client’s acceptance. This study will contribute to the literature by providing a better understanding of AI adoption at the firm level, thus filling the gaps in the literature. It may strengthen the theories that underpin our understanding of the technology adoption by firms, revising, extending, and elaborating the TOE framework with more empirical evidence

Machine Learning Algorithms and Auditor’s Assessments of the Risks Material Misstatement: Evidence from the Restatement of Listed London Companies

The purpose of this study is to investigate the relationship between machine learning algorithms and auditors assessments of the risks of material misstatement and restatement. Additionally, a focus on the effect of machine learning algorithms (SVM, Naïve Bayes, and K-means) on misstatement and restatement in London companies. The final sample of the study is 304 firm year observations. Which covers the listed firms on the London Stock Exchange and the period from 2018 to 2020. Especially, the firms that restated their financial statements -even just once- during the study period. The results showed a positive significant effect of machine learning techniques (K-means, Naïve Bayes, and SVM) on the intentional misstatements, which means that using machine learning techniques helps in determining the intentional misstatements. The results also showed a negative significant effect of the same techniques (K-means, Naïve Bayes, and SVM) on the restatements, which means that using machine learning techniques helps in avoiding the restatements

Delegar ou não delegar para inteligência artificial? um estudo no contexto da auditoria interna

O advento de novas tecnologias como a Inteligência Artificial estão modificando a forma como as tarefas e processos estão sendo realizados pelo agente humano. O objetivo geral desta pesquisa foi de compreender os fatores que levam à delegação ou não de tarefas para inteligência artificial no contexto de auditoria interna. Para atingir este objetivo, este estudo foi dividido em duas etapas. O primeiro estudo foi realizado a partir de uma revisão sistemática da literatura com artigos publicados em periódicos de Ciências Contábeis e Sistemas de Informação. Nesta primeira etapa, constatou-se a necessidade de estudos empíricos em auditoria interna e identificou-se fatores que poderiam contribuir ou não para a adoção, por exemplo, custo de implementação de IA e falta de qualificação dos profissionais. Já na segunda parte do estudo, foram efetuadas entrevistas semiestruturadas com 15 profissionais de auditoria interna, que atuam em diferentes segmentos empresariais. A partir destas entrevistas se propôs um framework que pode auxiliar no processo de tomada de decisão em delegar ou não uma tarefa para IA. Além disso, identificou-se que tarefas que exigem julgamento profissional são preferíveis para não serem delegadas, principalmente quando envolve a detecção e prevenção de fraude, pois os modelos precisam estar parametrizados a fim de classificar se uma anomalia encontrada pode ser classificada como uma fraude ou se trata de um erro não intencional. Como contribuições teóricas, este estudo complementa a literatura de delegação de tarefas ao identificar fatores que podem contribuir com adoção, por exemplo, tarefas rotineiras. Como contribuição prática, este estudo apresenta uma possível reconfiguração da função da auditoria interna, onde esta função poderia ser categorizada em auditoria tradicional, auditoria contínua e ciência de dados. Já como contribuição social, os resultados demonstram a necessidade de novas competências do profissional de auditoria, identificando a falta de formação em programação e uso de inteligências artificiais, ou seja, estes profissionais precisam se adaptar a uma nova realidade.The advent of new technologies such as artificial intelligence is changing the way tasks and processes are being performed by the human agent. The overall objective of this research was to understand the factors that lead to the delegation or not of tasks to artificial intelligence in the internal audit context. To achieve this goal, this study was divided into two stages. The first study was based on a systematic literature review of articles published in journals of Accounting and Information Systems. In this first stage, the need for empirical studies on internal auditing was noted and factors were identified that could contribute or not to adopt, for example, the cost of implementing AI and the lack of qualification of professionals. In the second part of the study, semi-structured interviews were conducted with 15 internal audit professionals working in different business segments. Based on these interviews, a framework was proposed that can help in the decision-making process of whether or not to delegate a task to AI. Furthermore, it was identified that tasks that require professional judgment are preferred not to be delegated, especially when it involves the detection and prevention of fraud because the models need to be parameterized in order to classify if an anomaly found can be classified as a fraud or if it is an unintentional error. As theoretical contributions, this study complements the task delegation literature by identifying factors that may contribute to adoption, e.g., routine tasks. As a practical contribution, this study presents a possible reconfiguration of the internal audit function, where this function could be segregated between traditional auditing, continuous auditing, and data science. As a social contribution, the results demonstrate the need for new skills for audit professionals, identifying the lack of training in programming and the use of artificial intelligence, i.e., these professionals need to adapt to a new reality

Technological innovations in the work environment and the career of the millennium generation

Purpose – The purpose of this paper is to identify the relationship of career anchors with three aspects: themillennials’ professional skills, the millennials’ awareness of the replacement of jobs with new technologiesand the technological stress in the millennials’ working environment.Design/methodology/approach – The responses of 200 questionnaires were analyzed using descriptiveand variance analysis techniques.Findings – Among the three hypotheses raised, two were confirmed, showing that these young peoplerecognize the development of professional skills through new technologies, but are not highly sensitive to thestress associated with technological innovations.Originality/value – The paper contributes to a recent debate, which emphasizes the impact of theapplication of new technologies on the nature of study and employment levels

What Makes AI Different? Exploring Affordances and Constraints - The Case of Auditing

This study aims to gain a comprehensive understanding of the differences between classic IT and AI artefacts. To achieve this objective, the study employs a grounded theory literature review approach and analyses 81 papers related to the application of classic IT and AI artefacts in the auditing industry. Drawing on the Technology Affordances and Constraints Theory, we examine the actions that can be potentially enabled or restricted by using classic IT and AI artefacts. This analysis allows us to conceptualise and compare the affordances and constraints associated with these two types of artefacts. The study addresses the need for more research on AI from both social and technical perspectives. Our findings may facilitate practitioners in improving their business processes and promoting effective collaboration between humans and AI

Accounting in a Social Context

Accounting permeates all of society. Accounting information is not homogenous and varies not just from company to company but from user to user, meaning that the use of such accounting information is actually a social phenomenon within an organization. Accounting cannot therefore be understood simply in terms of its functional properties but more as a socially constructed set of actions taking place within the organization, the landscape of which is constantly transforming. Digital technologies in the form of big data and artificial intelligence (AI) are expanding the organization’s data eco-system forcing the accountant to develop their digital technology skillset and forge links with the data scientist, the incumbent custodian of these growing data streams. Meanwhile, a rapidly expanding sustainability agenda is broadening the organization’s biophysical landscape leading to even more data flows and creating the need for management accounting and control systems which will help organizations to behave in an environmentally sustainable and socially responsible manner. This chapter explores each of these issues and calls for a deeper understanding of the relationship between accounting and big data, AI and sustainability