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on Accounting and Auditing |
| By: | Farras, Ashab; Ali, Amjad; Audi, Marc |
| Abstract: | Continuous auditing has emerged as a transformative practice within the accounting and auditing professions, driven by rapid technological advancements and the growing demand for real-time financial assurance. Traditional audit practices rely on manual work, increasing the risk of human error and repetitive tasks. But Continuous auditing is powered by transformative tools like robotic process automation which eliminates these barriers by automating routine processes, reducing errors, and freeing employees from repetitive work. This paper examines the evolution of continuous auditing, its integration with advanced technologies such as artificial intelligence, robotic process automation, blockchain, and data analytics, and the broader implications for auditors, organizations, and academic institutions. Such advanced technology works together in continuous auditing to enhance accuracy, automate processes, and ensure data accuracy. Synergy in these advanced technologies enhanced audit efficiency. Through a comprehensive review of scholarly literature, the study underscores how continuous auditing facilitates real-time monitoring, improves audit quality, and reduces risks associated with traditional audit methods. Nevertheless, its adoption presents several challenges, including the management of information overload, the preservation of auditor independence, and the resolution of skill deficiencies among professionals. The 2024 BDO Audit Innovation Survey found that more than two-thirds (69%) of finance leaders said establishing data governance and internal data management is a barrier to a smooth audit experience. According to a 2019 ISACA survey, nearly two-thirds of organizations say the tech skills gap is impacting IT audits. The paper concludes by stressing the critical need to align auditing practices, professional training, and technological innovation to get the maximum benefits of continuous auditing in a digitally driven business environment. |
| Keywords: | AI, RPA, Accuracy, Sample, Audit Frequency, Automation and Training |
| JEL: | G3 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127319 |
| By: | Amir, Muhammad Sikander; Ali, Amjad; Audi, Marc |
| Abstract: | This study investigates the impact of artificial intelligence investment on firm profitability in Pakistan’s accounting, finance, and external audit sectors by introducing a composite metric called adjusted artificial intelligence investment. The data of 28 Pakistani firms from 2020 to 2024 has been used for empirical analysis. The research integrates technological infrastructure, cybersecurity risk, and regulatory support into a unified econometric framework. The study is anchored in the technology acceptance model and the resource-based view theory to explain the strategic value and adoption dynamics of artificial intelligence. Using panel least squares, fixed effects, and random effects regressions, the results consistently reveal that adjusted artificial intelligence investment and technological infrastructure significantly enhance firm profitability, while cybersecurity risk negatively influences it. Regulatory support exhibits mixed effects, being negatively associated in pooled models but positively in fixed effects analysis, highlighting the contextual role of governance frameworks. These findings carry significant implications for multiple stakeholder groups. For firm managers, the results underscore the importance of adopting a strategic, infrastructure-backed approach to AI implementation, prioritizing integration with secure digital environments. Policymakers must move beyond generic regulatory frameworks and instead focus on designing sector-specific policies that promote innovation without compromising compliance. Investors, too, can benefit from evaluating AI maturity as a key indicator of future profitability. Therefore, the study not only confirms the financial value of AI but also highlights the ecosystem-level support needed to realize its full potential. This research fills a key gap by holistically evaluating artificial intelligence's role in shaping firm performance in a developing economy context and offers actionable insights for businesses and regulators aiming to enhance profitability through technological integration. |
| Keywords: | Artificial Intelligence, Firm Profitability, Accounting, Technological Infrastructure, Cybersecurity, Regulatory Support |
| JEL: | O3 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127314 |
| By: | Nicholas Lacoste (Tulane University); Zehra Farooq (Federal Board of Revenue, Pakistan) |
| Abstract: | This paper bridges welfare economics and machine learning econometrics to develop empirically implementable algorithms for optimal audit targeting. We derive a sufficient statistic-based targeting algorithm that depends on three individualized causal effects: the immediate revenue recovered from an audit, the causal effect of an audit on long-run tax revenue, and the marginal administrative cost of an audit. We estimate these effects with a variety of machine learners comparing causal forests, LASSO, gradient boosted trees, and neural networks using the universe of Pakistani income tax returns, exploiting years in which audits were assigned completely at random. We implement our targeting algorithms in out-of-bag years, comparing them to the real-world policy when audits were partially or entirely targeted. We show that the real world audit program in Pakistan lost almost 173, 000 Rs ($1, 700) in net revenue per-audit, while our optimal policy generates 285, 000 Rs ($2, 800) in expected net revenue per-audit. We also find that targeting audits based on immediate recoup is sub-optimal to targeting on long-run deterrence in this setting. Moving forward, our framework offers a general approach to empirical welfare maximization using machine learning in resource-constrained policy settings. |
| Keywords: | optimal audit policy, tax enforcement, machine learning, sufficient statistics |
| JEL: | H21 H26 C14 C45 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:tul:wpaper:2603 |
| By: | Sato, Hideki |
| Abstract: | This study addresses the following two questions focusing on state sales tax and the behavior of a monopolist: (1) Under what conditions would a monopolist evade state sales tax even if evasion is costly? and (2) Can tax rates and enforcement be effective deterrents against evasion? The analysis reveals that, under certain conditions, a monopolist facing enforcement may underreport sales rather than not report them at all, even if evasion incurs costs. Furthermore, this study demonstrates that reducing tax rates and strengthening enforcement can effectively prevent tax evasion and that such preventive measures can lead to increased tax revenue. |
| Keywords: | Sales tax, Monopolist, Tax evasion, Tax enforcement. |
| JEL: | D42 H26 H32 H71 |
| Date: | 2025–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:127426 |
| By: | Zouhair Eddekkar (EFMI - Economie, Finance, Management et Innovation - Université Ibn Tofail / FEG Kénitra (Maroc)); Badr Machrafi (EFMI - Economie, Finance, Management et Innovation - Université Ibn Tofail / FEG Kénitra (Maroc)); Soraya El Maaroufi |
| Abstract: | This paper presents a SLR of impact of Artificial Intelligence. We conduct a research from Scopus and WoS through 2 different requests: "Artificial intelligence" AND "impact" , "Artificial intelligence" AND "business" we found 626 References about this topic, this study employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to recognized standard for executing advanced systematic literature review to select articles and undertook thematic analysis to analyse the data, thanks to NVIVO we present our results in the forme of an analysis grid, with VoS Viewer the most important technical sub-topic in this field are : Machine learing, deep learning, chatbot.. and applications area are: Economic, financial performance, accounting, psyciatru and magistrate. The results of this RLS show that AI accuracy, interactive experience and observation strongly influence customers' perceived hedonic and utilitarian values, and the integration of AI into platforms creates a favorable environment for consumer behaviors and that customers expect increased utility from AI in terms of shopping time, savings and convenience; Furthermore, the study highlights the impact of AI on the labor market, indicating that if the Moroccan manufacturing sector adopts advanced technologies, manual and repetitive tasks will be replaced by machines, which can reduce the demand for unskilled labor, as well as AI will improve the performance of accounting operations by automating repetitive tasks and optimizing the time of professionals, certain missions of accountants remain inaccessible to AI due to human skills such as negotiation, artistry and team building.Companies use AI less and are less open to this technology and are less likely to integrate AI into their strategies, putting them at a competitive disadvantage. |
| Abstract: | Cet article présente une revue systématique d''impact de l'Intelligence Artificielle (IA). Nous avons mené une recherche à partir de Scopus et de WoS à travers deux requêtes différentes : "Intelligence artificielle" et "impact", "Intelligence artificielle" et "entreprise". Nous avons trouvé 626 références sur ce sujet. Cette étude a utilisé l'approche Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), une norme reconnue pour l'exécution de revues systématiques avancées de la littérature, pour sélectionner les articles et a entrepris une analyse thématique pour analyser les données. Grâce à NVIVO, nous présentons nos résultats sous forme de grille d'analyse. Avec VoS Viewer, les sous-thèmes techniques les plus importants dans ce domaine sont : l'apprentissage machine, le deep learning, les chatbots. Les domaines d'application comprennent : l'économie, la performance financière, la comptabilité, la psychiatrie et la magistrature. Les résultats de cette RLS montrent que la précision de l'IA, l'expérience interactive et l'observation influencent fortement les valeurs hédoniques et utilitaires perçues par les clients, et l'intégration de l'IA dans les plateformes crée un environnement favorable aux comportements des consommateurs et que les clients attendent une utilité accrue de l'IA en termes de temps d'achat, d'économies et de commodité; par ailleurs, l'étude souligne l'impact de l'IA sur le marché du travail, indiquant que si le secteur manufacturier marocain adopte des technologies avancées, les tâches manuelles et répétitives seront remplacées par des machines, pouvant réduire la demande de main-d'œuvre non qualifiée, ainsi que l'IA améliorera la performance des opérations comptables en automatisant les tâches répétitives et optimisant le temps des professionnels, certaines missions des experts-comptables demeurent inaccessibles à l'IA en raison de compétences humaines telles que la négociation, le sens artistique et la formation des équipes. Les entreprises utilisent moins l'IA et sont moins ouvertes à cette technologie et qui ont moins susceptibles d'intégrer l'IA dans leurs stratégies, cela constituer un désavantage concurrentiel pour elles. |
| Keywords: | Impact, Consumer, Business, Artificial intelligence, SLR, Consommateur, Numérique, Entreprise, Intelligence artificielle, RLS (Revue de la Littérature Systématique) |
| Date: | 2025–11–27 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05402380 |