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on Accounting and Auditing |
| By: | Mrs. Sage De Clerck; Joseph Cavanagh; Mariano D'Amore |
| Abstract: | This Working Paper explores the relationship between the uptake of accrual basis of accounting in government and the use of the resultant accrual information in fiscal statistics, using the European Union (EU) as a case study. The Paper looks at the current state of accounting practices in the general government sector (GGS) of the 27 EU member states. The study of accounting practice is based primarily on two sources that provided a comprehensive picture of government accounting in all the 27 member states, namely the PwC/Eurostat survey of Accounting Maturities of EU Governments and the International Federation of Accountants and the Chartered Institute of Public Finance and Accountancy (IFAC/CIPFA) International Public Sector Financial Accountability Index. The analysis then uses data compiled by Eurostat, the EU statistical office, to explore the extent to which the accrual information is being used in the separate statistical reporting of EU member state public finances. The study draws out some general observations which may be of relevance to all countries during accounting and fiscal reporting reforms. |
| Keywords: | accrual accounting; accounting reforms; fiscal reporting; fiscal statistics; reconciliation |
| Date: | 2025–07–18 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/148 |
| By: | Tingyu Yuan; Xi Zhang; Xuanjing Chen |
| Abstract: | In the face of global economic uncertainty, financial auditing has become essential for regulatory compliance and risk mitigation. Traditional manual auditing methods are increasingly limited by large data volumes, complex business structures, and evolving fraud tactics. This study proposes an AI-driven framework for enterprise financial audits and high-risk identification, leveraging machine learning to improve efficiency and accuracy. Using a dataset from the Big Four accounting firms (EY, PwC, Deloitte, KPMG) from 2020 to 2025, the research examines trends in risk assessment, compliance violations, and fraud detection. The dataset includes key indicators such as audit project counts, high-risk cases, fraud instances, compliance breaches, employee workload, and client satisfaction, capturing both audit behaviors and AI's impact on operations. To build a robust risk prediction model, three algorithms - Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) - are evaluated. SVM uses hyperplane optimization for complex classification, RF combines decision trees to manage high-dimensional, nonlinear data with resistance to overfitting, and KNN applies distance-based learning for flexible performance. Through hierarchical K-fold cross-validation and evaluation using F1-score, accuracy, and recall, Random Forest achieves the best performance, with an F1-score of 0.9012, excelling in identifying fraud and compliance anomalies. Feature importance analysis reveals audit frequency, past violations, employee workload, and client ratings as key predictors. The study recommends adopting Random Forest as a core model, enhancing features via engineering, and implementing real-time risk monitoring. This research contributes valuable insights into using machine learning for intelligent auditing and risk management in modern enterprises. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.06266 |
| By: | Blaufus, Kay; Bock, Julian; Peuthert, Benjamin |
| Abstract: | Using unique tax audit data of 499 German firms, we analyze whether family firms, public firms, financially constrained firms, and those firms with managers with low tax morale substitute two tax strategies, book-tax conforming and nonconforming tax avoidance strategies, and examine the effect on overall tax avoidance. The empirical results are in line with family firms and those firms with low tax morale managers substituting conforming for nonconforming tax avoidance strategies, whereas public and financially constrained firms do the opposite. Moreover, we find that family firms differ from nonfamily firms only in their strategic implementation but not in the overall amount of tax avoidance. With respect to public, financially constrained firms and those firms with low tax morale managers, we find a positive association with the total level of tax avoidance. |
| Keywords: | conforming tax avoidance, tax planning, nontax costs, book-tax conformity |
| JEL: | H26 M41 G32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:arqudp:323205 |
| By: | Shigeo Morita (Fukuoka University); Yukihiro Nishimura (Osaka University and CESifo) |
| Abstract: | The development of online markets has raised ongoing concerns that foreign app service developers are avoiding value-added tax (VAT) in destination countries. To address this issue, some countries have introduced tax reforms that require platforms to pay VAT on behalf of foreign firms based on the sales generated by each firm. This study investigates whether preventing tax leakage through platform taxation improves welfare in the destination country. We first show that taxing foreign firms leads to a reduction in the commission fees charged by the platform to the sellers (developers) which replaces exited foreign developers with domestic ones. However, the increased tax burden also decreases the size of the network user base. Given this trade-off, we demonstrate that whether the domestic welfare increases after the tax reform depends critically on how responsive the sellers’ market entry is to network size. When the tax reform brings welfare gain, it increases with the tax rate and reduces with the initial share of foreign developers. Finally, we show that digitalization mitigates both welfare loss and the platform’s tax avoidance. |
| Keywords: | Value-added tax; Tax reform; Digital economy; Platform; Network externality |
| JEL: | H25 H26 F23 L13 L86 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:osk:wpaper:2502r2 |
| By: | Yukihiro Nishimura (Osaka University and CESifo) |
| Abstract: | Value-added tax (VAT) has two major problems in its enforcement: taxing foreign vendors which do not have a business entity in the destination country, and taxing small and medium-sized enterprises (SMEs) with small tax bases. As a solution of these problems, some countries attempt to utilize online platform, to let the platformer pay the foreign firms’ and SMEs’ VAT according to the sales each firm made (platform tax for the destination principle and formalization of informal sector). In the monopolistic market where the platformer determines the fees for the network entry and the commission fee of the platform services, standard-good’s price, we show that taxing foreign developers increases the tax burden laid on the standard good, and we show that the increased tax burden is born 100% by the domestic standard-good’s consumers. We also investigate whether or not the prevention of tax leaks by platform taxes improves the vendors’ entry and tax revenue of the destination country. The effect of the tax reform on home developers crucially depends on the responsiveness of the developers’ entry to the number of network users, which is decreasing in the VAT rate. The derived formula of marginal value of public funds suggests that, due to the simultaneity of price and quantity, more fully fledged structural analysis may be necessary. Additionally, we show that the VAT serves as a Pigouvian tax to ease congestion externalities, and our results are robust with platform competition. In the context of formalization of SMEs, the strength of network externalities matters to see if the existing formal sector receives windfall gains or losses. |
| Keywords: | Digital economy; Platform; Network externality |
| JEL: | F23 H26 |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:osk:wpaper:2506 |
| By: | Marius Protte; Behnud Mir Djawadi |
| Abstract: | While most of the existing literature focused on human-machine interactions with algorithmic systems in advisory roles, research on human behavior in monitoring or verification processes that are conducted by automated systems remains largely absent. Our study examines how human dishonesty changes when detection of untrue statements is performed by machines versus humans, and how ambiguity in the verification process influences dishonest behavior. We design an incentivized laboratory experiment using a modified die-roll paradigm where participants privately observe a random draw and report the result, with higher reported numbers yielding greater monetary rewards. A probabilistic verification process introduces risk of detection and punishment, with treatments varying by verification entity (human vs. machine) and degree of ambiguity in the verification process (transparent vs. ambiguous). Our results show that under transparent verification rules, cheating magnitude does not significantly differ between human and machine auditors. However, under ambiguous conditions, cheating magnitude is significantly higher when machines verify participants' reports, reducing the prevalence of partial cheating while leading to behavioral polarization manifested as either complete honesty or maximal overreporting. The same applies when comparing reports to a machine entity under ambiguous and transparent verification rules. These findings emphasize the behavioral implications of algorithmic opacity in verification contexts. While machines can serve as effective and cost-efficient auditors under transparent conditions, their black box nature combined with ambiguous verification processes may unintentionally incentivize more severe dishonesty. These insights have practical implications for designing automated oversight systems in tax audits, compliance, and workplace monitoring. |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.15439 |