Search Results (16)

Peru’s tax dispute system—administrative claim, Tax Court appeal, and contentious-administrative review—has increasingly migrated toward electronic files, e-invoicing, interoperable databases, and data-driven oversight. This article examines whether artificial intelligence can reduce avoidable tax litigation without weakening taxpayers’ rights and identifies the institutional conditions required to reconcile administrative efficiency with due process, reason-giving, and effective contestation. Using a legal-doctrinal and policy-analytical design, the study analyzes Peru’s tax dispute architecture, digital evidence environment, and AI-related risks in compliance and administrative litigation. The findings show that only bounded decision-support applications are institutionally appropriate, including audit triage, anomaly detection, document classification, workflow prioritization, compliance assistance, and consistency checks, provided that they do not replace legally attributable human judgment. AI is compatible with digital tax justice only when six safeguards are institutionalized: legally meaningful explainability, evidentiary and computational traceability, meaningful human oversight with override authority, lifecycle auditability, effective contestation, and distributional equality. The analysis further demonstrates that facially neutral digital requirements and risk models may generate unequal effects when disparities in connectivity, digital literacy, record-keeping capacity, and access to professional assistance translate into differences in audit exposure, compliance costs, evidentiary burdens, and practical contestability. The article proposes a rights-compatible framework for AI-supported tax enforcement in Peru. Full article

Digital tax reforms are implemented through staged, publicly announced milestones, yet policymakers rarely have timely indicators of whether these signals mobilize information-seeking and whether such demand can be anticipated for operational planning. We analyze monthly Google Trends series for Greece’s myDATA/e-invoicing rollout (2016–present) using preregistered event study models that separate step changes from post-event trend shifts with HAC-robust inference, and we evaluate 1–3-month predictive performance via rolling-origin cross-validation against a seasonal-naïve benchmark. Search-based attention shifts appeared most clearly in application-related queries: invoicing app terms spike around visible rollout phases (≈+34 to +38 index points over six months) and decline around VAT–myDATA alignment (≈−34 to −43). Ecosystem attention (the “Electronic invoicing” topic) exhibits large, opposite-signed movements (≈−53 around public-sector expansion; ≈+46 around VAT alignment), whereas platform terms show smaller and less regular responses; a back-office milestone produces no detectable change. In out-of-sample tests, event-aware regressions improve short-horizon accuracy for platform terms (≈40–50% MAE reduction at one month; ≈18–32% at two to three months), with series- and horizon-dependent results elsewhere. Overall, the evidence supports using search activity as an intermediate planning signal—informative about when and where guidance demand concentrates but not evidence of compliance. Full article

The mandatory introduction of Romania’s national e-invoicing system, ANAF e-Factura, in January 2024 has reshaped B2B document exchange in the retail sector, but suppliers still operate in parallel with two proprietary electronic data interchange (EDI) platforms, EDINET and DocProcess, which increases integration complexity. This paper presents the architecture, implementation, and evaluation of a custom Laravel-based B2B platform developed to automate commercial workflows across these three channels. The system supports XML purchase order ingestion and normalization, product identifier resolution, unified order persistence, platform-specific invoice XML generation, and ANAF SPV submission via SmartBill and Oblio REST APIs. A comparative analysis of real production XML documents showed full field-level overlap across 21 invoice data dimensions, with the main differences between systems related to entity identification schemes rather than business information content. During 2025, the platform processed 1247 EDI purchase orders and achieved an 87.30% fully automated processing rate, reaching 94.60% by year-end through progressive product catalog enrichment. The results indicate that ANAF e-Factura is technically capable of covering the core invoice exchange function currently duplicated by proprietary EDI platforms, while their coexistence continues to impose additional integration effort and slows SME digital transformation, particularly for small and medium-sized suppliers. Full article

Digital transformation in public institutions is increasingly understood as a socio-technical and organizational process rather than a purely technological upgrade. This study presents the design of an ICT-based digital transformation roadmap aimed at improving administrative efficiency and citizen service delivery in a municipal public utility in Ecuador. A mixed-methods diagnostic approach was adopted, combining qualitative evidence from direct observation and a semi-structured interview with the head of the IT department, and quantitative data from a structured online survey administered to citizens. Baseline Key Performance Indicators (KPIs) were established using institutional records, service logs, and workflow analysis conducted over a three-month diagnostic window. Post-implementation KPI values are explicitly treated as ex ante projections, derived from process redesign analysis, benchmarking with comparable public utilities, and scenario-based assumptions, rather than empirically observed outcomes. The empirical results demonstrate high citizen readiness and acceptance of proposed digital services, including remote service portals, electronic invoicing, and automated support channels. The projected operational improvements—such as reductions in response and administrative processing times and increased digital transaction rates—are therefore presented as expected performance scenarios. A risk and alternative scenario analysis further examines how organizational constraints, resource availability, governance capacity, and change-management factors may moderate these outcomes. The study contributes a transparent and replicable framework for diagnosing digital readiness and planning ICT-driven transformation initiatives in resource-constrained public utilities, while emphasizing the need for future longitudinal validation using post-implementation data. Full article

Background: The rapid digital transformation in logistics requires the adaptation of transport companies to electronic information management, particularly through the implementation of electronic consignment notes (e-CMR). This study examines how the digitization of transport documentation affects the competitiveness, operational efficiency, and environmental performance of road freight transport companies. Methods: A questionnaire survey was conducted among Slovak and Czech carriers to analyze their experience and readiness for adopting e-CMR. The collected data were evaluated using descriptive and comparative methods to quantify economic and ecological impacts, focusing mainly on invoicing efficiency and paper consumption. Results: The results show that only a small share of carriers currently use e-CMR, primarily due to high software costs and the lack of partner participation. Nevertheless, digitization can significantly shorten the average invoicing delay by approximately 11.5 days, releasing around 7% of tied-up working capital and improving cash flow. From an environmental perspective, the replacement of paper CMR forms could save millions of sheets annually, leading to a substantial reduction in CO₂ emissions and paper waste within the V4 region. Conclusions: The findings confirm that the adoption of e-CMR improves economic performance, increases transparency, and contributes to sustainability, representing a crucial step toward a more competitive and environmentally responsible road freight transport sector in Europe. Full article

This paper addresses the time-intensive task of assigning accurate account labels to invoice entries within corporate bookkeeping. Despite the advent of electronic invoicing, many software solutions still rely on rule-based approaches that fail to address the multifaceted nature of this challenge. While machine learning holds promise for such repetitive tasks, the presence of low-quality training data often poses a hurdle. Frequently, labels pertain to invoice rows at a group level rather than an individual level, leading to the exclusion of numerous records during preprocessing. To enhance the efficiency of an invoice entry classifier within a semi-supervised context, this study proposes an innovative approach that combines the classifier with the A* graph search algorithm. Through experimentation across various classifiers, the results consistently demonstrated a noteworthy increase in accuracy, ranging between 1% and 4%. This improvement is primarily attributed to a marked reduction in the discard rate of data, which decreased from 39% to 14%. This paper contributes to the literature by presenting a method that leverages the synergy of a classifier and A* graph search to overcome challenges posed by limited and group-level label information in the realm of electronic invoicing classification. Full article

The field of electronic government (e-government) is gaining prominence in contemporary society, as it has a significant influence on the wider populace within the context of a technologically advanced world. E-government makes use of information and communication technologies (ICTs) at various levels and domains within government agencies and the public sector. ICT reduces manual labour, potential fraud points, errors, and process lapses. The Internet’s quick accessibility and the widespread adoption of modern technologies and disciplines, such as big data, the Internet of Things, machine learning, and artificial intelligence, have accelerated the need for e-government. However, these developments raise a number of data reliability and precision concerns. The adoption of blockchain technology by researchers demonstrates its efficacy in addressing such issues. The present study proposes the SECHash system model, which integrates blockchain and Optical Character Recognition (OCR) technologies for the purpose of regulating the processing of incoming documents by governmental agencies. As a case study to assess the proposed system paradigm, the study uses a document containing incoming invoices. The proposal seeks to maintain the integrity of document data by prohibiting its modification after acceptance. Additionally, SECHash guarantees that accepted documents will not be destroyed or lost. The analysis demonstrates that using the SECHash model system will decrease fraudulent transactions by eradicating manual labour and storing documents on a blockchain network. Full article

Traditional approaches for process modeling and process analysis tend to focus on one type of object (also referred to as cases or instances), and each event refers to precisely one such object. This simplifies modeling and analysis, e.g., a process model merely describes the lifecycle of one object (e.g., a production order or an insurance claim) in terms of its activities (i.e., event types). However, in reality, there are often multiple objects of different types involved in an event. Think about filling out an electronic form referring to one order, one customer, ten items, three shipments, and one invoice. Object-centric process mining (OCPM) takes a more holistic and more comprehensive approach to process analysis and improvement by considering multiple object types and events that involve any number of objects. This paper introduces object-centric event data (OCED) and shows how these can be used to discover, analyze, and improve the fabric of real-life, highly intertwined processes. This tutorial-style paper presents the basic concepts, object-centric process-mining techniques, examples, and formalizes OCED. Fully embracing object centricity provides organizations with a “three-dimensional” view of their processes, showing how they interact with each other, and where the root causes of performance and compliance problems lie. Full article

One way to reduce inequality and poverty is to promote tax justice. In 2021, the government of the state of Rio Grande do Sul, Brazil, implemented a program (the Devolve-ICMS Program) that refunds consumption tax to low-income citizens (cashback). This study aims to evaluate the impacts of this Program using a differences-in-differences model and having, as response variables, the monthly sum of electronic invoices issued to the Program’s beneficiaries, as well as their value. The database used includes all invoices issued to the target population during the 12 months before the Program’s implementation and the 14 months after its implementation, resulting in 7.7 million records. To receive the tax refund, the eligible population must pick up a Citizen Card, made available by the state government, which was done by a significant part of this population. The treatment group is composed of eligible citizens who have the Card, whereas the control group comprises eligible citizens who do not have it. The results show that the Program is effective, as it has reduced tax pressure on poor people and increased both the number of invoices issued and their value. Full article

Non-fungible Tokens (NFTs) are ownership records stored on a blockchain, and they are typically digital items such as photos and videos. In many ways, an NFT is like a conventional proof-of-purchase document, such as a paper invoice or an electronic receipt. NFTs are attractive among other things because of verifiability; each sale is recorded as a blockchain transaction, allowing ownership to be tracked. Also, NFTs can be used to transfer digital assets between two mutually distrusting parties, since both the crypto payment and the asset transfer take place in one transaction. With NFTs, all marketplaces can freely trade with the help of decentralized applications (DApps). It is currently estimated that there are over 245 NFT marketplaces (NFTM) listed with over 1000 blockchains as of August 2022 with 68 million blockchain wallet users. With the expansion of markets, they must face challenges and issues. The objective of this review is to study the market dynamics of NFT ecosystems. It also focuses on technical components that enable NFTs and their marketplace. The review provides a deeper understanding of its components, how they are related, and why they are important. The paper analyses the challenges faced by NFTs and marketplaces in terms of security, transparency, scalability, and the consequences leading to these issues and how they will address them, as well as future opportunities. Full article

The most common method used to document monetary transactions in Brazil is by issuing electronic invoices (NF-e). The audit of electronic invoices is essential, and this can be improved by using data mining solutions, such as clustering and anomaly detection. However, applying these solutions is not a simple task because NF-e data contains millions of records with noisy fields and nonstandard documents, especially short text descriptions. In addition to these challenges, it is costly to extract information from short texts to identify traces of mismanagement, embezzlement, commercial fraud or tax evasion. Analyzing such data can be more effective when divided into well-defined groups. However, efficient solutions for clustering data with characteristics similar to NF-es have not yet been proposed in the literature. We developed ELINAC, a service for clustering short-text data in NF-es that uses an automatic encoder to cluster data. ELINAC aids in auditing transactions documented in NF-e, clustering similar data by short-text descriptions and making anomaly detection in numeric fields easier. For this, ELINAC explores how to model the automatic encoder without increasing the calculation costs to suppress a large number of short text data. In the worst case, the results show that ELINAC efficiently groups data while performing three times faster than solutions previously adopted in the literature. Full article

Monitoring antimicrobial usage (AMU) in dairy cattle is becoming common in a growing number of countries, with the ultimate goal to improve practices, reduce the development of antimicrobial resistance, and protect human health. However, antimicrobials delivered as feed additives can be missed by some of the quantification methods usually implemented. Our objective was to compare three methods of quantification of in-feed AMU in Québec dairy herds. We recruited 101 dairy producers for one year in the Québec province. Quantities of antimicrobials were calculated by farm from: (1) feed mills invoices (reference method); (2) veterinary prescriptions; and (3) information collected during an in-person interview of each producer. We standardized AMU rates in kilograms per 100 cow-years and compared the reference method to both alternative methods using concordance correlation coefficients and Bland–Altman plots. Antimicrobial usage was well estimated by veterinary prescriptions (concordance correlation coefficient (CCC) = 0.66) or by the approximation using producer’s data (CCC = 0.73) when compared with actual deliveries by feed mills. Users of medically important antimicrobials for human medicine (less than 10% of the farms) were easily identified using veterinary prescriptions. Given that veterinary prescriptions were mostly electronic (90%), this method could be integrated as part of a monitoring system in Québec. Full article

The objective of the study was to compare three quantification methods to a “garbage can audit” (reference method, REF) for monitoring antimicrobial usage (AMU) from products other than medicated feed over one year in 101 Québec dairy farms. Data were collected from veterinary invoices (VET method), from the “Amélioration de la Santé Animale au Québec” provincial program (GOV method), and from farm treatment records (FARM method). The AMU rate was reported in a number of Canadian Defined Course Doses for cattle (DCDbovCA) per 100 cow-years. Electronic veterinary sales data were obtained for all farms for VET and GOV methods. For the FARM method, a herd management software was used by 68% of producers whereas farm treatment records were handwritten for the others; records could not be retrieved in 4% of farms. Overall, agreement was almost perfect between REF and VET methods (concordance correlation coefficient (CCC) = 0.83), but moderate between REF and GOV (CCC = 0.44), and between REF and FARM (CCC = 0.51). Only a fair or slight agreement was obtained between any alternative method of quantification and REF for oral and intrauterine routes. The billing software used by most of Québec’s dairy veterinary practitioners seems promising in terms of surveillance and benchmarking of AMU in the province. Full article

Electronic invoicing has been mandatory for Italian companies since January 2019. All the invoices are structured in a predefined xml template which facilitates the extraction of the information. The main aim of this paper is to exploit the information contained in electronic invoices to build an intelligent system which can simplify accountants’ work. More precisely, this contribution shows how it is possible to automate part of the accounting process: all the invoices of a company are classified into specific codes which represent the economic nature of the financial transactions. To accomplish this classification task, a multiclass classification algorithm is proposed to predict two different target variables, the account and the VAT codes, which are part of the general ledger entry. To apply this model to real datasets, a multi-step procedure is proposed: first, a matching algorithm is used for the reconstruction of the training set, then input data are elaborated and prepared for the training phase, and finally a classification algorithm is trained. Different classification algorithms are compared in terms of prediction accuracy, including ensemble models and neural networks. The models under comparison show optimal results in the prediction of the target variables, meaning that machine learning classifiers succeed in translating the complex rules of the accounting process into an automated model. A final study suggests that best performances can be achieved considering the hierarchical structure of the account codes, splitting the classification task into smaller sub-problems. Full article

The utilization of new technology in the form of blockchain technology for a Value Added Tax (VAT) acceptance system is relatively new and has not been widely encountered thus far. This research analyzes how blockchain technology can be applied to a VAT system, particularly for electronic invoices (e-Invoice). A qualitative approach was used in this study to analyze blockchain technology models that could be applied in a VAT system. The results of this study indicate that due to its characteristics, blockchain technology can only be applied to taxpayer data that do not require privacy. Data that are considered safe if distributed to nodes in the blockchain technology network include the Tax Invoice Serial Number (TISN). A TISN system based on blockchain technology will produce a faster and more efficient system. Transactions on the TISN in Indonesia can also be monitored and tracked directly by the Directorate General of Taxation (DGT). Blockchain technology can be applied in the TISN system by using a permissioned private blockchain type. Full article