Search Results (102)

The digitalization of financial systems has intensified risks such as cyber fraud, data breaches, and financial exclusion, particularly for individuals with low digital financial literacy (DFL). As digital finance becomes ubiquitous, DFL has emerged as a critical competency. However, the determinants of DFL remain insufficiently explored. This study aims to validate a comprehensive, theory-driven model that identifies the key sociodemographic, economic, and psychological factors that influence DFL acquisition among investors. Drawing on six established learning and behavioral theories—we analyze data from 158,169 active account holders in Japan through ordinary least squares regression. The results show that higher levels of DFL are associated with being male, younger or middle-aged, highly educated, and unemployed and having greater household income and assets. In contrast, being married, having children, holding a myopic view of the future, and high risk aversion are linked to lower DFL. Interaction effects show a stronger income–DFL association for males and a diminishing return for reduced education with age. Robustness checks using a probit model with a binary DFL measure confirmed the OLS results. These findings highlight digital inequalities and behavioral barriers that shape DFL acquisition. This study contributes a validated framework for identifying at-risk groups and supports future interventions to enhance inclusive digital financial capabilities in increasingly digital economies. Full article

This study investigates the influence of corporate governance on reducing financial statement fraud (FSF) in Jordanian service and industrial companies listed on the Amman Stock Exchange from 2018 to 2022. To achieve this, the study employed the Beneish M-score model to assess the likelihood of FSF and logistic regression to examine the influence of corporate governance structure on fraud mitigation. The study identified 13 independent variables, including board size, board director’s independence, board director’s compensation, non-duality of CEO and chairman positions, board diversity, audit committee size, audit committee accounting background, number of annual audit committee meetings, external audit fees, board family business, the presence of women on the board of directors, firm size, and market listing on FSF. The study included 74 companies from both sectors—33 from the industrial sector and 41 from the service sector. Primary data was collected from financial statements and other information published in annual reports between 2018 and 2022. The results of the study revealed a total of 295 cases of fraud during the examined period. Out of the 59 companies analyzed, 21.4% demonstrated a low probability of fraud, while the remaining 78.6% (232 observations) showed a high probability of fraud. The results indicate that the following corporate governance factors significantly impact the mitigation of financial statement fraud (FSF): independent board directors, board diversity, audit committee accounting backgrounds, the number of audit committee meetings, family business involvement on the board, and firm characteristics. The study provides several recommendations, highlighting the importance for companies to diversify their boards of directors by incorporating different perspectives and experiences. Full article

The increasing complexity of financial reporting has enabled the implementation of innovative accounting practices that often obscure a company’s actual performance. This project seeks to uncover manipulative behaviours by constructing an anomaly detection model that utilises unsupervised machine learning techniques. We examined a dataset of 149,566 Slovak firms from 2016 to 2023, which included 12 financial parameters. Utilising TwoSteps and K-means clustering in IBM SPSS, we discerned patterns of normative financial activity and computed an abnormality index for each firm. Entities with the most significant deviation from cluster centroids were identified as suspicious. The model attained a silhouette score of 1.0, signifying outstanding clustering quality. We discovered a total of 231 anomalous firms, predominantly concentrated in sectors C (32.47%), G (13.42%), and L (7.36%). Our research indicates that anomaly-based models can markedly enhance the precision of fraud detection, especially in scenarios with scarce labelled data. The model integrates intricate data processing and delivers an exhaustive study of the regional and sectoral distribution of anomalies, thereby increasing its relevance in practical applications. Full article

Neglecting preventive healthcare policies has contributed to the global surge in chronic diseases, increased hospitalizations, declining quality of care, and escalating costs. Non-communicable diseases (NCDs)—notably cardiovascular conditions, diabetes, and cancer—consume over 80% of healthcare expenditure and account for more than 60% of global deaths, which are projected to exceed 75% by 2030. Poor diets, sedentary lifestyles, regulatory loopholes, and underfunded public health initiatives are driving this crisis. Compounding the issue are flawed policies, congressional lobbying, and conflicts of interest that prioritize costly, hospital-based, symptom-driven care over identifying and treating to eliminate root causes and disease prevention. Regulatory agencies are failing to deliver their intended functions. For instance, the U.S. Food and Drug Administration’s (FDA) broad oversight across drugs, devices, food, and supplements has resulted in inefficiencies, reduced transparency, and public safety risks. This broad mandate has allowed the release of unsafe drugs, food additives, and supplements, contributing to the rising childhood diseases, the burden of chronic illness, and over-medicalization. The author proposes separating oversight responsibilities: transferring authority over food, supplements, and OTC products to a new Food and Nutraceutical Agency (FNA), allowing the FDA to be restructured as the Drug and Device Agency (DDA), to refocus on pharmaceuticals and medical devices. While complete reform requires Congressional action, interim policy shifts are urgently needed to improve public health. Broader structural changes—including overhauling the Affordable Care Act, eliminating waste and fraud, redesigning regulatory and insurance systems, and eliminating intermediaries are essential to reducing costs, improving care, and transforming national and global health outcomes. The information provided herein can serve as a White Paper to help reform health agencies and healthcare systems for greater efficiency and lower costs in the USA and globally. Full article

In such a dynamic and increasingly digitalized financial sector, many sophisticated fraudulent and cybercrime activities continue to challenge conventional detection systems. This research study explores a decentralized anomaly detection framework using deep autoencoders, designed to meet the dual imperatives of fraud detection effectiveness and user data privacy. Instead of relying on centralized aggregation or data sharing, the proposed model simulates distributed training across multiple financial nodes, with each institution processing data locally and independently. The framework is evaluated using two real-world datasets, the Credit Card Fraud dataset and the NeurIPS 2022 Bank Account Fraud dataset. The research methodology applied robust preprocessing, the implementation of a compact autoencoder architecture, and a threshold-based anomaly detection strategy. Evaluation metrics, such as confusion matrices, receiver operating characteristic (ROC) curves, precision–recall (PR) curves, and reconstruction error distributions, are used to assess the model’s performance. Also, a threshold sensitivity analysis has been applied to explore detection trade-offs at varying levels of strictness. Although the model’s recall remains modest due to class imbalance, it demonstrates strong precision at higher thresholds, which demonstrates its utility in minimizing false positives. Overall, this research study is a practical and privacy-conscious approach to fraud detection that aligns with the operational realities of financial institutions and regulatory compliance toward scalability, privacy preservation, and interpretable fraud detection solutions suitable for real-world financial environments. Full article

Human trafficking, a grave violation of human rights, frequently intersects with financial crimes, notably identity theft and coercive debt accumulation. This creates complex challenges for victims, survivors, and law enforcement. Victims of human trafficking are often coerced and/or threatened into committing various forms of crime, referred to as “forced criminality.” In recent years, this trend of criminality has moved from violent crimes to financial crimes and fraud, including identity theft, synthetic identity fraud, and serving as money mules. This phenomenon, termed “forced fraud”, exacerbates the already severe trauma experienced by victims (referred to as both victims and survivors throughout, consistent with trauma-informed terminology) trapping them in a cycle of financial instability and legal complications. Traffickers often coerce their victims into opening credit lines, taking out loans, or committing fraud all in their own names, leading to ruined credit histories and insurmountable debt. These financial burdens make it extremely difficult for survivors to rebuild their lives post-trafficking. This paper explores the mechanisms of forced fraud, its impact on survivors, and the necessary legislative and financial interventions to support survivors. By examining first-hand accounts and social and policy efforts from a range of sources, this paper highlights the urgent need for comprehensive support systems that address both the immediate and long-term financial repercussions of human trafficking. Full article

Past and current advances in artificial intelligence (AI) have resulted in a significant impact on business and accounting. Over time, AI has slowly transformed from the 1950s to today, from rule-based systems, also known as expert systems, to the deep learning architectures and sophisticated neural networks of modern generative AI. Early AI accounting applications of expert systems included a GAAP-based expert system to assess the appropriate accounting treatment for business combinations and an expert system to determine the proper type of audit report to issue. Recent accounting expert systems have been developed for document analysis, fraud detection, evaluating credit risk, and corporate default forecasting. The purpose of this study is to examine key events in the history of AI, current applications, and potential future effects pertaining to management accounting and society overall. In addition, the relationship of AI with economic and social factors will be evaluated. The study’s findings will be of interest to management accountants, businesspersons, academic researchers, and others who are concerned with artificial intelligence and its impact on management accounting and society overall. Full article

Rapid urbanization and the rising demand for sustainable living have encouraged the growth of smart cities, which incorporate innovative technologies to ameliorate environmental sustainability, optimize resource management, and improve living standards. The convergence of blockchain (BC) technology and the Internet of Things (IoT) presents transformative convenience for managing smart cities and achieving sustainability goals. In fact, blockchain technology combined with IoT devices provides a decentralized, transparent, and safe framework for managing massive volumes of data produced by networked sensors and systems. By guaranteeing accountability, minimizing fraud, and maximizing resource use, blockchain not only facilitates the smooth operation of smart city infrastructures but also encourages sustainable habits. The various uses of blockchain technology in smart city management and its contribution to sustainability objectives are examined in this study. Through an examination of important domains like energy distribution, waste management, transportation systems, healthcare, and governance, the research shows how blockchain promotes effective data exchange and data security, builds stakeholder trust, and makes it possible to establish decentralized organizations to improve decision-making. Full article

The growth of the older adult population, their wealth accumulation, and vulnerabilities from aging have contributed to increasing rates of abuse, fraud, and financial exploitation. However, the current responses and services are fragmented and ineffectual. This paper develops a novel strategy for addressing the variation in response and victim service provision through the development of a problem-solving court that is informed by the principles of restorative justice. Given the unique challenges, cases, and population, a problem-solving court for crimes against older adults will provide tailored interventions, responses, and sanctions while ensuring that older adult victims and their communities are at the center of the criminal justice process and that their needs are prioritized. Research on problem-solving courts; restorative justice; and older adult abuse, fraud, and financial exploitation are integrated with data from a case study of older adult financial exploitation in a large retirement community to develop the model problem-solving court. Consistent with best practices in victim services, the model court will provide comprehensive services in a one-stop location, while simultaneously increasing accountability for offenders who prey on this vulnerable population. The paper concludes with a plan to guide the implementation and evaluation of the proposed model problem-solving court for older adult abuse, fraud, and exploitation. Full article

Electoral fraud, particularly multiple voting, undermines the integrity of democratic processes. To address this challenge, this study introduces an innovative facial recognition system that integrates an enhanced 50-layer Residual Network (ResNet50) architecture with Additive Angular Margin Loss (ArcFace) and Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection. Using the Mahalanobis distance, the system verifies voter identities by comparing captured facial images with previously recorded biometric features. Extensive evaluations demonstrate the methodology’s effectiveness, achieving a facial recognition accuracy of 99.85%. This significant improvement over existing baseline methods has the potential to enhance electoral transparency and prevent multiple voting. The findings contribute to developing robust biometric-based electoral systems, thereby promoting democratic trust and accountability. Full article

Artificial Intelligence is a disruptive technology that is revolutionizing the accounting sector, e.g., by reducing costs, detecting fraud, and generating reports. However, the manual maintenance of booking ledgers remains a significant challenge, particularly for small and medium-sized enterprises. The usage of AI technologies in this area is rarely considered in the literature depite a significant interest in using AI for other acounting-related activities. Our study, which was conducted during 2023–2024, utilizes natural language processing and machine learning to construct a predictive model that accurately matches bank transaction statements with accounting records. The study employs Feedforward Neural Networks and Support Vector Machines with various settings and compares their performance with that of previous models embedded in similar predictive tasks. Additionally, as a baseline model, a software called Contofox, a rule-based system capable of classifying accounting records by manually creating rules to match bank statements with accounting records, is used. Furthermore, this study evaluates the business value of the model through an interview with an accounting expert, highlighting the potential benefits of artifacts in enhancing accounting processes. Full article

Despite the recognized importance of ethical idealism in enhancing fraud detection in the audit context, there remains limited understanding of the mediating role of conscientiousness in the relationship between auditors’ ethical idealism and fraud detection. The purpose of this paper is to analyze the influence of auditors’ ethical idealism on fraud detection via using the conscientiousness of auditors as a mediator. This study employs a cross-sectional approach, and quantifiable data were gathered via structured surveys from 401 external auditors employed in offices licensed to practice the accounting and auditing profession in Saudi Arabia. Accidental sampling was used to ensure a representative sample of auditors in Saudi audit firms. This study utilized the Structural Equation Modeling (SEM) technique to examine the relationships between ethical idealism (as independent variable), conscientiousness (as mediating variable), and fraud detection (as dependent variable). The result showed that ethical idealism has a positive effect on auditors’ detection of fraud. However, the proposed mediation effect of conscientiousness between ethical Idealism and fraud detection was not statistically significant. The research underscores that the ethical idealism of auditors can enhance fraud detection, especially when accounting firms give priority to ethical training programs, ensuring that they are guided by strong ethical idealism rather than personal conscientiousness. Full article

Gas chromatography–ion mobility spectrometry (GC-IMS) is an interesting candidate to face geographical origin declaration fraud in dehydrated apple samples. It allows the collection of the peculiar fingerprints of the analysed samples with the bi-dimensional separation of volatile molecules, based on their polarity and their dimension and shape. It represents a rapid, cost-effective, and sensitive solution for food authenticity issues. A design of experiment (DoE) led to robust sampling, taking into account different factors, such as harvesting year, the presence of peel, variety. The sample preparation was limited as it required only the milling of the dehydrated apple dices before the analysis. The GC-IMS analytical method permitted us to obtain of a 3D graph in 11 min, and the multivariate statistical analysis returned a clear separation between Italian and non-Italian (French, Chinese, Hungarian, Polish) samples, considering both unsupervised and supervised approaches. The statistical model, created employing a training set, was applied on a further test set, with a good overall performance. Thus, GC-IMS could play a relevant role as a tool to prevent/fight false origin declaration frauds and also, potentially, other kinds of food authenticity and safety frauds. Full article

The rapid growth of the Internet of Things (IoT) has introduced critical challenges related to security, scalability, and data integrity. Blockchain technology, with its decentralized, immutable, and tamper-resistant framework, presents a transformative solution to address these challenges. This study explores blockchain applications in the IoT, focusing on security, automation, scalability, and data sharing. Industry-specific applications, including supply chain management, smart cities, and healthcare, highlight the potential of blockchains to optimize operations, ensure compliance, and foster innovation. Additionally, blockchain technology enables robust audit trails, enhances accountability, and reduces fraud in sensitive IoT applications, such as finance and healthcare. The synergy between blockchains and the IoT creates a secure and transparent platform for managing device interoperability and data exchange, fostering seamless communication between diverse IoT components. Furthermore, this paper discusses layer 2 scaling techniques and tokenization to address scalability, ownership, monetization, and cost challenges, providing practical solutions for real-world deployments. Future directions emphasize integrating blockchain systems with artificial intelligence (AI), machine learning (ML), and edge computing, offering groundbreaking capabilities to further revolutionize IoT ecosystems. By merging these advanced technologies, organizations can build secure, scalable, and intelligent systems to drive innovation and trust. Full article

This study examines the key drivers behind the adoption of artificial intelligence (AI) in the accounting profession, emphasizing the influence of AI-related knowledge, personality traits, and professional roles. By applying Decision Tree Regression analysis to survey data from accounting professionals, our research identifies AI knowledge as the strongest determinant of AI adoption, underscoring the importance of expertise in technology acceptance. While personality traits play a secondary role, extraversion and openness emerge as significant factors influencing adoption intentions. The study further explores AI applications in financial auditing, tax compliance, and fraud detection, clarifying the specific accounting domains impacted by AI integration. These findings offer valuable guidance for policymakers, educators, and business leaders aiming to equip the accounting workforce with the necessary skills and mindset to navigate the AI-driven transformation of the profession. Full article