Search Results (222)

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

According to the literature, the advancement of information and communication technology (ICT) has increased individual exposure to scams, turning fraud victimization into a significant concern. While prior research has primarily focused on socio-demographic predictors of fraud victimization, this study adopts a behavioral perspective that is grounded in the Signal Detection Theory (SDT) to investigate the likelihood determinants of individuals becoming fraud victims. Using survey data of 671 Indonesian respondents analyzed with the Partial Least Squares Structural Equation Modeling (PLS-SEM), we explored the roles of vigilance and financial literacy in moderating the relationship between fraud exposure and victimization. Our findings substantiate the notion that higher exposure to fraudulent activity significantly increases the likelihood of victimization. The results also show that vigilance negatively moderates the relationship between fraud exposure and fraud victimization, suggesting that individuals with higher vigilance are better at identifying scams, thereby decreasing their likelihood of becoming fraud victims. Furthermore, financial literacy is positively related to vigilance, indicating that financially literate individuals are more aware of potential scams. However, the predictive power of financial literacy on vigilance is relatively low. Hence, while literacy helps a person sharpen their indicators for detecting fraud, psychological, behavioral, and contextual factors may also affect their vigilance and decision-making. Full article

This study addresses the challenges of data imbalance and missing values in credit card transaction datasets by employing mode-based imputation and various machine learning models. We analyzed two distinct datasets: one consisting of European cardholders and the other from American Express, applying multiple machine learning algorithms, including Artificial Neural Networks, Convolutional Neural Networks, and Gradient Boosted Decision Trees, as well as others. Notably, the Gradient Boosted Decision Tree demonstrated superior predictive performance, with accuracy increasing by 4.53%, reaching 96.92% on the European cardholders dataset. Mode imputation significantly improved data quality, enabling stable and reliable analysis of merged datasets with up to 50% missing values. Hypothesis testing confirmed that the performance of the merged dataset was statistically significant compared to the original datasets. This study highlights the importance of robust data handling techniques in developing effective fraud detection systems, setting the stage for future research on combining different datasets and improving predictive accuracy in the financial sector. Full article

This study examines how U.S. immigration policies enact legal violence and multigenerational punishment through the spousal reunification process, particularly in mixed-status, transnational families. Building on the concept of “deportability,” we introduce “importability” to describe a beneficiary’s potential to secure permanent residency, which varies according to social markers such as race, gender, and region of origin. Drawing from a content analysis of threads on the Immigration Pathways (IP) web forum, we analyze discussions among U.S. citizen petitioners navigating marriage-based green card applications, with a focus on experiences involving administrative processing (AP) (i.e., marriage fraud investigations). Our findings show that couples who do not align with the state’s conception of “proper” family—particularly U.S. citizen women petitioning for Black African partners—face intensified scrutiny, long delays, and burdensome requirements, including DNA tests and surveillance. These bureaucratic obstacles produce prolonged family separation, financial strain, and diminished sense of belonging, especially for children in single-parent households. Through the lens of “importability,” we reveal how legal violence and multigenerational punishment of immigration policies on mixed-status families beyond deportation threats, functioning as a gatekeeping mechanism that disproportionately affects marginalized families. This research highlights the understudied consequences of immigration policies on citizen petitioners and contributes to a broader understanding of inequality in U.S. immigration enforcement. 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

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

The surge in credit fraud incidents poses a critical threat to financial systems, driving the need for robust and adaptive fraud detection solutions. While various predictive models have been developed, existing approaches often struggle with two persistent challenges: extreme class imbalance and delays in detecting fraudulent activity. In this study, we propose an unsupervised Adversarial Autoencoder (AAE) framework designed to tackle these challenges simultaneously. The results highlight the potential of our approach as a scalable, interpretable, and adaptive solution for real-world credit fraud detection systems. 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

Financial fraud detection is a critical application area within the broader domains of cybersecurity and intelligent financial analytics. With the growing volume and complexity of digital transactions, the traditional rule-based and shallow learning models often fall short in detecting sophisticated fraud patterns. This study addresses the challenge of accurately identifying fraudulent financial activities, especially in highly imbalanced datasets where fraud instances are rare and often masked by legitimate behavior. The existing models also lack interpretability, limiting their utility in regulated financial environments. Experiments were conducted on three benchmark datasets: IEEE-CIS Fraud Detection, European Credit Card Transactions, and PaySim Mobile Money Simulation, each representing diverse transaction behaviors and data distributions. The proposed methodology integrates a transformer-based encoder, multi-teacher knowledge distillation, and a symbolic belief–desire–intention (BDI) reasoning layer to combine deep feature extraction with interpretable decision making. The novelty of this work lies in the incorporation of cognitive symbolic reasoning into a high-performance learning architecture for fraud detection. The performance was assessed using key metrics, including the F1-score, AUC, precision, recall, inference time, and model size. Results show that the proposed transformer–BDI model outperformed traditional and state-of-the-art baselines across all datasets, achieving improved fraud detection accuracy and interpretability while remaining computationally efficient for real-time deployment. Full article

The existing financial payment system has inherent credit card fraud problems that must be solved with strong and effective solutions. In this research, a combined deep learning model that incorporates a convolutional neural network (CNN), long-short-term memory (LSTM), and fully connected output layer is proposed to enhance the accuracy of fraud detection, particularly in addressing the class imbalance problem. A CNN is used for spatial features, LSTM for sequential information, and a fully connected output layer for final decision-making. Furthermore, SMOTE is used to balance the data and hyperparameter tuning is utilized to achieve the best model performance. In the case of hyperparameter tuning, the detection rate is greatly enhanced. High accuracy metrics are obtained by the proposed CNN-LSTM (CLST) model, with a recall of 83%, precision of 70%, F1-score of 76% for fraudulent transactions, and ROC-AUC of 0.9733. The proposed model’s performance is enhanced by hyperparameter optimization to a recall of 99%, precision of 83%, F1-score of 91% for fraudulent cases, and ROC-AUC of 0.9995, representing almost perfect fraud detection along with a low false negative rate. These results demonstrate that optimization of hyperparameters and layers is an effective way to enhance the performance of hybrid deep learning models for financial fraud detection. While prior studies have investigated hybrid structures, this study is distinguished by its introduction of an optimized of CNN and LSTM integration within a unified layer architecture. Full article

This study systematically reviews academic research on artificial intelligence (AI) in financial fraud prevention. Employing a bibliometric approach, we analyzed 137 peer-reviewed articles published between 2015 and 2025, sourced from Scopus, Web of Science, and ScienceDirect. Using Bibliometrix, we mapped the field’s intellectual structure, collaboration patterns, and thematic clusters. Research interest has surged since 2019, led mainly by China and India, though the literature is mostly technical, with limited social science engagement. Three main themes emerged: AI-based fraud detection models, blockchain and fintech integration, and big data analytics. Despite growing output, international collaboration and focus on ethical, regulatory, and organizational issues remain limited. These insights provide a foundation for advancing both research and practical AI-driven fraud mitigation. 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

The rising complexity of financial fraud in highly digitalized regions such as the Gulf Cooperation Council (GCC) poses challenging issues owing to class imbalance, adversarial attacks, concept drift, and explainability requirements. This paper suggests a hybrid machine-learning framework (HMLF) that incorporates SMOTEBoost and cost-sensitive learning to address imbalances, adversarial training and FraudGAN to ensure robustness, DDM and ADWIN to achieve adaptive learning, and SHAP, LIME, and human-in-the-loop (HITL) analysis to ensure explainability. Employing real transaction data from the GCC banks, the framework is tested through a design science research approach. Experiments illustrate significant gains in fraud recall (from 35% to 85%), adversarial robustness (attack success rate decreased from 35% to 5%), and drift recovery (within 24 h), while retaining operational latency below 150 milliseconds. This paper substantiates that incorporating technical resilience with institutional constraints offers an auditable, scalable, and regulation-compliant solution for detecting fraud in high-risk financial contexts. Full article

In modern digital transactions involving government agencies, financial institutions, and commercial enterprises, reliable identity verification is essential to ensure security and trust. Traditional methods, such as submitting photocopies of ID cards, are increasingly susceptible to identity theft and fraud. To address these challenges, this study proposes a novel and robust identity verification framework that integrates super-resolution preprocessing, a convolutional neural network (CNN), and Monte Carlo dropout-based Bayesian uncertainty estimation for enhanced facial recognition in electronic know your customer (e-KYC) processes. The key contribution of this research lies in its ability to handle low-resolution and degraded facial images simulating real-world conditions where image quality is inconsistent while providing confidence-aware predictions to support transparent and risk-aware decision making. The proposed model is trained on facial images resized to 24 × 24 pixels, with a super-resolution module enhancing feature clarity prior to classification. By incorporating Monte Carlo dropout, the system estimates predictive uncertainty, addressing critical limitations of conventional black-box deep learning models. Experimental evaluations confirmed the effectiveness of the framework, achieving a classification accuracy of 99.7%, precision of 99.2%, recall of 99.3%, and an AUC score of 99.5% under standard testing conditions. The model also demonstrated strong robustness against noise and image blur, maintaining reliable performance even under challenging input conditions. In addition, the proposed system is designed to comply with international digital identity standards, including the Identity Assurance Level (IAL) and Authenticator Assurance Level (AAL), ensuring practical applicability in regulated environments. Overall, this research contributes a scalable, secure, and interpretable solution that advances the application of deep learning and uncertainty modeling in real-world e-KYC systems. Full article

This study examines the relevance of the Fraud Pentagon Theory in detecting fraudulent financial reporting among companies listed on the Bucharest Stock Exchange. While financial reporting is essential for informed stakeholder decisions, requiring information to be accurate, reliable, and fairly presented and pressure to meet expectations can lead to manipulation. The Fraud Pentagon Theory identifies five potential drivers of such behavior: pressure, opportunity, rationalization, capability, and arrogance. This research contributes to the literature by empirically testing the theory in the Romanian context, an emerging market with limited prior analysis, using a sample of 62 listed companies over the 2017–2021 period. Regression analysis was applied, using the Dechow F-score, which combines accrual quality and financial performance to assess the likelihood of fraudulent financial reporting. The findings reveal that not all dimensions of the theory significantly affect the likelihood of fraudulent reporting. Specifically, pressure-related factors (financial performance and financial stability) were found to be statistically significant, while external pressure, opportunity (external auditor quality and nature of industry), rationalization (change of auditor), capability (change of director), and arrogance (number of CEO’s pictures) did not show significant influence in the Romanian framework. These results highlight the importance of contextual factors such as market structure, governance practices, and stakeholder expectations, suggesting that fraudulent reporting risk indicators may vary across different economic environments. Full article