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Blockchains are the backbone behind cryptocurrency networks, which have developed rapidly in the last two decades. However, this growth has brought several challenges due to the features of these networks, specifically anonymity and decentralization. One of these challenges is the fight against fraudulent activities performed in these networks, which, among other things, involve financial schemes, phishing attacks or money laundering. This article will address the problem of identifying fraud cases among a large set of transactions extracted from the Bitcoin network. More specifically, our study’s goal was to find reliable techniques to label Bitcoin transactions, taking into account their features. The approach followed involved two kinds of Machine Learning methods. On the one hand, anomaly detection algorithms were applied to determine whether fraudulent activities tend to show anomalous behaviour without resorting to manually obtained labels. On the other hand, Heterogeneous Graph Transformers were used to leverage the heterogeneous relational nature of the cryptocurrency information. As a result, the article will provide reasonable conclusions to acknowledge that unsupervised approaches can be useful for fraud detection on blockchain networks. Furthermore, the effectiveness of supervised graph methods was revalidated, emphasizing the importance of data heterogeneity.

Financial fraud activities are very frequent in recent days in rural areas where people are more in need of money and desperate to make some in any way possible. This research explores the nature, causes, and consequences of rural financial fraud with the impact on rural life and the responses of law enforcement to these occurrences. The primary data for this study was collected through in-depth interview from 24 victims of financial fraud, and 4 KIIs were conducted to get the data through semi-structured questionnaires using the qualitative research method, and the findings are presented using thematic analysis. This study found greed, temptation, and believing in fraudsters are the main causes of these fraud incidents. The fraudster, in all cases, motivated the victims in a certain way and took as much money as possible before running away, and the responses of law enforcement, credit cooperative authorities, and the local government are not satisfactory as they did nothing much to prevent and combat these fraudulent activities. The impact of these fraudulent schemes is severe, as the victims faced financial breakdown, family pressure, and psychological pressure. Moreover, reducing cravings for money, inspecting schemes before investing, monitoring by the respective authorities and building awareness among people can be preventive factors to stop these kinds of rural financial fraud.

Purpose The main objective of this study is to investigate the mediating effect of fraud triangle elements in the relationship between the presence of female legislators and fraud. In addition, this study examines the impact of fraud triangle elements on fraud, as well as the role of female legislators in contributing to these elements. Design/methodology/approach This paper conducted the study in all Indonesian provincial governments between 2016 and 2018, using purposive sampling techniques and observation periods. This process yielded a total sample of 102 observations. This research uses factor analysis, principal component analysis, and mediating regression methods. Before carrying out factor analysis and PCA to reduce variables and mediation analysis, multivariate assumption tests have been carried out. Findings The results of this study indicate that the fraud triangle element does not have a mediating effect on the relationship between the presence of female legislators and fraud. On the contrary, the presence of female legislators has a direct effect on fraud, indicating that the presence of female legislators can provide variation in policymaking, especially in terms of improving performance, supervision, strengthening governance and preventing fraudulent activities. This study encourages fraud prevention efforts so that the Indonesian Government prioritizes strengthening ethical character among state administrators by considering the gender dimension accompanied by an in-depth evaluation of individual track records. Research limitations/implications This research has limitations in terms of sample size which needs to be expanded to district and city governments. Then, this research only uses fraud triangle theory and it can be developed to carry out investigations using fraud diamond theory. Practical implications This paper critically analyzes the existence of female legislators through an approach to controlling elements of the fraud triangle in preventing fraud, especially in Indonesia which is still faced with high levels of fraud. Social implications Characteristics or demographics represented by the presence of female legislators play an important role in preventing and reducing fraud in Regional Governments in Indonesia. The presence of female legislators provides diversity in behavior and taking action, especially in monitoring and ratifying budgets and regulations. Originality/value This paper contributes to the literature by investigating the existence of female legislators in controlling fraud triangle elements and mitigating fraud, which are still unexplored, by combining gender factors in legislators and controlling fraud risk elements simultaneously within a single framework of the context of provincial government in Indonesia. Enhance understanding and provide a framework to inform the Indonesian Government’s strategies for mitigating fraud incidents.

Social media platforms are currently confronted with a substantial problem concerning the presence of fake accounts, which pose a threat by spreading harmful content, spam, and misinformation. This study aims to address the problem by differentiating between fake and real X accounts (formerly Twitter). The need to mitigate the negative impact of fake accounts on online communities serves as the driving force for this work, with the goal of developing an effective method for identifying fake accounts and their fraudulent activities, such as posting harmful links, engaging in spamming behaviors, and disrupting online communities. The scope of this work focuses specifically on fake Twitter account detection. A comprehensive approach is taken, leveraging user information and tweets to discern between genuine and fake accounts. Various deep learning architectures are proposed and implemented, utilizing different optimizers and evaluating performance metrics. The models are trained and tested using a collected dataset, augmented to cover diverse real-life scenarios. The results show promising progress in distinguishing between fake and real accounts, revealing that the inclusion of tweet content along with user metadata does not significantly improve the classification of fake accounts. It also highlights the importance of selecting appropriate optimizers. The implications of this study are relevant to social media platforms, users, and researchers. The findings provide insights into combating fake accounts and their fraudulent activities, contributing to the enhancement of online community safety. While the research is specific to Twitter, the methodology and insights gained may be potentially generalizable to other social media platforms.

Purpose Money laundering has affected the economy in different ways, where the fraudulent activities are either domestic or abroad, resulting in financial instability globally. Anti-money laundering (AML) system is applied to detect and report any suspicious transactions. There are numerous approaches, techniques and algorithms in AML that are applied to fight against money laundering. This study aims to understand, identify and document the AML techniques applied to detect and prevent money laundering activities. Design/methodology/approach A systematic literature review is applied for searching articles based on methods used for AML from the electronic database platform. For review, data is considered from journal articles, books and conference proceedings with a time framework from 2014 to 2024. Findings In total, 53 papers were selected in the domain of money laundering concepts, issues and techniques of AML. The review articles are on the techniques of AML, such as machine learning, data mining, graph networks and artificial intelligence, which are applied to detect and prevent money laundering issues. Originality/value Money laundering, being a global issue, is a threat to the economy and society. Detecting money laundering activities is utmost required; this study contributes in selecting the articles that are involved in the application of techniques of AML in detecting and preventing money laundering activities. The results of this study can provide support instruments to identify the better AML techniques that are useful for practitioners and industry experts working in the AML domain. Further research can be explored with other AML techniques.

The main objective of this study was to investigate how food fraud is perceived among consumers in Serbia and Montenegro. A total of 1264 consumers from the two countries participated in an online survey during the second half of 2022, using Google forms®. In the Serbian population, older or highly educated respondents are aware of different types of fraudulent activities such as substitution, mislabeling, concealment, and counterfeiting. Dilution is mostly recognized by women, the younger population, and students. Consumers believe that trust is the most important factor when purchasing food. The highest level of agreement regarding food fraud is that such activities may pose serious health risks to consumers, and that food inspection services are the most responsible actors in the food chain continuum. When it comes to purchasing food, open green markets are most trustworthy, followed by hypermarkets. Concerning the types of food, fish is most susceptible to fraud, followed by olive oil. This study builds upon existing knowledge of food consumers about food fraud in Europe.

Detecting fake multimodal property listings is a significant challenge in online real estate platforms due to the increasing sophistication of fraudulent activities. The existing multimodal data fusion methods have several limitations and strengths in identifying fraudulent listings. Single-level fusion models whether at the feature, decision, or intermediate level struggle with balancing the contributions of different modalities leading to suboptimal decision-making. To address these problems, a dual-level fusion from multimodal for fake real estate listings detection is proposed. The dual-level fusion allows the integration of detailed features from text and image data to be performed at an early stage, followed by the metadata fusion at the decision stage in order to obtain a more comprehensive final classification. Furthermore, a new weighting scheme is introduced to optimize Dempster-Shafer in decision fusion to help the model achieve optimal performance and as a result, our method improves the classification. The Dempster-Shafer without class weightage lacks the flexibility to adapt to varying levels of uncertainty or importance across different classes. In Class Weighted Dempster-Shafer in Dual Level Fusion (CWDS-DLF), we employ advanced models (XLNet for text and ResNet101 for images) for feature extraction and use the Dempster-Shafer theory for decision fusion. A new weighting scheme, based on Bayesian optimization, was used to assign optimal weights to the 'fake' and 'not fake' classes, thereby enhancing the Dempster-Shafer theory in the decision fusion process. The CWDS-DLF was evaluated on the property listing website dataset and achieved an F1 score of 96% and an accuracy of 93%. A t-test confirms the significance of these improvements ( < 0.05), demonstrating the effectiveness of our method in detecting fake property listings. Compared to other models, including 2D-convolutional neural network (CNN), XGBoost, and various multimodal approaches, our model consistently outperforms in precision, recall, and F1-score. This underscores the potential of integrating multimodal analysis with sophisticated fusion techniques to enhance the detection of fake property listings, ultimately improving consumer protection and operational efficiency in online real estate platforms.

The rapid expansion of online learning has made education more accessible but has also introduced significant challenges in maintaining academic integrity, particularly during online exams. For certain types of exams, students are prohibited from connecting to the Internet to prevent them from accessing unauthorized resources, utilizing generative artificial intelligence tools, or engaging in other forms of cheating. However, in online exams, students must remain connected to the Internet. Most existing online proctoring systems rely on various devices to monitor students’ actions and environments during the exam, focusing on tracking physical behavior, such as facial expressions, eye movements, and the presence of unauthorized materials, rather than analyzing the students’ work within their computers. This often requires human review to determine whether students are engaging in unauthorized actions. This article presents the development and evaluation of a machine-learning-based assistant designed to assist instructors in detecting fraudulent activities in real-time during online programming exams. Our system leverages a convolutional neural network (CNN) followed by a recurrent neural network (RNN) and a dense layer to analyze sequences of screenshot frames captured from students’ screens during exams. The system achieves an accuracy of 95.18% and an F 2 -score of 94.2%, prioritizing recall to emphasize detecting cheating instances, while minimizing false positives. Notably, data augmentation and class-weight adjustments during training significantly enhanced the model’s performance, while transfer learning and alternative loss functions did not provide additional improvements. In post-deployment feedback, instructors expressed high satisfaction with the system’s ability to assist in the rapid detection of cheating, reinforcing the potential of machine learning to support real-time monitoring in large-scale online exams.

The last two decades have witnessed high-profile corporate accounting scandals and multi billion-dollar frauds. Since then, forensic accounting has been in focus and has played a prominent role in discovering financial statement frauds. This research aims to analyze the applicability of the Beneish M-Score model on small and medium enterprises (SMEs) in Federation of Bosnia and Herzegovina (FBiH). Based on a sample that includes 4,580 small and medium enterprises, data will be analyzed using audited financial statements in the period from 2008 to 2015. By using independent sample t-test, correlation, and regression, it has been concluded that Beneish model is indeed applicable on the market of FBiH and aids effectively in the detection of fraud in financial statements. The study describes the comparison of different industry sectors regarding the possible manipulators and serves as a solid foundation for further research in the area of forensic accounting.

Corruption and thefts of public assets harm a diffuse set of victims, weakens confidence in public institutions, damages the private investment climate, and threatens the foundations of the society as a whole. In developing countries with scarce public resources, the cost of corruption is an impediment to development: developing countries lose between US