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Copyright of Dilemas Contemporáneos: Educación, Política y Valores is the property of Dilemas Contemporaneos: Educacion, Politica y Valores and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To optimise the regulatory approach, the China Securities Regulatory Commission (CSRC) introduced the double‐random inspection policy (DRIP), which mandates that the provincial branches of the CSRC randomly select at least 5% of local listed firms each year and randomly assign inspectors to conduct on‐site inspections of their information disclosure and corporate governance practices. This paper investigates the real effect of the DRIP on corporate financial fraud. Performing a multi‐period synthetic difference‐in‐differences model (SDID), we first find that the random inspections of CSRC have a positive causal effect on the probability of exposing corporate financial fraud. Furthermore, our heterogeneity analysis reveals that this effect is more pronounced for private firms and firms with poor accounting information quality. We then delve into the mechanisms through which random inspections affect corporate financial fraud. Our findings suggest that random inspections influence corporate behaviour by increasing media and investor attention, as well as prompting the issuance of inquiry letters by stock exchanges. Finally, we examine the economic consequences of random inspections and find that random inspections by the CSRC reduce firms' stock price crash risk. [ABSTRACT FROM AUTHOR]

This paper examines the legal loopholes and regulatory measures taken in response to pyramid and Ponzi schemes, specifically analyzing the lessons learned from Albania's financial crisis in the 1990s and what insights Indonesia can learn. The collapse of these schemes in Albania, which had a catastrophic effect on the economy and triggered an entire social and political crisis, exposed significant measures in governance negligence, financial supervision, legal frameworks, and understanding of investments. Through an in-depth examination of the Albanian catastrophe experience, this paper examines the legal framework in terms of inadequate regulations, government negligence due to lack of experience handling Ponzi and pyramid scheme issues, and lack of investment literacy. The paper explores how Indonesia can enhance its legal and regulatory environment by drawing lessons from Albania's mistakes in the past. Suggestions involve strengthening financial authority roles, establishing specific regulations to regulate pyramid and Ponzi schemes, and providing comprehensive investment literacy education for investors. This study highlights the significance of legislative reform and efficient governance to prevent financial schemes that take advantage of legal loopholes and uneducated investors, ultimately leading to a more resilient and stable financial system. By examining Albania's experience, Indonesia may establish a proactive strategy to protect its citizens and economy from the dangers of pyramid and Ponzi schemes. [ABSTRACT FROM AUTHOR]

This article assesses how good character is used to discount sentences in tax and benefit fraud cases in Aotearoa New Zealand. Four factors are highlighted. The first is a focus on neoliberal priorities that privilege "good citizens" who are primarily tax fraudsters, with the provision of sentence discounts to ensure the offender can remain in employment. The second is sentence reduction, which is primarily visible in tax cases when repayment of the outstanding funds is made. While the average value of benefit fraud is less than a quarter of the average tax fraud, typically, money obtained from benefit fraud is used for living expenses and not available for repayment. Third, only tax fraudsters benefited from anticipated reparation, on the expectation from the court that this would occur after sentencing. Finally, the ways in which prior tax offending is classified leave tax offenders in a stronger position to claim no prior offending and receive a sentence reduction for good character. These factors combine to result in unfair outcomes that benefit tax offenders. [ABSTRACT FROM AUTHOR]

The financial markets' growing complexity and the exponential growth of data availability have made fraud detection in financial statements a critical and challenging issue. This review article offers a thorough summary of the different approaches and procedures employed in the detection of fraudulent financial statements. It explores traditional statistical methods, machine learning algorithms, and hybrid models, highlighting their strengths and limitations in identifying anomalies and irregularities. The paper also discusses the contribution of artificial intelligence and big data analytics to improving the precision and effectiveness of fraud detection. Furthermore, it underscores the need for robust regulatory frameworks and ethical guidelines to prevent misuse and ensure transparency. The review concludes with a discussion on future research directions, emphasizing the potential of emerging technologies in revolutionizing the field of financial fraud detection. [ABSTRACT FROM AUTHOR]

Are business groups more likely to commit financial fraud when they are politically connected? Many past studies and anecdotal evidence have pointed out that business groups may behave more opportunistically when they are politically connected. However, the nature of the business group-government relationship evolves amid governance reform in many countries, making it difficult for business groups to abuse their political connections. In this research, we examine the business group-government relationship through the lens of social exchange and find a deterring effect of political connections on a connected business group's propensity to commit fraud. Our results indicate that business groups are less likely to commit financial fraud when the extent of their political connection is high. By doing so, politically connected firms can prove themselves as legitimate business partners for the government and can more effectively secure a position to leverage their political connections. We additionally find that such an exchange relationship is weakened under a business group-unfriendly government due to their hostile relationships. [ABSTRACT FROM AUTHOR]

In the article the author examines the acts which are considered to be fraudulent in the field of banking. The purpose of the study is to define the concept of fraud in the field of banking, the acts which are related to it, and to formulate proposals to the Criminal Code of Ukraine based on a thorough analysis of the provisions of current criminal law and scientific sources. The author notes that fraud in banking poses a threat to banking in particular and the economy in general, since their commission entails rather serious negative consequences and affects the entire financial and economic security of the State. To ensure the efficient operation of banking institutions and their financial security, it is necessary to develop and implement an effective fraud detection and prevention system. This includes the use of advanced software solutions and compliance with security standards when working with electronic payment systems, identifying potential threats and using appropriate measures to prevent and deter fraud in the banking sector. The article notes that in order to effectively prevent fraud in the banking sector, it is necessary to first understand its essence, define the definition of this concept and generally characterize the actions which are the objective side of criminal offenses related to the commission of the analyzed types of fraud. On the basis of a thorough legislative, regulatory and scientific study and harmonization of the very definition of «fraud in the field of banking», the author proposes amendments and proposals to the Criminal Code of Ukraine. Based on the scientific analysis of the definition of the concept under study, it can be concluded that frauds committed in the banking sector include the following acts: frauds with bank accounts, frauds with credit cards, frauds related to forgery of documents for obtaining loans, as well as illegal actions by bank employees which result in fraudulent actions. [ABSTRACT FROM AUTHOR]

This study examines some of the documentation tools and techniques that forensic accountants, internal auditors, external auditors, and others can use to document accounting and financial reporting systems under investigation. While prior research has addressed these items piecemeal, this study is the first to incorporate them, along with current related research and theoretical foundation, and relate them in aggregate to the work of forensic accountants, internal auditors, external auditors, and others. Inputs, processes, and outputs of modern accounting and financial reporting systems are often difficult to fully grasp, with weaknesses obscured by the complexities of the system. These weaknesses make a system vulnerable to fraudsters, embezzlers, hackers, and others who will take advantage of system weaknesses to perpetrate financial fraud, embezzlement, or other financial crimes. Documentation tools and techniques examined in this study will be useful to forensic accountants, internal auditors, external auditors, and others for identifying the components, processes, and potential weaknesses of accounting and financial reporting systems. [ABSTRACT FROM AUTHOR]

The concealment of assets is a critical challenge in financial fraud and asset recovery investigations, posing significant obstacles for creditors and regulatory authorities. National governments commonly possess the necessary data for detecting and combating this type of fraud, typically related to personal data and asset ownership. However, this information is often dispersed across different departments within the same government and sometimes in databases shared by other countries. This leads to difficulty semantically integrating this large amount of data in various formats and correlating entities through identifying hidden relationships, which are essential in this type of analysis. In this regard, this work proposes an ontology to support the data integration process in the domain of asset concealment and recovery and fill the gap in the existence of a public ontology for this domain. The applicability of this ontology in the context of integration between data from different departments and countries was validated. The use of the ontology in a pilot project in the context of a tool for investigating this type of fraud was conducted with a Brazilian government agency, and the users validated its applicability. Finally, a new method for constructing ontologies is proposed. The proposed process was evaluated during the asset concealment ontology building and proved to be more suitable than the similar processes analyzed concerning the partial reuse of existing ontologies and the construction of ontologies for data with a transnational scope. [ABSTRACT FROM AUTHOR]

[Purpose] This study analyzed the case of Bernard Madoff's Ponzi scheme, investigating the causes, implications and regulatory failures that allowed this fraud to be perpetuated for so long [Methodology/approach/design] We used a multidisciplinary approach that involved reviewing the literature, analyzing legal documents, regulatory reports and related academic research. We explored the structure of Madoff's Ponzi scheme, the factors that made it sustainable and the deficiencies in the regulatory system that allowed it to operate. [Findings] The study revealed that Bernard Madoff created one of the largest Ponzi schemes in history, causing massive losses for investors. Regulatory failures, including ineffective SEC oversight, lack of coordination between regulatory agencies and conflicts of interest in audits, played a crucial role in perpetuating the fraud. Madoff's 150-year sentence and asset forfeiture were part of the legal measures taken to deal with the consequences of the fraud. [Practical implications] The practical implications of this study highlight the importance of regulatory reforms to strengthen financial and securities supervision. In addition, it highlights the need to protect whistleblowers and promote a favorable environment for revealing wrongdoing in the financial sector in order to prevent future similar cases. [Originality/value] This article fills a gap in the study of financial regulation by providing a comprehensive analysis of the Madoff case and its implications. Its relevance lies in the lesson it offers about the devastating consequences of financial fraud and the measures needed to protect investors and improve regulatory oversight.

The world is turning to financial fraud as a base for daily transactions due to the rapid growth of digital technologies, which creates numerous new opportunities for criminals to misuse credit and debit cards. The card's issuer should offer a service to shield users from any risks they might encounter in order to guarantee the security of users of those cards. This study describes the usage of deep learning algorithms for prediction of financial fraud. With the aim to represent the duration sequence generated by series of identical card transactions, we propose the use of an evolving machine learning technique. However, because most datasets containing financial fraud transactions are severely skewed, financial fraud detection remains a substantial issue for statistical solutions. This is a crucial field of research since fraudulent instances are difficult to identify and get tougher as more data is collected, decreasing the number of these instances. In this paper, utilising a Flexible Simulated Instance approach, the problem of data imbalance is resolved. Also, a tuned bidirectional LSTM with attention layer (A-BiLSTM) is proposed to detect the financial fraud. This model aims to improve existing detection strategies and detection accuracy in the context of enormous amounts of data. A benchmark dataset of financial frauds is used to evaluate the proposed model, and the outcomes are compared against existing models based on various deep learning approaches. The testing findings showed that A-BiLSTM performed flawlessly, achieving 99.96% accuracy. [ABSTRACT FROM AUTHOR]

We examine the characteristics of consumers who reported scams to the U.S. Federal Trade Commission. We assess how consumers vary demographically across six scam types, and how the overall emotional sentiment of a consumer's complaint (positive, negative, neutral/mixed) relates to reporting victimization versus attempted fraud (no losses). For romance, tech support, and prize, sweepstakes, and lottery scams, more older than young and middle-aged adults reported victimization. Across all scam types, consumers classified as Black, Hispanic, and Asian/Asian Pacific Islander were more likely than non-Hispanic white consumers to report victimization than attempted fraud. Relative to complaints categorized as emotionally neutral or mixed, we find that emotionally positive complaints and emotionally negative complaints were significantly associated with victimization, but that these relationships differed by scam type. This study helps identify which consumer groups are affected by specific scams and the association between emotion and victimization. [ABSTRACT FROM AUTHOR]

The present research aims to improve the detection of financial frauds in enterprises through the utilization of Artificial Intelligence (AI) methods. The proposed framework employs machine learning algorithms and data analytics to accurately identify patterns, anomalies, and signs of fraudulent activity. Exploratory data analysis approaches were employed to identify instances of missing values and imbalanced data. The random forest was based on its ability to consistently capture intricate patterns and efficiently tackle the multicollinearity problem. The isolation forest approach yielded an accuracy of 99.7%, while the local outlier factor method achieved an accuracy of 99.8%. Similarly, the random forest algorithm demonstrated an accuracy of 99.9%. [ABSTRACT FROM AUTHOR]

The modern financial sector faces major challenges in the fight against fraud due to the growth of digital transactions, the complexity of criminal schemes and the development of cyber threats. In this regard, innovative technologies for detecting and combating financial fraud are becoming crucial for ensuring economic stability and trust in financial institutions. The purpose of the study is to analyze the patent landscape of innovative technologies aimed at identifying and combating financial fraud, money laundering, and money laundering. The proposed analysis methodology is based on the integrated application of dynamic, thematic and subject analysis approaches. The results are based on the use of the online tool GetFocus, which is built on the basis of AI and provides access to the world's largest international patent databases. The results of the dynamic analysis demonstrated a relatively small number of patents in the context of the studied topics around the world (4,553), of which almost half (49.7%) were registered in 2023-2024. This growth is explained by the intensification of cyber threats during the study period. The key thematic areas of patents were identified: machine learning, artificial intelligence, blockchain technologies, and predictive modelling. The geographical analysis showed that the leading countries in this area are the United States, China, and India, demonstrating the largest number of registered patents. The subject analysis has shown that the key patent holders are companies such as IBM Corp, Capital One Financial Corp, and Mastercard Inc., which are actively investing in innovative solutions in the field of prevention, identification, and counteraction to financial fraud. Promising areas of research include the study of patents on innovative technologies in the field of identification, prevention, and counteraction to fraudulent activities with cryptocurrency transactions; analysis of patent data by country and empirical assessment of the implementation of patented technologies in practice, and their alignment with existing regulatory requirements.

This article explores the emergence and evolution of financial fraud and money laundering schemes in Ukraine during the war. The study uses qualitative analysis methods, including a systematic review of news reports, scientific papers, and publicly available data. The findings reveal significant growth in financial fraud activities following the start of the full-scale invasion of Ukraine, driven by economic instability, emotional vulnerability, and trust in charitable initiatives. Key fraudulent schemes are categorized and analyzed, including volunteer fraud, fake investment projects, charity fraud, sales fraud, and banking and card scams. Furthermore, the study examines new fraudulent tactics, such as targeting government aid programs, impersonating social service representatives, and exploiting the psychological distress of victims. The research highlights how fraudsters quickly adapt to new circumstances during the war, creating increasingly sophisticated schemes to fraud individuals, military personnel, and organizations. Practical recommendations are provided for the prevention, detection, and mitigation of financial fraud in war contexts. These include enhancing public awareness, strengthening cybersecurity measures, improving legal frameworks, and fostering international cooperation to address the transnational nature of financial crimes. The study underscores the need for coordinated efforts between governmental structures, law enforcement agencies, and financial institutions to combat the rapidly evolving threat of financial fraud and money laundering during the war.

Digitization has led to the emergence of increasingly sophisticated forms of financial fraud, necessitating more advanced and integrated approaches for their rapid detection and prevention. This challenge prompted the authors to examine relevant literature and analyze current policies and measures for detecting financial fraud within the digital environments of organizations, with the aim of enhancing proactive prevention strategies. To this end, an online empirical survey was conducted with 118 executives and managers from Serbia during the first half of 2024, supported by the Association of Employers of Serbia and the Association of Managers. The research focused on the impact of new technologies, particularly AI, on the regulations and organizational policies related to financial fraud detection. Qualitative research, which utilized 12 predefined statements within each impact group using a five-point Likert scale, provided insights into the actual experiences and perspectives of participants concerning financial fraud as a distinct business, social, and economic issue. Multiple correlation approaches were employed to analyze the data. The outcomes suggest that all analyzed factors contribute to addressing financial fraud, with new technologies – especially those based on artificial intelligence – and corporate policies and strategies playing significant roles. Conversely, regulations have a lesser impact, attributed to their correctness, implementation, and enforcement. These findings enhance the understanding of the significance of taking a comprehensive approach to combating fraud, corruption, and financial crime, and highlight the roles of continuous technological advancements, employee digital education, and enhanced communication with the public and investors in building trust and maintaining a company’s reputation.

Drawing on prior literature on audit fees, client reputation, and corporate governance, we posit that a material adverse event at a firm, such as a financial fraud allegation, leads to an increase in the audit fees of firms connected to the former by a board interlock. We propose two possible mechanisms to explain the upward pressure on audit fees: a client-side effect, where the client demands additional audit services, and an auditor-side effect, where the auditor raises its audit fees due to a perceived increase in audit engagement risk. The results indicate an average marginal increase of 12.86% in audit fees in the year following the public revelation of financial fraud. Additional analyses suggest that an auditor-side effect is in place, while we cannot find clear evidence supporting the client-side effect. Furthermore, we document that the positive effect on audit fees persists for up to at least 2 years after public disclosure of the event when the interlocked director serves as a member of the audit committee. JEL Descriptors: G34, M40, M42 [ABSTRACT FROM AUTHOR]

Purpose - Financial fraud, considered an illegal act that aims to obtain financial benefits, has recently become a widespread menace in companies and organizations. Moreover, in recent years, financial fraud scandals of listed companies have occurred frequently, which has attracted great attention from regulatory bodies in various countries. This study aims to present an analysis of the evolution of scientific publications regarding financial fraud in recent years and how much debate and interest this topic has aroused from researchers. Methodology - This study started from a framework that combines bibliometric analysis techniques such as word frequency, co-authorship analysis, which allows the analysis of the relevance of the subject of financial fraud in the specialized literature. This method was used to discover what sub-themes were covered in the financial fraud research articles. Results - This study contributes to the specialized literature as follows: first of all, in this study it was possible to obtain information with reference to the most important journals, articles, authors, keywords that managed to capture aspects related to financial fraud. Secondly, the research results indicate a significant concern on the part of researchers regarding the determining factors of fraud, detection and prevention methods, the role of corporate governance, respectively the financial auditor in detecting and preventing fraudulent acts. Research limitation - The limits of this study can be given by the complexity of the topic, the time period and the number of articles analyzed. Originality - Although bibliometric analysis is quite well-known, there are limited publications that use this type of analysis, in order to review the specialized literature on the subject of financial fraud.

The study ascertained the perception of lecturers and students on the role of social support and psychological first aid in assisting financial fraud victims in South East Nigeria. The study adopted a descriptive survey design. The study was carried out in three Universities in the South East, Nigeria: the University of Nigeria, Nsukka; Nnamdi Azikiwe University, Awka; and Michael Okpara University of Agriculture, Umudike. The population comprises of 135 students and 52 lecturers in Home Economics programme of the three Universities, making a total of 187 respondents. The entire population is used as the sample of the study because of the manageable size. A 32-item Questionnaire was used as instrument for data collection. The instrument is validated by three experts. Data collected is analyzed using mean and standard deviation to answer research questions. Decision on research questions is taken based on real limits of numbers. For the test of significance, the probability (p) value is used in comparison with the alpha value of 0.05, and at 90 degree of freedom (df). The study found that social support and psychological first aid can be used to help financial fraud victims by reaching out to them to help them cope with stress and psychological trauma associated with loss of properties. The study recommended that government at all levels should establish social support and psychological first aid units that will help to cater for the emotional, psychological and other needs of financial fraud victims in order to enable them recover efficiently.

The primary aim of this research is to present a thorough and all-encompassing examination of artificial intelligence (AI) methodologies employed in the detection of financial fraud. The present study employs a systematic literature review (SLR) that was conducted utilizing the PRISMA approach. A comprehensive search was undertaken on reputable academic databases including ScienceDirect, Scopus, Springer, and Emerald, yielding a total of 24 papers published throughout the timeframe of 2014 to 2023. These articles will, thereafter, undergo further analysis. The findings of this study demonstrate that the implementation of artificial intelligence (AI) techniques for detecting financial fraud yields favorable outcomes. Specifically, the AI approach proves to be effective in enhancing the precision and efficiency of fraud pattern identification, thereby making a substantial contribution in this domain. In contrast, the prevailing methodology employed in the realm of financial fraud detection is frequently centered around machine learning. Furthermore, a majority of the research encompassed a diverse range of industries, with particular emphasis on the financial industry as the primary domain for the implementation of artificial intelligence (AI) in the detection of financial fraud. [ABSTRACT FROM AUTHOR]

Financial fraud presents a substantial challenge to the American economy, culminating in substantial monetary losses and breaching the integrity of financial systems. The focal objective of this research paper was to resolve the prevalent issue of financial fraud detection in the USA by performing a comparative study of multiple machine learning algorithms, particularly concentrating on their anomaly detection capabilities. Experimentation was performed using various machine learning classifiers, such as logistic regression, random forest, Multi-layer perceptron, SVM, Naive Bayes, AdaBoost, decision tree, and KNN. Data utilized for this study was retrieved from Kaggle's website (https://www.kaggle.com/mlg-ulb/creditcardfraud). The five-metrics used for the performance evaluation were accuracy, precision, recall, F1-score, and confusion matrix. Decision Tree had superior performance at classification accuracy, followed closely by AdaBoost, then KNN and Random Forest, as per the outcomes obtained in this study. Implementing the proposed models has an array of benefits to both financial organizations and the US economy in terms of real-time fraud detection, advanced accuracy of fraud detection, cost efficiency, reduction in financial losses as well as strengthening financial organizations. [ABSTRACT FROM AUTHOR]

Copyright of Revista Jurídica (0103-3506) is the property of Revista Juridica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of Internet finance, the problem of financial fraud has become increasingly prominent, which has brought severe challenges to the security and stability of the financial industry. This paper aims to use artificial intelligence (AI) technology to empower data mining models to build an efficient financial fraud detection and prevention system. First of all, through the analysis of the development trend of Internet finance and the current situation of financial fraud, the limitations of traditional prevention and control measures in the face of big data and complex fraud modes are revealed. Secondly, the paper introduces the application prospect of AI technology in the financial field, especially the advantages and potential in data mining and fraud detection. Then, the data mining techniques adopted are discussed in detail, including the application of machine learning-based model recognition and artificial intelligence algorithms, as well as data preprocessing and feature engineering in big data environment. Further, the paper describes the system architecture design and key technology implementation, including model training and optimization, real-time monitoring and early warning solutions. Finally, through the in-depth analysis and evaluation of the experimental results, the paper verifies the remarkable effect and feasibility of the system in financial fraud detection and prevention, and provides a new technical guarantee and risk management strategy for the financial industry. The experimental results show that the model has achieved good results in the actual data in the financial field, and provides effective technical guarantee and support for financial security. [ABSTRACT FROM AUTHOR]

This study aims to improve the accuracy of detecting financial fraud in listed companies by applying various deep learning algorithms. First, we comprehensively reviewed the current state of research on financial fraud theory and identified 67 recognition features to create a new recognition indicator system. Then, we collected data samples of all A-share listed companies from 2010 to 2022, preprocessed them, and generated a basic dataset. To address the unbalanced dataset, we used the Borderline-SMOTE algorithm. Empirical analysis results show that this algorithm can significantly improve the recognition performance of the model. Finally, we conducted experiments on the new dataset using three types of deep learning algorithms. The results show that the model constructed using the Long Short-Term Memory (LSTM) algorithm has the best prediction performance, with an accuracy rate higher than that of the DCRN, autoencoder, and other models. In addition, the classification effects of all deep learning algorithms are better than basic models and ensemble models. This research provides a powerful tool for the regulatory authorities of listed companies, helping them more effectively monitor and prevent financial fraud. We have three innovations in this study: (1) Development of a comprehensive recognition indicator system with 67 features; (2) Utilization of the Borderline-SMOTE algorithm to handle data imbalance; (3) Demonstration of the superior performance of the LSTM algorithm compared to other deep learning, basic, and ensemble models. [ABSTRACT FROM AUTHOR]

Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment employs FL to establish a global learning model on the cloud server, which transmits initial parameters to individual local learning models on fog nodes. With three banks (fog nodes) involved, each bank trains its learning model locally, ensuring data privacy, and subsequently sends back updated parameters to the global learning model. Through the integration of FL and blockchain, our system ensures privacy preservation and data protection. We utilize three machine learning and deep neural network learning algorithms, RF, CNN, and LSTM, alongside deep optimization techniques such as ADAM, SGD, and MSGD. The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy. [ABSTRACT FROM AUTHOR]

Fundraising fraud is one of the most serious and complicated financial crimes in China. It has an intertwined relationship with a regulatory offence, known as illegally taking in deposits from the general public (ITIDFGP). In judicial practice, ITIDFGP works as the downgraded form of fundraising fraud. This article explores why fundraising fraud is identified as financial fraud whereas ITIDFGP is a regulatory offence. The paper discerns the essence of fraud in Chinese criminal law and critically examines the concept of "intention to possess illegally." It argues that ITIDFGP should be classified as a form of fraud de jure , although, in reality, there are policy considerations behind its categorization as a regulatory offence. The paper also suggests that the role played by the socioeconomic policy in Chinese criminal-law legislation should be fully acknowledged. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze and classify the main schemes of organized and transnational cybercriminal activity, as well as to identify the methods and technologies used by cybercriminals to carry out criminal activities in the digital space. The article explores current trends in cybercrime, such as the rise in financial fraud, ransomware attacks, phishing, and DDoS attacks. The study shows that the use of the latest technologies, particularly cryptocurrencies and the Internet of Things (IoT), significantly complicates the detection and prosecution of cyber criminals, necessitating the constant improvement of legal and technical tools to combat cybercrime. Key findings indicate that the transnational nature of cybercrime often makes it difficult to prosecute criminals across jurisdictions, requiring greater international coordination. It was also found that cybercriminals are using increasingly sophisticated methods to conceal their activities, incorporating modern technological solutions into criminal schemes. This underscores the need for continuous monitoring of new threats and the updating of protection methods for both organizations and individual users. The main conclusions of the study emphasize the importance of global cooperation in the fight against cybercrime, the need to enhance technological protection, and the development of legal mechanisms to counter modern cyber threats.

This study addresses the impact of psychological contract breaches and organizational factors on fraud disclosure in the banking system. The statistical population includes all the employees and internal auditors in the banks listed on the stock exchange in 2022. The data is collected using a questionnaire. Structural equation modeling and Smart PLS v.3 Software propose and test eight hypotheses. Results showed a significant negative relationship between interpersonal effect, employee transactional relationship, financial system instability, deficient organizational changes, and unethical organizational morality with fraud disclosure. However, results showed no significant relationship between past disclosure responses and fraud disclosure tendency. The mentioned organizational factors affect fraud disclosure via the mediating role of psychological contracts. The last hypothesis showed that employees tend to disclose fraud to their supervisors rather than to the fraudster and internal auditors. This article is an innovative study because it is the first research addressing psychological contracts' effects in the accounting and auditing field. Our results and findings by investigating the negative impact of psychological contract breach on the tendency to disclose fraud contribute to audit literature, fraud diamond, and fraud disclosure. Psychological contract breach might affect the “opportunity” element in the fraud diamond by not whistleblowing fraud. Management relies on their employees as an internal control against fraud.

Corruption poses a significant challenge to economic development and governance worldwide, with its detrimental effects permeating various levels of society. In the context of Greece, where corruption has been a longstanding issue, the role of internal audit mechanisms within local government organizations (LGOs) emerges as paramount. This paper presents a comprehensive analysis of the internal control landscape within LGO revenue departments, focusing on factors influencing its effectiveness and proposing strategies for improvement. Drawing upon survey data and regression analyses, this study highlights the crucial role of robust internal control mechanisms in combating corruption and fostering economic development. The findings underscore the importance of competent personnel, legislative compliance, interdepartmental collaboration, and technology utilization in enhancing internal control practices. Despite existing legislation, gaps in internal control implementation persist, including understaffing, inadequate procedures, and limited access to information. This study emphasizes the transformative potential of effective internal audit measures in mitigating corruption at the local level, thereby contributing to broader economic growth and societal well-being. Recommendations for strengthening the internal control structures within LGOs include the formal establishment of internal audit functions, adherence to professional standards, and the promotion of information system utilization. By addressing the corruption and inefficiencies within LGOs, this research underscores the pivotal role of institutional effectiveness in promoting transparency, accountability, and sustainable economic progress. [ABSTRACT FROM AUTHOR]

This study addresses the impact of psychological contract breaches and organizational factors on fraud disclosure in the banking system. The statistical population includes all the employees and internal auditors in the banks listed on the stock exchange in 2022. The data is collected using a questionnaire. Structural equation modeling and Smart PLS v.3 Software propose and test eight hypotheses. Results showed a significant negative relationship between interpersonal effect, employee transactional relationship, financial system instability, deficient organizational changes, and unethical organizational morality with fraud disclosure. However, results showed no significant relationship between past disclosure responses and fraud disclosure tendency. The mentioned organizational factors affect fraud disclosure via the mediating role of psychological contracts. The last hypothesis showed that employees tend to disclose fraud to their supervisors rather than to the fraudster and internal auditors. This article is an innovative study because it is the first research addressing psychological contracts' effects in the accounting and auditing field. Our results and findings by investigating the negative impact of psychological contract breach on the tendency to disclose fraud contribute to audit literature, fraud diamond, and fraud disclosure. Psychological contract breach might affect the "opportunity" element in the fraud diamond by not whistleblowing fraud. Management relies on their employees as an internal control against fraud. [ABSTRACT FROM AUTHOR]

In a retail financial market, firms strategically choose whether to commit financial fraud to exploit naive investors who are unaware of such practices. In a leader-follower setting, we identify a segmented equilibrium in which an honest firm offers a normal product to sophisticated investors, while a dishonest firm offers a fraudulent product that targets naive investors. Competition may not benefit investors because the presence of a rival firm reduces the profitability of offering a normal product and thus discourages honest behavior. If there is a positive cost of entry, the incumbent firm may strategically switch to offering a fraudulent product to deter entry. As a result, policies that alter the entry barriers to the market may have unintended negative effects on welfare. In the segmented equilibrium with free entry, the honest firm has an incentive to disclose information about financial fraud and steal market share from the dishonest firm. However, this incentive is limited because such disclosure may prompt the dishonest firm to deviate and compete for the normal product market. [ABSTRACT FROM AUTHOR]

This paper explores the positive governance effects of the Procuratorate’s Public Interest Litigation System in China, which combines the powers of litigation and administrative supervision, on the quality of information disclosure by listed state-owned enterprises. We report several findings. (1) The likelihood that listed state-owned enterprises would issue financial restatements and participate in financial fraud decreased significantly in areas selected for pilot implementation. (2) The governance effect is stronger in regulated industries than in unregulated industries. After the pilot implementation, the agency costs decreased, and the increase in legal litigation risks related to false statements faced by enterprises played a deterrent effect. (3) The significance of the above results is stronger when a company’s external and internal governance are weaker. This study provides both new evidence of the effectiveness of the integrated governance mechanism and inspiration for future efforts to widely implement this mechanism in the capital market.

This paper presents a meticulous bibliometric analysis of forensic accounting research conducted between 1991 and 2021, employing Scopus and VOSviewer. With a dataset comprising 232 documents, the study aims to scrutinize existing literature, identify influential authors, and discern the prominent countries in the field. Findings reveal a notable dearth in the documentation of forensic accounting within academic journals, suggesting a subdued demand for the profession, especially in certain nations. The analysis contributes to an enriched understanding of forensic accounting by offering a comprehensive taxonomy, pinpointing research gaps, identifying prospective research directions, and benefiting academia, practitioners, educators, policymakers, and professionals. The study's originality lies in its integration of bibliometric analysis, data visualization, and systematic examination, providing a distinctive contribution to the field. The comprehensive approach enhances our understanding of forensic accounting, offering invaluable insights for future research and fostering collaboration within the realm of financial fraud detection and prevention.

Supply chain financial fraud, characterized by extensive false fund circulation and fictitious business events, causes substantial financial losses and undermines the efficiency of supply chain operations. To address this challenge, we introduce an innovative research framework that utilizes knowledge graphs and spatial-temporal neural networks for effective fraud detection. Our approach involves constructing a supplier-customer knowledge graph from data of Chinese listed companies, capturing the complex supply-demand relationships within the supply chain. We designed a spatial-temporal Graph Neural Network (GNN) that models both node attributes and the time-evolving graph topology. By incorporating temporal and spatial dual attention mechanisms, our model adeptly identifies local topology and temporal changes in the knowledge graph. Empirical evaluations demonstrate that our Dual Attention Spatial-Temporal Graph Neural Network (DAST-GNN) outperforms existing methods, achieving an AUC of 93.64%, which is 10.41% higher than the leading machine learning methods. Furthermore, analyzing supplier-customer relationships across different historical periods enhances fraud detection, highlighting the robustness of our approach. This research offers a potent tool for regulators, investors, and researchers, advancing the security and efficiency of supply chain operations.

The concealment of assets is a critical challenge in financial fraud and asset recovery investigations, posing significant obstacles for creditors and regulatory authorities. National governments commonly possess the necessary data for detecting and combating this type of fraud, typically related to personal data and asset ownership. However, this information is often dispersed across different departments within the same government and sometimes in databases shared by other countries. This leads to difficulty semantically integrating this large amount of data in various formats and correlating entities through identifying hidden relationships, which are essential in this type of analysis. In this regard, this work proposes an ontology to support the data integration process in the domain of asset concealment and recovery and fill the gap in the existence of a public ontology for this domain. The applicability of this ontology in the context of integration between data from different departments and countries was validated. The use of the ontology in a pilot project in the context of a tool for investigating this type of fraud was conducted with a Brazilian government agency, and the users validated its applicability. Finally, a new method for constructing ontologies is proposed. The proposed process was evaluated during the asset concealment ontology building and proved to be more suitable than the similar processes analyzed concerning the partial reuse of existing ontologies and the construction of ontologies for data with a transnational scope.

Key points: This article highlights two types of publishing fraud: fake acceptance letter and financial fraud.Prepared by a third party, fake acceptance letters affirm that a paper, which we had never received before, has been accepted by IPSR.In the financial fraud case, a third party pretends to be an editor of IPSR, sends out authentic looking fake acceptance letters and then solicits authors to pay an article processing fee (APC). [ABSTRACT FROM AUTHOR]