Types Of Fraud Research Articles

CIBERCRIMES: ESTELIONATO VIRTUAL NO DIREITO BRASILEIRO

The research addresses the topic of cybercrimes, focusing on virtual fraud in the context of Brazilian law. The main objective is to analyze the elements that make up the practice of virtual fraud, investigating its insertion in the Brazilian legal system and the evolution of the rules that govern this crime. In addition, the research discusses the emergence of virtual fraud, its distinctive characteristics and the punishments provided for in current legislation. The study also seeks to understand virtual crimes in Brazil and evaluate preventive measures aimed at victims, aiming to reduce and inhibit the practice of this crime, which has grown with the digitalization of everyday transactions. Strengthening digital education policies and creating protection mechanisms for users, especially the most vulnerable, are strategies that should be adopted in conjunction with the application of criminal laws. The conclusion highlights the need for more effective educational and legal policies to mitigate the damage caused by this type of fraud in the cyber environment.

Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud Detection

Credit card fraud detection is a critical challenge in the financial sector due to the rapidly evolving tactics of fraudsters and the significant class imbalance betweenegitimate and fraudulent transactions. Traditional models, while effective to some extent, often suffer from high false positive rates and fail to generalize well to emerging fraud patterns. In this paper, we propose a novel approach that integrates a Mixture of Experts (MoE) model with a Deep Neural Network-based Synthetic Minority Over-sampling Technique (DNN-SMOTE) to enhance fraud detection performance. The MoE modeleverages multiple specialized expert networks, each trained to detect specific types of fraud, while the DNN-SMOTE generates high-quality synthetic samples to address the class imbalance. Our experimental results on a publicly available dataset demonstrate that the proposed method achieves a classification accuracy of 99.93%, a true positive rate of 84.69%, and a true negative rate of 99.95%. The Matthews Correlation Coefficient (MCC) of 0.7883 further highlights the model’s balanced performance in detecting fraudulent transactions. These results underscore the effectiveness of combining MoE with DNN-SMOTE, offering a robust solution for real-world credit card fraud detection scenarios.

Ontology Development for Asset Concealment Investigation: A Methodological Approach and Case Study in Asset Recovery

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.

SUBSCRIPTION FRAUD DETECTION USING AUTOENCODER IN CASE OF ETHIOTELECOM

Nowadays, telecom services are becoming an essential communication and business facilitators. However, the development of telecom services motivates fraudsters for illegal use. Hence, telecom fraud becomes a serious challenge in telecommunication sector by losing telecom revenue. It also results in poor quality of services for their customers. Telecom data, SMS and voice call are not free from security issues. Despite SMS, and USSD services becomes good options over installable mobile based or web based applications due to lesser cost and real time support, the methods provide fixed amount requirements which is not dynamic way to address the need of these services subscribers. The other challenge of these services is that; the services are not safe from frauds as such, especially in GSM switches. Subscription fraud is one type of fraud in today’s telecom business which is a common telecom fraud. The need of fraudsters is to make money illegally or getting telecom services with the intention of not to pay for the service they used. The prime objective of this study is to build a predictive model using deep auto-encoder to detect subscription fraud in case of Ethiotelecom. The performance of fraud detection model is 98.95% validation accuracy on ethiotelecom call detail record dataset using deep neural network autoencoder and CNN-LSTM autoencder algorithms at threshold of 0.0103 and 0173 respectively.

온라인 무역사기 사례에 관한 연구: 이메일 해킹 무역사기 사례를 중심으로

Purpose: This study focuses on the fact that fraud using email, which is a major means of communication in trade transactions, continues to occur. To raise awareness of the email hacking risks, this study suggests practical countermeasures through analysis of fraud that has caused damage and cases of attempted fraud. Research design, data, and methodology: Along with a literature review, this study focuses on successful and attempted email hacking trade fraud based on recent data from KOTRA. Results: According to the analysis, representative types of email-related trade fraud are mainly “new forms of email account hacking” and “existing forms of similar email alteration fraud cases”. Recently, in response to changes in companies' awareness of email fraud, the number of cases has decreased, but caution is required because fraudsters are becoming more detailed and sophisticated in their approach. Conclusions: This paper encourages step-by-step practical and policy alternatives in order to be suspicious of (and to confirm) account change requests based on the changing perception of fraud by email, which is the main means of communication for traders.

Lessons learned from Dread darknet communities: How and why are fraudsters targeting the elderly to be victims or accomplices?

AbstractResearch SummaryWe examined darknet user discussions on the Dread forum to identify key themes and emerging topics in fraud planning, focusing additionally on elderly victimization. Using the conceptual framework of criminogenic learning to study the process of fraud planning in darknet communities of practice, we analyzed the content of original user posts (n = 818) and comments (n = 1365) collected from the Dread forum. We identified nine unique fraud categories, of which payments‐related fraud was the most‐discussed fraud category and accounted for 25% of original user posts. We further found our sampled forum content to be dominated by the theme of learning or knowledge sharing in eight of the nine fraud categories, which appeared in more than 44% of original user posts. Our content analysis revealed specific insights into why and how darknet forum users may target people, including the elderly subpopulation, for different types of fraud. Overall, our research demonstrates the diversity of opinion and knowledge sharing among darknet forum users in relation to planning and committing fraud against the elderly, views on who is a suitable target and why, and what veteran and aspiring fraudsters consider to be important information for success in fraud.Policy ImplicationsUtilized independently or as part of a multistage strategy of darknet data analysis, our research method can be used to monitor criminally active darknet forums for current and emerging themes in fraud against general populations and vulnerable subpopulations, such as the elderly, and to develop strategies to identify, disrupt, or destroy hubs of criminal planning and knowledge sharing. Our study additionally informs policy makers of certain elder‐specific vulnerabilities that might be addressed by more up‐to‐date elder cybercrime awareness campaigns and initiatives. Our findings also highlight the presence of insider threats that may inform discussions on how health and financial institutions can better regulate these cybersecurity risks.

The Rise of Bot-Driven Fraud: Understanding Threats and Implementing Advanced Prevention Strategies

In today’s digital landscape, the proliferation of bots has fundamentally changed the cybersecurity ecosystem. While bots were initially designed to automate simple, repetitive tasks, they have since evolved into sophisticated tools used by cybercriminals to carry out large-scale attacks. From brute force attacks and credential stuffing to account takeovers and denial of service (DoS) attacks, bots pose a growing threat to businesses across all sectors. The damage caused by bot-driven fraud extends far beyond financial losses—it affects operational efficiency, customer trust, and brand reputation. This paper explores the various types of bot-driven fraud, the consequences businesses face as a result of these activities, and the advanced prevention strategies that are being developed to counter these threats. By understanding how bots are used in cybercrime and implementing proactive defense mechanisms, businesses can reduce their vulnerability to fraud and better protect their customers and assets.

Consumer perspectives on interventions to combat fraudulent product returns in omnichannel fashion retail

PurposeThe expansion of online shopping aligned with challenging economic conditions has contributed to increasing fraudulent retail product returns. Retailers employ numerous interventions typically determined by embedded perspectives within the company (supply side) rather than consumer-based assessments of their effectiveness (demand side). This study aims to understand how customers evaluate counter-fraud measures on opportunistic returns fraud in the UK. Based on the fraud triangle and the theory of planned behaviour, we develop an empirically informed framework to assist retail practice.Design/methodology/approachWe collected 485 valid survey responses about consumer attitudes regarding which interventions are effective against different types of returns fraud. First, a principal component section evaluates the policies' effectiveness to identify any policy grouping that could help prioritise specific sets of policies. Second, cluster analysis follows a two-stage approach, where cluster size is determined, and then survey respondents are partitioned into subgroups based on how similar their beliefs are regarding the effectiveness of anti-fraud policies.FindingsWe identify policies relating to perceived effectiveness of interventions and create customer profiles to assist retailers in conceptualising potential opportunistic fraudsters. Our product returns fraud framework adopts a consumer perspective to capture the perceived behavioural control of potential fraudsters. Results suggest effectiveness of different types of interventions vary between different types of consumers, which leads to the development of propositions to combat the fraud.Originality/valueThis study is unique in assessing the perceived effectiveness of a range of interventions based on data collection and advanced analytics to combat fraudulent product returns in omnichannel retail.

An assessment of convicted cryptocurrency fraudsters

ABSTRACT We examine cryptocurrency fraud cases prosecuted by Nigeria's Economic and Financial Crimes Commission (EFCC). We considered the lens of the Space Transition Theory (STT) in exploring the dynamics of these digital crimes. Our data analysis reveals common types of fraud, including cryptocurrency investment schemes. The results show an exclusive male demographic (100%), with the majority under 30 years old and only a quarter possessing a degree, providing insights into the socio-demographic characteristics of cryptocurrency fraudsters. Additionally, while most fraudsters (55%) targeted victims in the United States, Bitcoin, leveraging blockchain technology, was the most commonly used method (46%) for cryptocurrency fraud. Our examination of the methods and mediums used for cryptocurrency fraud supports some aspects of STT, while others do not. We advocate for a multifaceted strategy that prioritises stringent regulation, implementation, and heightened scrutiny of digital currency ecosystems in Nigeria and beyond. This study contributes to the broader discourse on cybercrime prevention and enforcement by emphasising the novel methodological approach utilised.

Assessing Mobile Network Fraud Threats and Prevention Strategies in Kenya

Background: Mobile network technology has exponentially advanced in the last decade and with this development, fraud activities have risen in equal measure resulting in companies and customers losing huge amounts of money as a result of, especially in developing economies that lag in the regulatory frameworks when it comes to Mobile network fraud. The purpose of the study was to explore Mobile network fraud in Kenya identifying the most common types of fraud, ways which service providers and regulators are employing to prevent or reduce fraud, methods currently used to detect fraud, and gaps thereof. Finally, the effect of concept drift in the automated fraud detection process. Method: A qualitative research method was adopted for the study and using a semi-structured question guide, four focus group discussions composed of 23 participants were conducted. The criteria used for selecting and placing participants into focus groups considered the following: The expert area of the participant, years of experience in the fraud ecosystem of the participant, and the organization to which the participant is attached. The availability and willingness of the participants were also considered in the selection process. The focus group approach was selected as it facilitated balanced discussion amongst all the players in the Kenyan fraud ecosystem, harnessing the power of group dynamics as it involved the regulators and the service providers who were drawn from different organizations. Results: The mobile network fraud ecosystem was stratified into three dimensions namely Fraud prevention which looked at the policies and methods used by both regulators and service providers to reduce fraud, Fraud categorization which aimed at categorizing different types of mobile frauds, and finally the Fraud detection which looked at the current tools being used to detect fraud. From the study, it emerged that although the regulators have provided strict guidelines on the customer onboarding process, not all service providers are currently using biometric approaches while onboarding new customers as this was highlighted as the entry point of most fraud cases. The study established five major types of Mobile network fraud in Kenya: SIM swap, SIM boxing, Wangiri, Commission arbitrage, and Hoax SMS and scams. Most of this fraud is committed using either SMS or voice channels; in some cases, both channels are used. Different matrixes derived from multiple factors are used by service providers while evaluating the criticality of fraud cases though not enforced by the regulators. The study also revealed that most of the fraud detection processes amongst the service providers still use manual tools that constantly require human input. While some of the detection processes are automated, concept drift is a major challenge for automated classification models due to the constant evolution of fraud patterns. Conclusion: The study revealed gaps in Mobile Network fraud prevention processes in Kenya as service providers still use non-biometric customer validation processes that are open to forgery and exploitation. A strict customer onboarding process that is fully automated and integrated should be used to address this gap. In the fraud categorization, there is no clear universal categorization matrix to guide the service providers while assessing the criticality of fraud and in this regard, a qualitative scientific model should be developed and used by all the stakeholders as a reference point. When it comes to fraud detection, concept drift is a major challenge, and service providers in Kenya still rely on manual processes due to the dynamic nature of mobile fraud. This exposes a huge gap in the detection process and there is a need to address this by developing systems and processes that will automatically detect and react to concept drift while automating the detection processes.

Data integrity in an online world: Demonstration of multimodal bot screening tools and considerations for preserving data integrity in two online social and behavioral research studies with marginalized populations.

Internet-based studies are widely used in social and behavioral health research, yet bots and fraud from "survey farming" bring significant threats to data integrity. For research centering marginalized communities, data integrity is an ethical imperative, as fraudulent data at a minimum poses a threat to scientific integrity, and worse could even promulgate false, negative stereotypes about the population of interest. Using data from two online surveys of sexual and gender minority populations (young men who have sex with men and transgender women of color), we (a) demonstrate the use of online survey techniques to identify and mitigate internet-based fraud, (b) differentiate techniques for and identify two different types of "survey farming" (i.e., bots and false responders), and (c) demonstrate the consequences of those distinct types of fraud on sample characteristics and statistical inferences, if fraud goes unaddressed. We provide practical recommendations for internet-based studies in psychological, social, and behavioral health research to ensure data integrity and discuss implications for future research testing data integrity techniques. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The subjective side of real estate fraud

The article deals with the subjective side of the criminal offense under Article 190 of the Criminal Code of Ukraine, in particular, real estate fraud. The author establishes that the subjective side of a criminal offense is of great importance for characterizing fraud. As is known, the subjective side of a criminal offense is its internal side, namely, the mental activity of a person which reflects his/her attitude to the socially dangerous act which he/she commits and its consequences. It is noted that the content of the subjective side of the criminal offense of real estate fraud includes such features as guilt, motive and mercenary purpose. The author concludes that when committing real estate fraud, a person realizes the socially dangerous nature of the offense which he/she commits. Guilt in committing real estate fraud is characterized by direct intent, the content of which is formed by two aspects – intellectual and volitional. The mercenary purpose in committing real estate fraud is expressed in obtaining personal property benefits and is a necessary subjective feature of this type of fraud; it arises in the mind of the guilty person in the form of the final result of the criminal offense – the achievement of material benefit. The motives for committing real estate fraud are of a mercenary nature. They are manifested as internal motivations that guide the perpetrator in committing real estate fraud and form the structure of criminal mercenary motivation. Real estate fraud is committed by persons who are psychologically prepared to commit a criminal offense, clearly realize the illegal actions aimed at seizing ownership of residential property, clearly realize the illegal actions aimed at seizing ownership of someone else’s real estate (to obtain money in a particularly large amount), and the consequences in the form of harm to the victim. Persons who develop such fraudulent schemes mislead victims about their true criminal intentions or abuse the victim’s trust for material gain. The criminals are certainly guided by a selfish purpose and mercenary motives that are fundamental to their behavior.

Analysis and Classification of Corporate Fraud Based on the Literature and Investigated Cases in Romania

Abstract Corporate fraud has become increasingly common and sophisticated in today’s complex economic world, and a variety of strategies for perpetrating fraud have arisen. Financial fraud has various negative implications in the corporate sector since it reduces efficiency and undermines confidence and loyalty among all stakeholders. In this context, the aim of this article is to identify the various types of corporate fraud by describing and categorizing them based on the motivation and purpose for which they are committed; additionally, different types of corporate financial crimes were examined in a case study for Romania. Data for this study were gathered from past research on the subject as well as other national databases on financial crime. Tax evasion, financial statement manipulation, and bankruptcy fraud to deceive financial data users are among the various types of fraud examined. Financial fraud in organizations is a continually changing topic. The findings suggest that corporate fraud must be prevented at multiple levels, including corporate governance, internal control and external regulation. This study contributes to the existing body of knowledge on corporate fraud and can be utilized as a resource by managers and regulators looking to better understand fraud and strengthen governance and internal control systems.

Exploring spatiotemporal Dynamics and influencing factors of cyber-fraud: A quantile regression approach in Xiaoshan District, China

This study utilizes data on cyber-fraud crimes from the Public Security Bureau in Xiaoshan District for the year 2021 as its case study. It examines the spatiotemporal distribution of various types of cyber-fraud and investigates the influencing factors from social and built environments. Additionally, quantile regression models are employed to analyze the variation in the number of cases across different quantiles of the influencing factors. The study reveals significant differences in individual characteristics among victims of different types of fraud. Cyber-fraud occurrences exhibit distinct temporal patterns across various temporal scales, with significant differences in the duration of crimes among different fraud types. Cyber-fraud demonstrates significant spatial clustering, mainly concentrated in residential areas. The results of quantile regression indicate that cyber fraud is influenced by both the built environment and social environment, with noticeable variations in the coefficient of influence across different quantiles of the independent variables.

Bank Corporate Governance: Shield Against Fraud

An efficient corporate governance system in a bank is crucial for maintaining financial stability, boosting stakeholder confidence, promoting corporate accountability, and mitigating fraud risks. This paper investigates the role of bank corporate governance in safeguarding against fraud, reviwing 20 artciles published from 2017-2024. The study belives that fraud in banking institutions is a significant threat to the financial system, causing financial hardship and disrupting the banking system, indicating that opportunity such poor governance, weak control systems, poor organizational culture is the most contributing factor of these illegal and unethical behavior. The paper found that strong corporate governance significantly reduces the risk of fraud, with the board and audit committee playing a crucial role in ensuring accountability and boosting governance. To achieve bank corporate governance goals, it is necessary to utilize all available resources, including human and technological resources. The paper also found that corporate governance brings benefits such as increased transparency, accountability, reduced risk, enhanced decision-making, compliance, ethical conduct, and improved brand value, underlying that fraud risk incurs financial costs, reputational and regulatory risks, and decreased employee efficiency. The review concludes that bank corporate governance can act as shield against fraud, adding that ethical leadership, focusing on transparency and accountability, is essential for effective governance and deterring bank fraud. The study suggests investigating various types of fraud in the banking sector and their correlation with corporate governance, as well as conducting longitudinal studies to assess the long-term effects of robust corporate governance. Originality/contribution: The study asserts that effective governance is crucial for banks to become secure, stable, and robust. It emphasizes the importance of the board, audit committee, and moral leadership as crucial elements, and offers a how bank fraud is mitigated through good bank governance.

Exploring the Effectiveness of Data Mining Classification Algorithms in Credit Card Fraud Detection

Credit card fraud is a widespread problem that impacts both individuals and companies. Data mining provides a powerful solution to not only detect but also prevent this type of fraud. This research explores this approach by utilizing data mining classification techniques to determine the potential for fraudulent credit card transactions. Data from various sources is collected and processed to extract relevant features. This research will compare 8 classification algorithms, namely Naive Bayes, Decision Trees, Artificial Neural Network, SVM, Linear Regression, Logistic Regression, LDA and Random Forest, in classifying transactions as legitimate or fake. These findings suggest that the combined use of these data mining classification methods offers a powerful tool in combating credit card fraud. To combat the problem of credit card fraud and maintain financial security for both individuals and institutions, this researcher explores the power of data mining. By using potential classification techniques, this research aims to predict fraudulent transactions on credit cards.

‘Authorized Push Payment’ Bank Fraud: What Does an Effective Regulatory Response Look Like?

ABSTRACT Authorized Push Payment (APP) fraud occurs where bank customers are tricked into transferring money from their account. As this article shows, this type of fraud is a growing threat, catalysed by the rise of remote banking. However, long-standing legal and regulatory rules leave most victims without a route to redress, as recently confirmed by the UK Supreme Court’s 2023 decision in Philipp v Barclays. Through this lens, the article examines a new and ‘world first’ UK regulatory response, which includes a mandatory reimbursement scheme for APP fraud victims in certain circumstances. The article finds that the UK’s new loss allocation scheme is valuable, but also that its specific coverage is problematic given the broad nature of this threat. Overall, the article argues that the priority for UK regulators should be to develop a more ‘joined-up’ response to APP fraud, and it offers generally applicable insights into effective regulatory responses to this evolving threat.

АНАЛИЗ ФАКТОРОВ, СПОСОБСТВУЮЩИХ РОСТУ МОБИЛЬНОГО МОШЕННИЧЕСТВА В РОССИИ

The article examines the problems of combating mobile fraud in Russia. The statistical data characterizing the development of mobile fraud in our country are analyzed. The features of mobile fraud and its strengths, including such a technique as social engineering, are being studied. A type of mobile fraud is considered – "prison" mobile fraud.

Overconfidence, short selling, and corporate fraud: Evidence from China

Using data on Chinese A-share listed firms from 2010 to 2020, this study employs a partial observable bivariate probit model and introduces fraud triangle theory to explain the mechanisms of overconfidence, short selling, and corporate fraud. Our findings show that overconfidence offers rationalization to investors and corporations, reduces fraud detection, and increases corporate incentives to commit fraud. Short selling promotes information transparency, increases fraud detection, and reduces the opportunities to commit fraud. Moreover, it moderates the relationship between overconfidence and corporate fraud. In addition, overconfidence and short selling affect different types of fraud (operational, executive, and information disclosure fraud). Furthermore, our results show heterogeneity among the ownership types. This study provides a theoretical basis for corporate fraud governance in China’s stock market.

The super‐recogniser advantage extends to the detection of hyper‐realistic face masks

AbstractHyper‐realistic silicone masks provide a viable route to identity fraud. Over the last decade, more than 40 known criminal acts have been committed by perpetrators using this type of disguise. With the increasing availability and bespoke sophistication of these masks, research must now focus on ways to enhance their detection. In this study, we investigate whether super‐recognisers (SRs), people who excel at identity recognition, are more likely to detect this type of fraud, in comparison to typical‐recogniser controls. Across three tasks, we examined mask detection rates in the absence of a pre‐task prompt (covert task), and again after making participants aware of their use in criminal settings (explicit task). Finally, participants were asked to indicate which aspects of the masks could support their detection (regions of interest task). The findings show an SR advantage for the detection of hyper‐realistic masks across the covert and explicit mask detection tasks. In addition, the eye, mouth, and nose regions appear to be particularly indicative of the presence of a mask. The lack of natural skin texture, proportional features, expressiveness, and asymmetry are also salient cues. The theoretical and applied implications of these findings are discussed.