Online Fraud Research Articles

Unveiling deceptive tactics: exploring fraudulent online ratings in the restaurant industry and their impact on consumer choice

Unveiling deceptive tactics: exploring fraudulent online ratings in the restaurant industry and their impact on consumer choice

An ESG-Compliant Framework for Fraud Detection in Online Payments Using Data Privacy and Machine Learning

Purpose: The purpose of this paper is to analyze the role of machine learning (ML) in fraudulent online payment transaction detection across industries, with a specific focus on integrating Environmental, Social, and Governance (ESG) principles to bring sustainability and ethics in fraud detection. Methodology: This study reviews the effectiveness of machine learning (ML) techniques in real-time fraud detection, presenting the efficiency of different ML techniques across industries and how those techniques can be modified to apply the principles of ESG. It checks on ethical practices concerning data, compliance with data privacy, and sustainable management, further investigating how effectively ML-driven frameworks can support fraud detection without compromising the standards of ESG. Findings: The study finds that integrating ESG principles within machine learning frameworks for fraud detection enhances both the effectiveness and ethical alignment of these systems. ML models not only support real-time fraud detection but also reinforce sustainable data management and governance practices, providing businesses with an advanced approach to mitigating cyber risks while upholding ESG commitments. Unique Contribution to Theory, Practice, and Policy: This paper contributes by advancing ESG-integrated frameworks for fraud detection, offering a sustainable and ethical model for using ML in financial systems. In practice, it provides actionable insights for industries seeking to align fraud prevention with ESG goals. The paper also suggests policy considerations, advocating for stronger ethical standards and data governance in ML applications for fraud detection.

Fraudulent Participation in Online Qualitative Studies: Practical Recommendations on an Emerging Phenomenon.

Fraudulent participation is defined in the following as participation in research by individuals who, for one reason or another, intentionally provide false responses. Qualitative studies are at an increased risk of fraudulent participation when online recruitment and participation are used, and monetary incentives offered. Fraudulent participation threatens data quality and subsequent evidence-based practice, yet validated guidance on how to tackle it is lacking. This paper offers a critical reflection thereon by three separate qualitative research groups that experienced fraudulent participation in collaboration with a patient representative, a bioethicist, a legal expert, a journal deputy editor, and a chief executive of a national charity. The Prevent FRaudulent Online STudy participation (P-FROST) recommendations provide advice on (1) Study set-up (including team members and study design), (2) Monetary incentives and recruitment, (3) Data collection (screening and interview considerations), and (4) Analysis, reporting, and support. The reflection which balances the diverse perspectives of patients, researchers, funders, ethics boards, and legal teams puts forward the P-FROST recommendations to identify and prevent fraudulent participation throughout the design, ethical approval, and implementation of online qualitative research.

Adaptation of Islamic Criminal Law in Facing Digital Economic Crime in the Era of Technological Disruption

The rapid development of digital technology has significantly impacted various aspects of life, including the economy, but it has also given rise to complex digital economic crimes. This research examines the adaptation of Islamic criminal law in facing the challenges of digital economic crimes, such as online fraud, money laundering, and skimming. The method used is qualitative with a descriptive-analytical approach, relying on literature and official documents. The research findings indicate that Islamic criminal law needs to update its definitions and classifications of crimes to include actions in the digital space and apply principles of justice, prevention, rehabilitation, legal certainty, and limitations on punishment. In addition, using technology in law enforcement and international cooperation is crucial in addressing transnational crime. With a comprehensive approach, Islamic criminal law can serve as an effective tool in creating a safer and more just society in the era of technological disruption.

Worry about online fraud and older adults: Avoidant and protective responses

Given that cyber-enabled fraud makes up the vast majority of digital crime suffered by older adults, the impact of victimisation on this demographic group is of academic and practical relevance. This article examines the relationship between cyber fraud victimisation, worry about cyber fraud, and online avoidant behaviours and protective responses in older adults. The data come from the 2019 Special Eurobarometer 499, and the sample is made up of 6,257 Internet users aged 60 years and above. In addition to suffering cyber-enabled fraud, we also analyse various factors, such as the respondent's sex and knowledge of risks, which have been found to be associated with fear or worry about crime in other contexts. Our findings show that the respondent's sex and having experienced prior victimisation of oneself or family and friends is related to greater worry about online fraud. Moreover, we find that fraud victimisation of friends and family, knowledge of risks, and perceived self-efficacy are associated with dysfunctional avoidant behaviours and the adoption of functional security measures. We discuss the results in relation to gendered fear of crime socialisation and in terms of interventions with older adults that aim to respond to victimisation and to combat the digital divide.

Indonesia's Digital Literacy as a Challenge for Democracy in the Digital Age

In the era of digitalization, digital literacy plays a vital role in shaping active participation and the quality of democracy. There are still challenges in the level of digital literacy in Indonesia, which impacts blind trust in emerging issues. As a result, people are vulnerable to the spread of fake news, online fraud, harmful content, and privacy violations. This research uses a desk research method with a qualitative approach, collecting secondary data from journal articles, books, and related reports. Analyses were conducted using content analysis techniques to identify patterns and relationships between digital literacy and democracy. The results show that although Indonesia's digital literacy index has improved, digital crime and hoaxes are rising, indicating a gap between technological skills and digital ethics. The findings emphasize the need for a more comprehensive approach to digital literacy programs, including integrating digital literacy education into the formal curriculum and improved technological infrastructure, particularly in underdeveloped areas. The implications of this study suggest that improving digital literacy is essential for safe access to information and an important step toward strengthening more critical and responsible democratic participation in Indonesia.

The Patterns of Digital Deception

Current consumer-protection debates focus on the powerful new data-analysis techniques that have disrupted the balance of power between companies and their customers. Online tracking enables sellers to amass troves of historical data, apply machine-learning tools to construct detailed customer profiles, and target those customers with tailored offers that best suit their interests. It is often a win-win. Sellers avoid pumping dud products and consumers see ads for things they actually want to buy. But the same tools are also used for ill—to target vulnerable members of the population with scams specially tailored to prey on their weaknesses. The result has been a dramatic rise in online fraud that disproportionately impacts those least able to bear the loss. The law’s response has been technology centric. Lawmakers race to identify those technologies that drive consumer deception and target them for regulatory restrictions. But that approach comes at a major cost. General-purpose data analysis and communications tools have both desirable and undesirable uses, and uniform restrictions on their use impede the good along with the bad. A superior approach would focus not on the technological tools of deception but on what this Article identifies as the legal patterns of digital deception: those aspects of digital technology that have outflanked the law’s existing mechanisms for redressing consumer harm. This Article reorients the discussion from the power of new technologies to the shortcomings in existing regulatory structures that have allowed for their abuse. Focus on these patterns of deception will allow regulators to reallocate resources to offset those shortcomings and thereby enhance efforts to combat online fraud without impeding technological innovation.

Factors Influencing Customers’ Online Shopping Behavior in Malaysia During the COVID-19 Pandemic

The article presents an overview of the arguments and rebuttals made in the scientific discourse regarding Malaysia's expanding online shopping trend, particularly in light of the COVID-19 epidemic. This scientific decision is significant since the widespread usage of COVID-19 has altered consumer purchasing behavior and how firms operate globally by reducing potential risks such as making the wrong purchase or falling prey to online fraud. This study aims to determine the impact of perceived risk, trust, and product diversity on consumers' decisions to purchase things online in Malaysia's Klang Valley. The study utilized a quantitative survey methodology, selecting 327 sample sizes from a targeted demographic of online shoppers using Krejcie and Morgan tables. SPSS was used for descriptive and inferential analyses. The study found that product variety, trust, and perceived risk significantly predict consumers' online buying behavior in Malaysia during the COVID-19 pandemic. Trust and product variety moderately influence online purchasing, while perceived risk has a negative impact. In contrast to perceived risk (standardized coefficient = -.252), which has a negative and moderate impact, the results indicate that product variety (standardized coefficient =.571) and trust (standardized coefficient =.461) have an average influence on customers' online purchase behaviors. The study supports all hypotheses (H1, H2, and H3), indicating that trust, perceived risk, and product variety significantly influence consumers' online purchasing behaviors in Malaysia during the COVID-19 pandemic. The study emphasizes the need for a comprehensive understanding of online consumer factors and their implications for e-commerce vendors, suggesting the inclusion of marketing professionals in marketing strategy formulation.

Role of Policy in the Management of Smartphones among Undergraduates in Nigeria Higher Institutions of Learning

Smartphones have become a necessity in today’s world. Majority of our undergraduate students make use of their smartphones to take notes in their classes, do assignments and other class related works. Studies and observations have shown that students use their smartphones for other activities like online frauds, visit to sites that can corrupt their minds and even waste their precious time on video watching, streaming and downloading of inappropriate materials and films that are injurious to them which eventually have a negative impact on their studies. In view of the aforementioned, the formulation of policies to guide the management of smartphones by undergraduate students in our higher institution of learning is therefore very germane at this time. It has been postulated that Policy makers should ban the use of smartphones among the undergraduate students in our higher institutions of learning but some other concerns are of the opinion that it should not be banned but regulated. Customized tabs with WIFI capabilities should be provided to enable students to go online for their research activities and classes thereby restricting them to their academics and prohibiting them from accessing forbidden sites. Institutional customized tabs will block some harmful materials and prevent them from unnecessary sites. Institutions should also allocate fixed megabytes to students for their educational programs. This will inculcate discipline into the minds of the students and make them to use their online allocations for necessary things.

Factors Affecting Intention to Make Purchasing Through E-Commerce in Indonesia

Electronic commerce, or "e-commerce," is the term used to describe all sales and purchases made using electronic media, mostly websites and applications. With the growth of digital interactions, it is more important than ever to understand the subtleties of e-commerce purchasings. With an emphasis on Indonesia, where online purchase is becoming more and more popular, this study attempts to pinpoint the critical factors that affect customers' decision to interact with e-commerce platforms. This study looks at the variables impacting customer behaviour in the platforms using a quantitative research methodology. The results, which cover social risk, financial risk, product risk, and social impact, are important components. When it comes to purchasing, trust is an important consideration, especially in a market where worries about online fraud are prevalent. Furthermore, studies show that an easy-to-use interface increases the probability that customers will make a purchase since it promotes trust and contentment. The variety of products offered and the allure of pricing packages impact customer decisions as well, highlighting the necessity for e-commerce suppliers to adopt a holistic strategy. The research provides an overview of Indonesia's e-commerce scene and makes suggestions for merchants looking to improve consumer happiness and build a strong online presence

Proactive fraud defense: Machine learning’s evolving role in protecting against online fraud

As online fraud becomes more sophisticated and pervasive, traditional fraud detection methods are struggling to keep pace with the evolving tactics employed by fraudsters. This paper explores the transformative role of machine learning in addressing these challenges by offering more advanced, scalable, and adaptable solutions for fraud detection and prevention. By analyzing key models such as Random Forest, Neural Networks, and Gradient Boosting, this paper highlights the strengths of machine learning in processing vast datasets, identifying intricate fraud patterns, and providing real-time predictions that enable a proactive approach to fraud prevention. Unlike rule-based systems that react after fraud has occurred, machine learning models continuously learn from new data, adapting to emerging fraud schemes and reducing false positives, which ultimately minimizes financial losses. This research emphasizes the potential of machine learning to revolutionize fraud detection frameworks by making them more dynamic, efficient, and capable of handling the growing complexity of fraud across various industries. Future developments in machine learning, including deep learning and hybrid models, are expected to further enhance the predictive accuracy and applicability of these systems, ensuring that organizations remain resilient in the face of new and emerging fraud tactics.

Navigating online perils: Socioeconomic status, online activity lifestyles, and online fraud targeting and victimization of old adults in China

Online fraud poses a growing threat to the well-being of old adults. This study examines the causes and correlates of online fraud targeting and victimization among old adults in China. Drawing insights from the digital inequality theory and lifestyle routine activity theory and utilizing a nationally representative survey of Chinese old adults (N = 4293), this study examines how socioeconomic status and online lifestyle patterns affect their susceptibility to online fraud. The results show that old adults of better socioeconomic status are more likely to be targeted by online frauds, though not necessarily experiencing financial loss. Latent class analysis identifies four online activity lifestyles among old adults: basic, entertainment, risky, and advanced users. Subsequent analysis demonstrates that these lifestyle types predict different risks of online fraud targeting and victimization. Basic users face a heightened risk of being targeted but not necessarily monetary loss. Risky users, while not exhibiting elevated targeting risks compared to basic users, are more susceptible to financial loss once targeted. Advanced users, in contrast to basic users, face elevated risks of being targeted and monetary loss. This study offers new insights into the risk factors for online fraud targeting and victimization among old adults in China, informing the development of effective online fraud prevention programs.

Information Systems and Mechanism for Prevention of Cyber Frauds

Cyber criminals are targeting online frauds, exploiting anonymity to deceive. They use fake websites, fake ads and stolen credit or debit cards for purchases. Bank frauds, despite high-speed processing and technical assistance, can damage a bank's reputation and operational efficiency, necessitating a strong emphasis on business ethics in the banking sector. The machine learning and artificial intelligence enhance online security of information systems. US enforces anti-trust laws and promotes stakeholder rights, addressing fraud and identity theft through safety tips and civil law implementation. Cyber criminals steal personal information for unauthorized purchases, identity theft, and fraudulent activities. Machine learning and artificial intelligence can enhance online security and user awareness. AI-based threat detection analyzes network activity for cyberattacks, limiting damage. Multi-Factor Authentication (MFA) combines traditional passwords with biometric authentication for enhanced security. The application of Big Data systems allows administrations for the collection and analysis of Data and its storage obtained from various sources, using platforms like Hadoop, Apache Spark, and Kafka for real-time processing and data analytics. Digital devices are increasingly being used in banking frauds, posing significant risks to both customers and the banking sector, necessitating stricter regulations and enforcement measures. Common banking sector issues include negligent, fraudulent, and deviant behavior, affecting various functions of getting, gathering, transporting, disbursing, loaning, trading, capitalizing, replacing, and servicing money-claims domestically and internationally. This paper discusses machine learning and anomaly detection techniques for preventing fraud in online payment systems, including behavioral profiling and Bagged Decision Tree models along with other methods.

Is financial literacy a protection tool from online fraud in the digital era?

Cybercrime increased dramatically during the Covid-19 pandemic, when people’s online exposure rose significantly. In this paper, we analyze the relation between being a victim of online financial fraud and financial literacy, using representative Italian data collected in 2020. We find that basic financial knowledge is negatively associated with the probability of being defrauded online, while individuals who are overconfident about their level of financial literacy are more likely to fall victim to fraud. Moreover, those who were forced to work remotely because of the pandemic experienced a higher risk of fraud, that was reduced by financial literacy. Our findings suggest that financial literacy may provide protection against online fraud even in a context of high online exposure.

An intelligent approach to detect and predict online fraud transaction using XGBoost algorithm

The most popular payment method in recent years is the credit card. Due to the E-commerce industry’s explosive growth, the usage of credit cards for online purchases have been greatly increased as a result frauds has increased. Banks have been facing challenges to detect the credit card system fraud in recent years. Credit card fraud happens when the card was stolen for any unauthorized purposes or if the fraudster utilizes the credit card information for his own use. In order to prevent credit card fraud, it is essential to build detection measures. While detecting credit card theft with machine learning (ML), the features of credit card frauds play an important and they must be carefully selected. A fraud detection algorithm must be created in order to correctly locate and stop fraudulent activity as technology advances along with the amount of fraud cases. ML methods are essential for identifying fraudulent transactions. The implementation of fraud detection models is particularly difficult because of the sensitive nature of the data, the unbalanced class distributions, and the lack of data. In this work, an intelligent approach to detect and predict online fraud transaction using extreme gradient boosting (XGBoost) algorithm is described. The XGBoost model predicts whether a transaction is fraud or not. This model will achieve better performance interarm of recall, precision, accuracy and F1-score for credit card fraud detection.

Identifying and preventing future forms of crimes using situational crime prevention

Traditional crime has been falling for some time in most countries. However, developing technologies and our use of them are creating new opportunities for offending. For example, estimates from the Crime Survey of England and Wales indicate that in the UK, online fraud and related offences account for as many crimes as do “traditional” offences. To date, academia, law enforcement and governments have been reactive both in terms of identifying new and emerging forms of offending and in developing approaches to address them. In this paper, I will discuss the future crime agenda, and how futures thinking can help identify future crime opportunities and security threats including online fraud, crimes involving artificial intelligence, and crime in the metaverse. The paper will close with a discussion of the implications for theory and crime prevention.

Online fraud detection: ‘In the moment’ digital accountability in a data-sensitive setting

Online fraud detection: ‘In the moment’ digital accountability in a data-sensitive setting

Psychological predictors of online fraud victimhood in China: a machine learning approach

ABSTRACT Understanding why some individuals are more susceptible to becoming victims of fraud is crucial for developing effective anti-fraud strategies. This study employs a machine learning approach to explore the impact of individual psychological and socio-demographic characteristics on susceptibility to fraud. The random forest (RF) models reveal that psychological factors are more influential in determining an individual's vulnerability to fraud than demographic factors. Within the RF models, feature importance analyses highlight that subdimensions of critical thinking – such as truth-seeking, open-mindedness, and cognitive maturity – along with susceptibility to persuasion, perceived benefits on risk, and self-control, are pivotal in influencing an individual’s susceptibility to fraud. These insights are critical for informing targeted interventions and enhancing the effectiveness of anti-fraud measures.

Challenges of Indonesian Industrialization and Trade Problems in the Industrial Era 4.0 for the Indonesian Economy and Government Policy Breakthrough

Industry 4.0 was born in 2011, for Indonesia it has brought benefits to industrialization and trade starting from 24-hour production automation, time efficiency because the process is fast and connected to the internet for unlimited distances with the help of internet signals, saving minds because AI-based technology is able to think to answer our questions and the ability to store and provide big data, production and trading costs are clearly more efficient because paperless, sending data without couriers, calculating the use of production inputs is more accurate so it is more economical. However, besides the advantages, there are also many challenging problems in Indonesia, starting from misuse of online media platforms, cyber crime (illegal taking of company data/hacking, misuse of other people's personal data for online fraud and illegal investment) which causes state losses of more than Rp. 232, 646 trillion until 2023. Therefore, the government has issued a policy breakthrough in the form of policies that have a broad impact and legal regulations that provide strict limits and sanctions.

“Beyond Stereotypes”: Investigating the Roles and Motivations of Girls in Cybercriminal Networks in Ibadan Metropolis, Oyo State, Nigeria

This study investigates the involvement of girls in cybercriminal networks within Ibadan Metropolis, Oyo State, Nigeria. It examines their roles, motivations, and the socio-economic implications. The study is anchored on the Structural Strain and Sex Role theories. Utilizing an explanatory sequential mixed-methods design of the quantitative and qualitative data collection procedure, the respondents were purposively sampled. Findings of the research revealed that although girls' participation in cybercrime is relatively lower compared to boys, their roles are nonetheless significant and multifaceted, encompassing activities such as romance scams, phishing, online fraud, identity theft, and cyber-enabled financial crimes. Additionally, girls provide psychological support to their male counterparts, often engaging in domestic chores and serving as "sex tools," which facilitates the smooth operation of these networks. The findings indicate that motivations for girls' involvement in cybercrime include economic vulnerability, peer influence, poverty, access to Western education and technology, and the anonymity of the internet. The socio-economic implications were discovered to be profound, disrupting educational paths of girls, inflicting psychological damage, and perpetuating cycles of poverty and social instability. The study concludes that cybercriminal networks have devastating consequences on girls and thus, it was proposed that targeted interventions focusing on education, mental health support, and socio-economic empowerment are crucial in mitigating the risks and providing alternative pathways for at-risk girls.