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"Internet fraud."
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How are financial institutions enabling online fraud? A developmental online financial fraud policy review
Purpose
The criminalization of online financial fraud is examined by analyzing the existing literature, policies and state statutes within the context of the cybercrime ecosystem. Therefore, this paper aims to investigate online fraud policies within the USA and the prevalence of such incidents to explore the effectiveness of current fraud policies.
Design/methodology/approach
This examination explores policies related to online fraud within the USA by defining online financial fraud incidents within the context of the cybercrime ecosystem and analyzing such incidents with routine activities theory to emphasize the current legislative inadequacies with provisional policy recommendations.
Findings
This research suggests online financial fraud is not unanimously conceptualized among regulating or criminal institutions. Although federal regulators have governed financial institutions, federal institutions have failed to account for the capabilities of computer-mediated and technological device use (12 USC §1829).
Research limitations/implications
The limited research analyzing the effectiveness of guardianship that prevents or deters internet-mitigated or dependent financial fraud crimes.
Practical implications
Policy recommendations include but are not limited to mandating federal and privatized financial institutions to disclose all fraudulent activity to all stakeholders (e.g. customers and local and federal criminal justice agencies).
Originality/value
This paper provides an innovative approach using a criminological theory and policy framework to examine the prevalence of online fraud and the regulations enacted to counteract such violations.
Journal Article
Detecting, Preventing, and Responding to “Fraudsters” in Internet Research: Ethics and Tradeoffs
Research that recruits and surveys participants online is increasing, but is subject to fraud whereby study respondents — whether eligible or ineligible — participate multiple times. Online Internet research can provide investigators with large sample sizes and is cost efficient. Internet-based research also provides distance between the researchers and participants, allowing the participant to remain confidential and/or anonymous, and thus to respond to questions freely and honestly without worrying about the stigma associated with their answers. However, increasing and recurring instances of fraudulent activity among subjects raise challenges for researchers and Institutional Review Boards (IRBs). The distance from participants, and the potential anonymity and convenience of online research allow for individuals to participate easily more than once, skewing results and the overall quality of the data.
Journal Article
Information crisis
Discusses the scope and types of information available online and teaches readers how to critically assess it and analyze potentially dangerous information.
Book
Internet fraud transaction detection based on temporal-aware heterogeneous graph oversampling and attention fusion network
This study proposes an advanced Internet fraud transaction detection method, the Temporal-aware Heterogeneous Graph Oversampling and Attention Fusion Network (THG-OAFN), designed to address the increasingly severe fraud issues in EC. The method innovatively abstracts transaction data into a heterogeneous graph structure, captures temporal dynamic features through Gated Recurrent Unit (GRU), and fuses Graph Neural Network (GNN) to process static topological relationships. To address data imbalance, an improved Graph-based Synthetic Minority Oversampling Technique (GraphSMOTE) framework is introduced, maintaining the structural integrity of fraud clusters through k-hop topological constraints. Meanwhile, a multi-layer attention mechanism (including relationship fusion, neighborhood fusion, and information perception modules) is employed to achieve active fraud prevention. Experimental results show that THG-OAFN attains an area under the curve (AUC) of 96.56% (a 7.78% improvement over the best baseline). Moreover, it achieves a recall of 95.21% (a 6.29% improvement) and an F1-score of 94.72% (a 3.96% improvement) on the Amazon dataset. On the YelpChi dataset, these three metrics reach 90.43%, 89.51%, and 90.31%, respectively, remarkably outperforming existing GNN models. This achievement provides a deployable solution for dynamic fraud detection and active defense. Our code is available at https://github.com/wei4zheng/THG-OAFN.
Journal Article
Spotting online scams and fraud
\"Scammers adopt fake identities and use them to defraud people using texts, emails, social media sites, ecommerce sites, dating apps, and banking apps. Scammers can operate from almost anywhere in the world and in most cases victims have little chance of getting their money back\"-- Provided by publisher.
BOOK
Online payment fraud: from anomaly detection to risk management
Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account. Successfully preventing this requires the detection of as many fraudsters as possible, without producing too many false alarms. This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud. In addition, classical machine learning methods must be extended, minimizing expected financial losses. Finally, fraud can only be combated systematically and economically if the risks and costs in payment channels are known. We define three models that overcome these challenges: machine learning-based fraud detection, economic optimization of machine learning results, and a risk model to predict the risk of fraud while considering countermeasures. The models were tested utilizing real data. Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15% compared to a benchmark consisting of static if-then rules. Optimizing the machine-learning model further reduces the expected losses by 52%. These results hold with a low false positive rate of 0.4%. Thus, the risk framework of the three models is viable from a business and risk perspective.
Journal Article
Kiss me first : a novel
Leila, a sheltered young misfit, discovers an online chat forum where she feels accepted and falls under the spell of the website's charismatic founder, who entices her into assuming the stolen identity of a glamorous but desperate woman.
Book
Corporate Scandals and Household Stock Market Participation
We show that, after the revelation of corporate fraud in a state, household stock market participation in that state decreases. Households decrease holdings in fraudulent as well as nonfraudulent firms, even if they do not hold stocks in fraudulent firms. Within a state, households with more lifetime experience of corporate fraud hold less equity. Following the exogenous increase in fraud revelation due to Arthur Andersen's demise, states with more Arthur Andersen clients experience a larger decrease in stock market participation. We provide evidence that the documented effect is likely to reflect a loss of trust in the stock market.
Journal Article