Fraud Issues Research Articles

Research on the Criminal Law Response to Telecom Fraud in the Digital Society

With the rapid development of the digital society, telecom fraud has increasingly exhibited characteristics of intelligence, cross-border operations, and concealment, posing serious threats to both the economy and individual interests. However, the current criminal law has many deficiencies in addressing such crimes, making it difficult to effectively curb their spread. This paper analyzes the current state and characteristics of telecom fraud and explores the shortcomings of criminal law in responding to these new forms of fraud in the digital society. It proposes measures such as enhancing the adaptability of the law, improving sentencing standards, and strengthening cross-border judicial cooperation. Furthermore, the paper emphasizes the importance of a coordinated response between technological means and the law, recommending the use of big data and artificial intelligence to improve the investigation and prevention of telecom fraud. The study suggests that only through the deep integration of legal frameworks and technology can the increasingly complex issue of telecom fraud in the digital society be effectively addressed.

Multi-Elemental Analysis and Geographical Discrimination of Greek "Gigantes Elefantes" Beans Utilizing Inductively Coupled Plasma Mass Spectrometry and Machine Learning Models.

Greek giant beans, also known as "Gigantes Elefantes" (elephant beans, Phaseolus vulgaris L.,) are a traditional and highly cherished culinary delight in Greek cuisine, contributing significantly to the economic prosperity of local producers. However, the issue of food fraud associated with these products poses substantial risks to both consumer safety and economic stability. In the present study, multi-elemental analysis combined with decision tree learning algorithms were investigated for their potential to determine the multi-elemental profile and discriminate the origin of beans collected from the two geographical areas. Ensuring the authenticity of agricultural products is increasingly crucial in the global food industry, particularly in the fight against food fraud, which poses significant risks to consumer safety and economic stability. To ascertain this, an extensive multi-elemental analysis (Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ga, Ge, K, Li, Mg, Mn, Mo, Na, Nb, Ni, P, Pb, Rb, Re, Se, Sr, Ta, Ti, Tl, U, V, W, Zn, and Zr) was performed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Bean samples originating from Kastoria and Prespes (products with Protected Geographical Indication (PGI) status) were studied, focusing on the determination of elemental profiles or fingerprints, which are directly related to the geographical origin of the growing area. In this study, we employed a decision tree algorithm to classify Greek "Gigantes Elefantes" beans based on their multi-elemental composition, achieving high performance metrics, including an accuracy of 92.86%, sensitivity of 87.50%, and specificity of 96.88%. These results demonstrate the model's effectiveness in accurately distinguishing beans from different geographical regions based on their elemental profiles. The trained model accomplished the discrimination of Greek "Gigantes Elefantes" beans from Kastoria and Prespes, with remarkable accuracy, based on their multi-elemental composition.

Enhancing fairness, transparency and accountability during tendering under Ghana’s procurement system: a systematic review

PurposeGhana’s legal framework for procurement has undergone substantial reform to increase its efficacy. However, disregard for legal obligations set forth has resulted in issues of fraud, corruption and poor oversight. This study seeks to synthesize literature on the recognition of legal obligations arising from tendering procedures and measures to promote fairness, transparency and accountability under Ghana’s procurement framework.Design/methodology/approachLegal frameworks and publications from diverse countries have been synthesized using a systematic literature review across three databases (Scopus, JSTOR and HeinOnline) to illuminate key concepts, issues and best practices relevant to the study. Data obtained from included publications was synthesized using Sandelowski and Barroso’s two-step approach by using a qualitative meta-summary and thematic synthesis.FindingsThe study reveals that issues of conflict of interest, corruption, lack of capacity, inadequate oversight and insufficient legal follow-through hinder the effectiveness of procurement regulations. The findings highlight the need for targeted improvements in resource allocation for consistent application of transparency measures, regular publication of notices and robust enforcement of accountability mechanisms. The report proposes the creation of a common data environment for networking and information dissemination, implementing feedback systems and trust rating schemes.Practical implicationsThe study contributes to the body of knowledge on procurement regulation by providing a thorough analysis of Ghana’s procurement framework. The findings will help policymakers close the observed implementation gaps by guiding the revision of current legislation and the introduction of new regulations. Research findings can be used to guide the creation of focused training courses.Originality/valueThis study, one of the first of its kind in Ghana, examines the current procurement framework, including legal obligations and implementation challenges. It contributes to the body of knowledge on the subject by providing a current and fact-based analysis as well as relevant recommendations for strengthening the framework.

Sentiment Analysis of Support for the DPR's Right to Inquiry on the Issue of 2024 Election Fraud Using the Support Vector Machine Method

This research aims to analyze public sentiment towards supporting the DPR's right to inquiry in the 2024 Election fraud issue using the Support Vector Machine (SVM) method. Data was obtained from the social media application X which has a wide user base and is relevant to the issue under study. Comments on the application are classified into positive and negative sentiments after going through the pre-processing stage. The SVM method was chosen because of its high ability in text classification based on appropriate kernels. This research shows how much influence the X application has in identifying public sentiment and the effectiveness of the SVM method in sentiment classification. It is hoped that the research results will provide in-depth insight into public sentiment regarding the issue of fraud in the 2024 elections and support better decision making in the context of politics and democracy in Indonesia.

Analisis Konten Film Dirty Vote di Tiktok dalam Mempengaruhi Persepsi Masyarakat

This research aims to analyze how the content of the Dirty Vote film circulating on TikTok Social Media influences public perception of the election process and the results of the 2024 Election. The film "Dirty Vote" is a 1 hour 57 minute political film depicting allegations of fraud in the presidential election process in a fictional country. The research method used in this article is a qualitative approach by conducting content analysis of comments and responses from the public on TikTok social media related to the film "Dirty Vote". The results of the study show that this film succeeded in creating significant buzz and discussion on the platform. Many TikTok users expressed their opinions on the issue of fraud in the 2024 Presidential Election and shared the film with their followers. This shows that TikTok social media can be an effective tool for disseminating political information and influencing public opinion. In conclusion, the film "Dirty Vote" on TikTok social media has a significant influence on public perceptions to the issue of fraud in the 2024 Presidential Election.

Research on Legal Issues of Financial Fraud in Listed Companies

Research on Legal Issues of Financial Fraud in Listed Companies

Election Laws and Electoral Reforms in India and U.K.

This paper examines the election laws and electoral reforms in India and the United Kingdom, highlighting their evolution, current frameworks, and ongoing challenges. India, the world's largest democracy, operates under a legal structure largely defined by the Representation of the People Act, 1951, and is overseen by the Election Commission of India. The Indian electoral system, based on the First-Past-The-Post (FPTP) model, has undergone significant reforms to address issues of transparency, voter fraud, and campaign finance, including the introduction of electronic voting machines (EVMs) and Voter Verifiable Paper Audit Trails (VVPATs). In contrast, the United Kingdom's electoral system, rooted in centuries-old traditions, is regulated by laws such as the Representation of the People Acts and the Political Parties, Elections and Referendums Act, 2000. While the UK also employs the FPTP system, it faces criticism for not reflecting the proportionality of votes. Recent reforms in the UK have focused on improving voter registration, addressing electoral fraud, and debating alternative voting methods like the Single Transferable Vote (STV) to enhance democratic representation. The study identifies both similarities and divergences in the electoral processes of these two democracies, emphasizing the impact of their respective legal frameworks on electoral integrity and democratic participation. It also explores ongoing debates and proposed reforms aimed at enhancing the inclusiveness, fairness, and transparency of elections in both nations.

The 2024 General Elections in Indonesia: Issues of Political Dynasties, Electoral Fraud, and The Emergence of A National Protest Movements

This research aims to explain the phenomenon of political dynasties, electoral fraud, and protest movements during the 2024 General Elections in Indonesia. By using a qualitative descriptive method, this research focuses on aspects related to the main factors of the emergence of political dynasties, electoral fraud, and protest movements in the 2024 election based on secondary data obtained from online news sources. Data collection process was carried out through reading the news articles using the keywords “political dynasties”, “electoral fraud”, “protest movements”, and “2024 General Elections”. Data analysis was conducted through content analysis of news texts to obtain accurate data, descriptive insights and thematically explore the data inductively. The findings in this study show that the issues of political dynasties, electoral fraud, and the national protest movements in the 2024 General Elections are a series of interconnected phenomena. In addition, this study also reccomends further research on the importance of understanding public perceptions of political dynasties, the issue of electoral fraud, and protest actions against active voters in the 2024 General Elections using an empirical approach.

Novel Machine Learning Based Credit Card Fraud Detection Systems

This research deals with the critical issue of credit card fraud, a problem that has escalated in the last decade due to the significant increase in credit card usage, largely driven by advances in international trade, e-commerce, and FinTech. With global losses projected to exceed USD 400 billion in the next decade, the urgent need for effective fraud detection systems is apparent. Our study leverages the power of machine learning (ML) and presents a novel approach to credit card fraud detection. We used the European cardholders dataset for model training, addressing the data imbalance issue that often hinders the effectiveness of the learning process. As a key innovative element, we introduce compact data learning (CDL), a powerful tool for reducing the size and complexity of the training dataset without sacrificing the accuracy of the ML system. Comparative experiments have shown that our CDL-adapted feature reduction outperforms various ML algorithms and feature reduction methods. The findings of this research not only contribute to the theoretical foundations of fraud detection but also provide practical implications for the financial sector, which can benefit immensely from the enhanced fraud detection system.

AI-POWERED FRAUD DETECTION IN BANKING: SAFEGUARDING FINANCIAL TRANSACTIONS

The banking industry's metamorphosis through digitalization has unquestionably revolutionized accessibility and convenience for customers worldwide. However, this paradigm shift has ushered in a new era of challenges, most notably in the realm of cybersecurity. Conventional rule-based fraud detection strategies have struggled to keep pace with the rapid evolution of cyber threats, prompting a surge of interest in more adaptive approaches like unsupervised learning. Furthermore, the COVID-19 pandemic has exacerbated the issue of bank fraud due to the widespread transition to online platforms and the proliferation of charitable funds, which present ripe opportunities for exploitation by cybercriminals. In response to these pressing concerns, this study delves into the realm of machine learning algorithms for the analysis and identification of fraudulent banking transactions. Notably, it contributes scientific novelty by developing models specifically tailored to this purpose and implementing innovative preprocessing techniques to enhance detection accuracy. Utilizing a diverse array of algorithms, including Random Forest, K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees, and Logistic Regression, the study showcases promising results. In particular, logistic regression and decision tree models exhibit impressive accuracy and Area Under the Curve (AUC) values of approximately 0.98, 0.97 and 0.95, 0.94, respectively. Given the pervasive nature of banking fraud in our digital society, the utilization of artificial intelligence algorithms for fraud detection stands as a critical and timely endeavor, promising enhanced security and trust in the financial ecosystem.

LightGBM Model for Detecting Fraud in Online Financial Transactions

With the rapid development of online finance, combined with its significant potential threats, nowadays it has increasingly drawn attention to the issue of fraud in online payment transactions. Due to the large and diverse nature of online payment data and the binary nature of fraud detection, this study implements the machine learning algorithm LightGBM to detect fraudulent activities. Through data exploration and data preprocessing, the data is refined. Leveraging violin plots to observe the distribution of variables in fraudulent and non-fraudulent activities, and then an insignificant variable is removed. Next, the processed data is fed into the LightGBM model. The result yields a fraud prediction accuracy of 99.5%, shows a very high precision. Additionally, the study verifies the algorithm's robustness and generalization ability. Overall, this research holds significant importance, given its findings and implications for addressing fraud in the rapidly evolving landscape of online financial transactions.

A Comprehensive Approach to Safeguard Credit Card Transactions and Fraud Prevention

The escalating prevalence of financial fraud within the financial sector poses profound challenges. Detecting credit card fraud in online transactions necessitates data mining due to inherent complexities. Addressing two key issues—evolving patterns in legitimate and fraudulent behaviors and highly skewed datasets of credit card frauds—renders the task challenging. This paper scrutinizes the performance of naive Bayes, KNN, and logistic regression on significantly imbalanced credit card fraud data comprising 284,807 transactions from European cardholders. The dataset's skewness is addressed through a hybrid under-sampling and oversampling approach. The three techniques are applied to both unprocessed and preprocessed data. Fraud detection, defined as a set of activities thwarting illicit acquisition of assets or funds through deceptive means, varies across industries and methods. Credit card fraud, particularly susceptible due to its ease and prevalence in e-commerce and online platforms, prompted the adoption of diverse machine learning strategies to combat rising fraud rates. This paper employs machine learning algorithms for credit card fraud detection, utilizing a publicly available credit card dataset for model evaluation. While acknowledging that achieving 100% accuracy in fraud detection is elusive, the paper emphasizes the real-world applicability of its findings through the analysis of credit card data from a financial institution. In addition to assessing model efficacy, the study introduces noise into the data samples to evaluate algorithm robustness. Experimental outcomes underscore the effectiveness of the majority voting method, achieving commendable accuracy rates in detecting credit card fraud cases. The study sheds light on the pressing issue of credit card fraud, emphasizing the importance of deploying robust machine learning approaches for timely and accurate detection in real-world scenarios.

Implementation of Credit Card Fraud Detection

The issue of credit card fraud presents a notable concern within the financial sector, leading to considerable finacial losses for both financial institutions and consumers. To address this challenge, this study investigates the application of machine learning methods for detecting credit card fraud. We explore the performance of diverse machine learning algorithms on an actual dataset and suggest an ensemble-based approach that harnesses the strengths of multiple models. Our experimental outcomes demonstrate the effectiveness of machine learning in accurately identifying fraudulent transactions while minimizing false positives.

Cooperative operation of multiple low-carbon microgrids: An optimization study addressing gaming fraud and multiple uncertainties

To achieve efficient utilization of renewable energy and facilitate the development of a more equitable and harmonious power market, an optimization study is conducted focusing on peer-to-peer (P2P) power sharing within multiple low-carbon microgrids (LCMGs). Firstly, the coordinated operation mechanism of a LCMG is proposed involving a step-type carbon trading scheme as well as flexible supply and demand. Then, an optimization model of multi-LCMG cooperative operation is formulated based on the Nash bargaining theory. The original Nash bargaining problem is equivalently transformed into a subproblem of maximizing alliance benefits and a subproblem of maximizing the benefits of power trading. In the first subproblem, a combination of opportunity constraints and robust optimization method is employed to address these multiple uncertainties from renewable generation and electricity prices. The second subproblem addresses the issue of gaming fraud among LCMGs with a third-party intermediary model applied to achieve a fraud equilibrium. Further, to solve these problems efficiently, an improved adaptive parametric alternating direction method of multipliers (AP-ADMM) algorithm is used to protect the privacy of each party. Finally, a case study was conducted to validate the efficacy of the proposed method in enhancing the stability and cost-effectiveness of cooperative optimization within multi-LCMG systems.

Credit Card Fraud Detection

The issue of credit card fraud presents a notable concern within the financial sector, leading to considerable financial losses for both financial institutions and consumers. To address this challenge, this study investigates the application of machine learning methods for detecting credit card fraud. We explore the performance of diverse ma- chine learning algorithms on an actual dataset and suggest an ensemble-based approach that harnesses the strengths of multiple models. Our experimental outcomes demon- strate the effectiveness of machine learning in accurately identifying fraudulent transactions while minimizing false positives.

Mothers’ experience of virtual education during and after the COVID-19 pandemic: A qualitative study

This qualitative study was conducted with the aim of investigating the experience of mothers in Tehran in the field of virtual education during the COVID-19 pandemic. The participants in this study were 17 mothers of school children who experienced virtual education in Tehran during the COVID-19 pandemic. Data collection was done through semi-structured interviews, over the phone, and lasted for approximately 30–45 min. The interviews were audio recorded with the permission of the participants, transcribed verbatim, and analyzed using the method of conventional content analysis. For this study, 17 mothers were interviewed. Of those, 7 had one school-age child, 8 had two, and 2 had three. All of the children lived with both their mother and father. The majority of the mothers (16 out of 17) held bachelor's degrees. On average, the students were 11.76 years old. Fourteen of the students attended public schools, while 3 attended private schools. Most of the mothers (14 out of 17) reported their financial status as average. Data analysis led to the identification of 3 main themes and 12 subthemes. The three main categories were family-related factors, student-related factors, and school-related factors. The 4 themes related to family included 1) the role of family as a teacher, 2) the importance of direct and indirect supervision in family education, 3) the cost, and 4) the peace of mind. Factors related to students included 5 themes; 1) the advantages and disadvantages of virtual learning environments, 2) time management in education, 3) the consequences of failing to acquire necessary skills and knowledge, 4) issues of indifference and fraud in education, 5) physical, mental, and social problems affecting students. Factors related to schools included 3 themes; 1) the role of the teacher in virtual teaching and learning, 2) the challenges of lack of prior experience in education, 3) inequality in access to education and opportunities. The findings of this study have several implications for policymakers. Paying attention to technological and educational challenges related to virtual education and providing adequate support to parents are important things that should be prioritized. In addition, psychological support services should be made available to parents so that they can cope with the emotional challenges of virtual education. Finally, the positive experiences reported by mothers suggest that virtual education can be an effective and flexible option for future education, provided that the challenges are adequately addressed. Moreover, no work has been done on this issue comprehensively and the few studies that have followed this method have had some limitations. Therefore, schools, parents, and policymakers should explore innovative ways of integrating virtual education into the educational system to provide flexible and effective learning opportunities for students.

Systematic Review to Propose a Blockchain-based Digital Forensic Ready Internet Voting System

The ballot paper-based voting system has a high risk of data manipulation and vote tampering due to a lack of immutability, transparency and privacy. This systematic review is conducted with the intention of proposing a digital forensic-ready internet voting process to mitigate issues of vote rigging and vote fraud. The review focused on current and up-to-date literature. Publications that are out of this date range were ignored and considered stale or irrelevant. We extracted and reviewed publications with either or all the following keywords: “digital forensic”, “internet voting”, “e-voting” and “blockchain”. A total of thirteen databases were consulted. These include ACM, IEEE, Web of Science, Science Direct, Academic Search Complete, Access Science, ProQuest, Oxford Academy, Ingenta, Cambridge Core and Clarivate. From the inclusion list of one hundred and five (105) studies that were looked at in detail, twenty of them (i.e.,19%) covered blockchain technology. Two percent (2%) of them were focused on digital forensics in internet voting. The results herein were synthesised and presented in a qualitative methodology. The review shows that a secure and reliable digital forensic system could effectively mitigate vote rigging and fraud. Therefore, we proposed a blockchain-based digital forensic-ready internet voting system. The proposed system is beneficial to the electorate, election observers, electoral candidates, electoral administration bodies as well as the national law enforcement agencies.

Predicting Risk of and Motives behind Fraud in Financial Statements of Jordanian Industrial Firms Using Hexagon Theory

This study intends to identify the motives that lead to increasing or fighting the fraud risk in the Financial Statements (FSs) of industrial companies whose shares are traded in regulated and unregulated markets at the Amman Stock Exchange (ASE) based on the Hexagon theory, which divides the motives for fraud into six factors. The study relied on secondary data to collect and measure the study variables by extracting them from the annual reports that were published by those companies on the website of the ASE during the period of 2012–2017. The collected data were analyzed using the logistic regression model on the SPSS program. The results confirmed that the return on assets (ROA), percentage of independent members in audit committees, and tone-related party transactions had a statistically significant relationship with predicted fraudulent FSs, where these three variables belong to pressure, opportunity, and collusion fraud motives, respectively. Thus, it is worth mentioning that this study is distinguished from previous studies that examined the issue of fraud in Jordanian companies by detecting the motives of fraud according to the Fraud Hexagon theory. Moreover, some of the fraud motives were measured using new variables such as a change in inventory, the age of auditing committee’s members, and tone-related party transactions.

Casting light on the European anchovy: from biology to conservation and industry

This manuscript explores the role of European anchovies (Engraulis encrasicolus) in the central Mediterranean Region, shedding light on their ecological significance, conservation challenges, and sustainable utilization. The European anchovy is one of Europe’s most important fish resources in the Mediterranean basin, and it is considered a keystone species, playing a pivotal role in both ecological and socio-economic dimensions. However, in recent decades, European anchovy, together with Sardina pilchardus (commonly known as European sardine), has suffered a population decline for several reasons. Consequently, it is necessary to improve the management of anchovy fisheries by understanding the reproductive modes and characteristics, the influence of currents on the passive transport of eggs and larvae, the feeding habits, the environmental adaptability (e.g., salinity), and the distribution of ecotypes along the Italian coasts. Such information is fundamental for the management of fisheries, especially artisanal ones, and to control frauds, especially in protected, geographically referred, and traditional high-quality commercial products. Various aspects, ranging from their population dynamics to their industrial processing and the ecological implications of these activities were delved, highlighting the knowledge about anchovy populations and ecotypes and its importance in maintaining ecosystem balance and sustaining human communities. The ecological interaction of anchovies within the food web, as essential data in the conservation actions and management of these resources was emphasized. In addition, the metabolic and stomach contents diversity among anchovy populations and ecotypes was discussed, enhancing our understanding of their adaptability to varying environmental conditions. The manuscript then explores the traditional and industrial processing of anchovies, encompassing aspects ranging from fishing techniques (i.e., methods of capture) to their industrial significance, sustainability concerns, issues of fraud, and the establishment of geographical traceability. Finally, the opportunities for sustainable and biotechnological utilization of anchovy discards were also further explored, demonstrating the potential for waste reduction and resource optimization.

Identifikasi Kecenderungan Persepsi Investasi sebagai Respon Isu Investasi Bodong Pada Mahasiswa di Universitas Islam Majapahit

Issues are the basis for forming perceptions because often the issues that develop influence a person's perception. The negative issue of fraud under the guise of investment will of course have a big influence on the formation of investment perceptions. Students are a group that easily accepts issues through intensive interaction with communication media so that students are a group that is quite dynamic in forming investment perceptions. This research aims to analyze students' gender tendencies in forming investment perceptions and to analyze the influence of negative issues about fraud under the guise of investment on the formation of investment perceptions. The research method used in this research was a survey method on 381 students in Malang City. The research was conducted when there were many negative issues surrounding fraud under the guise of investment in 2017. The results of the research showed that investment perception was still quite low, namely the average score was 5.58 or 55.8% of the maximum score. The average investment perception for women is higher than men, namely 5.7. Men's investment perception score is lower, namely 5.36, this shows that women are more interested in investing than men.