Fraud Information Research Articles

Research on Financial Fraud Based on Changyuan Group's Acquisition of Changyuan and Eagle

In recent years, with the development of the China’s economy, in order to seek business growth points, the number of mergers and acquisitions of enterprises has increased rapidly. In the process of mergers and acquisitions, there are often some financial fraud behaviors, such as fictitious income and false statements, to push up the merger and acquisition premium, which will greatly damage the stability of the financial market and the legitimate rights and interests of investors. This paper adopts the financial fraud case of Changyuan Group's acquisition of Shanghai Heying Electromechanical Technology Co., LTD. in 2016 as an example, and combines the fraud triangle theory to analyze the motivation of financial fraud. According to the punishment announcement and other information, this paper analyzes the identification of financial fraud information with a three-dimensional model, analyzes and presents the consequences of financial fraud, and proposes the suggestions and enlightenment for subsequent M&A cases.

An imbalanced learning method based on graph tran-smote for fraud detection

Fraud seriously threatens individual interests and social stability, so fraud detection has attracted much attention in recent years. In scenarios such as social media, fraudsters typically hide among numerous benign users, constituting only a small minority and often forming “small gangs”. Due to the scarcity of fraudsters, the conventional graph neural network might overlook or obscure critical fraud information, leading to insufficient representation of fraud characteristics. To address these issues, the tran-smote on graphs (GTS) method for fraud detection is proposed by this study. Structural features of each type of node are deeply mined using a subgraph neural network extractor, these features are integrated with attribute features using transformer technology, and the node’s information representation is enriched, thereby addressing the issue of inadequate feature representation. Additionally, this approach involves setting a feature embedding space to generate new nodes representing minority classes, and an edge generator is used to provide relevant connection information for these new nodes, alleviating the class imbalance problem. The results from experiments on two real datasets demonstrate that the proposed GTS, performs better than the current state-of-the-art baseline.

Does Exposure to Election Fraud Research Undermine Confidence in Elections?

Abstract Can academic research on election fraud undermine public confidence in elections? As election results become instantaneously accessible all over the world, academic research can increasingly influence perceptions of election integrity. To examine how electoral fraud information from academic research can affect voters’ beliefs about the legitimacy of election results, we conducted a survey experiment following the 2020 general election in South Korea. Using a nationally representative sample of Korean voters and exploiting a unique real-world situation featuring foreign academics considered relatively impartial, we found that exposure to hypothetical election fraud claims undermined confidence in electoral outcomes, measured via both self-reports and behavioral measures. Moreover, the claims’ impact was conditional on voters’ partisan attachments—strongest among supporters of the losing party—which is in line with the logic of motivated reasoning. Our work contributes to the literature on election integrity, using a relatively new yet stable democracy as its test case.

What are the key drivers of Fraud reporting in Takaful insurance? Evidence from count data models

Purpose This study aims at investigating the effect of corporate governance attributes on the Fraud disclosure of Takaful insurance companies in Saudi Arabia from 2014 to 2022. Design/methodology/approach This study uses a self-constructed disclosure index to quantify the level of fraud information using content analysis. The count regression (Poisson and negative binomial) models in panel data modeling are used to check the interdependence relationship between the Fraud disclosure and the corporate governance structure of 26 Takaful insurers. Findings The findings confirm the negative effect of ownership structure and the board size on the Fraud disclosure. However, the high proportion of independent board members, the audit board committee and the size of the risk board committee positively affect the extent of Fraud disclosure. Finally, this study provide evidence that large size of Shariah board is associated with a lower level of voluntary Fraud disclosure. Research limitations/implications Both economics-based theories and social exchange theory provide a better basis upon which to understand mechanisms by which board of directors in Takaful insurance provides external stakeholders with valuable information about corporate fraud. Practical implications It seems important to equip audit and Shariah board committee with the tools to give them an operational content that focus systematically on the “tone at the top” in investigating fraud, including disclosure and discipline. Originality/value Corporate governance is rapidly changing in Saudi Arabia and it is unclear whether adopting a corporate governance practices in financial institutions is appropriate for Islamic insurance companies.

The impact of honey fraud information on the valuation of honey origin: Evidence from an incentivized economic experiment

The impact of honey fraud information on the valuation of honey origin: Evidence from an incentivized economic experiment

Agri-Food Fraud in a Geographic Area in Relation to Global Health: A Probe into the Perceptions, Knowledge, Attitudes, Practices, and Concerns of Cameroonian

Objective: This study probes into the perceptions, knowledge, attitudes, practices, and concerns of Cameroonians on agri-food fraud intra-/inter-Cameroon trade borders about the local and global health of consumers. Results: Two hundred and two Cameroonians (56% male and 44% female; including business people, entrepreneurs, farmers, students, etc) responded to the online questionnaire. Many participants (81%) were interested in the origin and authenticity of food they consume. Their perception on authentic agri-food product is mostly influenced by feedbacks from other consumers (59%), besides other factors (price/cost, labeling, packaging, and taste). Most participants (58%) sought information about the food products from other consumers (46.4%), in addition to the internet, self-intuition, product labels, and from retailers and suppliers. Participants (54%) considered canned food, fish, red palm oil, honey, spices, milk, meat, palm wine, cookies, wine, brewed, and powdered products to be vulnerable to fraud. Participants specified that intake of authentic food makes consumers healthier (73%) and nurtures confidence in the brand (36%), while dietary exposure to manipulated foods may result in sicknesses (80%), and a decrease in consumers’ confidence in the food brand. Participants think agri-food fraud across Cameroon trade borders is commonplace (91%), as such food products should be bought cautiously (88%). The majority of participants indicated that to ensure the authenticity and safety of food products across the Cameroon trade borders, there is a need for proper regulations and standards (81%), effective traceability systems (69%), technology such as blockchain (48%), and multi-stakeholder collaboration (48%) including international collaborations focusing on shared intelligence (76%), unified food standards (53%), accompanied by joint inspections (59%) and data exchange (48%). Participants speculated that the global disease burden may be linked to the consumption of manipulated foods (91%). Conclusion: There is speculation on agri-food fraud in Cameroon and across its trade borders. Thus, there is a need for proper agri-food fraud information, education, and communication to improve consumers’ awareness and health protection. There is a needfor a wareness raising on agri-food fraud from multistake holder view-point (e.g. consumer advocacy groups) at national and international levels.

Pengaruh Sistem Pengendalian Intern dan Asimetri Informasi terhadap Kecurangan (fraud) Laporan Keuangan pada Kantor Pusat PT Bank Sulselbar Makassar

This research aims to determine whether the system of internal control and fraud information asymmetry affect the financial statements of PT Bank Sulselbar Makassar. Collecting data using primary data obtained from questionnaires using sampling techniques nonprobability (saturated sample). The population is 50 employees, and samples taken 50 respondents. Methods of data analysis using multiple linear regression techniques.The results of this study indicate that the internal control system and significant negative effect, and the information asymmetry influence positively and significantly

Improving transaction safety via anti-fraud protection based on blockchain

Financial enterprises generate profits based on economic development. More importantly, a healthy market is difficult to achieve due to their susceptibility to the parasitic credit card fraud transactions that accompany economic growth, unless an effective anti-counterfeiting technology is developed to alleviate the issue. To solve the problem, we propose a gradient-boosting decision tree based anti-fraud protection with blockchain Technology, referred to as GBDT-APBT, which treats anti-fraud transaction model as the accumulation of the classfiers' weakness and builds up a classifiers' to judge whether the transaction is fraudulent. Each user's private data is trained offline at the local blockchain node, then the trained model is directly uploaded to the cloud, and the final consensus model is obtained by voting. Due to incorporating blockchain technology, GBDT-APBT demonstrates decentralisation, openness, autonomy, anonymity, and immutability, showing its ability to satisfying the demand for an effective and beneficial anti-counterfeiting system, with high performance and effectiveness in detecting fraud information. Experiments show that compared with other methods, GBDT-APBT offers a promising approach to the security of credit card transactions with reference to the detection accuracy.

The role of different information sources in information spread: Evidence from three media channels in China

Using a comprehensive dataset from 2008 to 2018, we study the role of different information sources (traditional media, new media, and social media) on information spread in the Chinese stock market. The findings reveal varied leading and lagging relationships in the timeliness of fraud information disclosure from three media types and the most significant impact from social media. The results on price synchronicity reveal the significantly negative influence of all media coverages; among them, the impact of new media is in the first position, social media is in second place, and traditional media is the smallest. Furthermore, this influence is more pronounced for firms that are not audited by Big 4 auditors or with lower institutional ownership. In summary, we reveal the different effects of three media channels around fraud events and price synchronicity in China.

Establishment of a food fraud database and analysis of fraud information based on network data in China

Establishment of a food fraud database and analysis of fraud information based on network data in China

Light At the End of The Tunnel: Re-Storying the Experiences of Teacher- Covid19 Survivors

The continued service at the frontline of educational delivery in the time of COVID19 pandemic situates teachers to susceptibility that is no less critical. This study aimed to tell the experiences of teachers who were in the throes of COVID-19 disease and have recovered, through the narration of their personal stories. Narrative Inquiry was employed and facilitated by the use research interviews and written accounts of 4 teacher-survivors. Using Narrative Thematic approach in data analysis, themes were developed and were formatted in a literary chronological sense of the pre, during and post COVID19 stages of their journey to tell a cohesive narrative story. The plot started with Skepticisms and disbelief that describe the pre-COVID stage. Guilt, Anxiety and Shame; Fraud Information and Disclosure; Stigmas and Discrimination; and experiences pertaining to Temporality and Place; to their Being a Teacher; and to Relationship and Sociality, capture their During-COVID19 stage. The common accounts of Lessons and New Beginnings articulate the Post-COVID19 Stage. Recommendations to help advance the public’s understanding of the experiences of these teacher-survivors were also explicated.

Image Based Classification of Rumor Information from the Social Network Platform

Spam emails based on images readily evade text-based spam email filters. More and more spammers are adopting the technology. The essence of email is necessary in order to recognize image content. Web-based social networking is a method of communication between the information owner and end users for online exchanges that use social network data in the form of images and text. Nowadays, information is passed on to users in shorter time using social networks, and the spread of fraudulent material on social networks has become a major issue. It is critical to assess and decide which features the filters require to combat spammers. Spammers also insert text into photographs, causing text filters to fail. The detection of visual garbage material has become a hotspot study on spam filters on the Internet. The suggested approach includes a supplementary detection engine that uses visuals as well as text input. This paper proposed a system for the assessment of information, the detection of information on fraud-based mails and the avoidance of distribution to end users for the purpose of enhancing data protection and preventing safety problems. The proposed model utilizes Machine Learning and Convolutional Neural Network (CNN) methods to recognize and prevent fraud information being transmitted to end users.

PENGARUH EFEKTIVITAS PENGENDALIAN INTERNAL, ASIMETRI INFORMASI DAN KETAATAN ATURAN AKUNTANSI TERHADAP KECURANGAN AKUNTANSI

The purpose of this research is to examine the influence of effectiveness of internal control, information asymmetry and fidelity to accounting rules on accounting fraud. The population in this study is the regional work unit of Aceh Besar District, which the sampling technique uses non probability sampling with purposive sampling method. Respondents who are the object of this research are those who are most responsible for regional financial management consisting of the Head of Institution, Technical Activity Management Officer (PPTK) or Secretary and Head of Finance at 34 SKPD. Data analysis method used is multiple linear regression with the help of STATA 15. The result of this research shows that (1) effectiveness of internal controls, information asymmetry and fidelity to accounting rules has an effect on accounting fraud (2) ) effectiveness of internal controls has an effect on accounting fraud (3) information asymmetry has an effect on on accounting fraud and (3) fidelity to accounting rules has an effect on accounting fraud.

Data Quality Analysis based Machine Learning models for Credit Card Fraud Detection

Misrepresentation of money is a developing issue in monetary business with far-reaching consequences and keeping in mind that many processes have been found. Data quality management with data mining has been effectively applied to data sets to mechanize the investigation of massive amounts of complex information. Data mining has likewise played a notable role in identifying credit card fraud in online exchanges. Fraud detection in credit cards is a data quality management issue that considered under data mining, tested for two important reasons — first, the profiles of ordinary and false practices habitually change, and also because of the explanation that charge card fraud information is exceptionally slow. This research paper examines the performance of Decision Trees, Logistics Regression, and Random Forest rely strategically on profoundly skewed credit card fraud data. The dataset of credit card transactions is sourced from Kaggle (a publically accessible dataset repository) with 284,807 transactions. These methods are applied to raw data values and data preprocessing techniques. Assessment of the performance of techniques depends on accuracy, sensitivity, specificity, precision, and recall. Results indicate the optimal accuracy for the decision trees, logistics regression, and random forest classifiers with 90.8%, 98.5%, and 99.1% respectively.

Dynamic Control of Fraud Information Spreading in Mobile Social Networks

Mobile social networks (MSNs) provide real-time information services to individuals in social communities through mobile devices. However, due to their high openness and autonomy, MSNs have been suffering from rampant rumors, fraudulent activities, and other types of misuses. To mitigate such threats, it is urgent to control the spread of fraud information. The research challenge is: how to design control strategies to efficiently utilize limited resources and meanwhile minimize individuals' losses caused by fraud information? To this end, we model the fraud information control issue as an optimal control problem, in which the control resources consumption for implementing control strategies and the losses of individuals are jointly taken as a constraint called total cost, and the minimum total cost becomes the objective function. Based on the optimal control theory, we devise the optimal dynamic allocation of control strategies. Besides, a dynamics model for fraud information diffusion is established by considering the uncertain mental state of individuals, we investigate the trend of fraud information diffusion and the stability of the dynamics model. Our simulation study shows that the proposed optimal control strategies can effectively inhibit the diffusion of fraud information while incurring the smallest total cost. Compared with other control strategies, the control effect of the proposed optimal control strategies is about 10% higher.

The Effect of Fraud Disclosure, Internal Control, Information Technology on Corporate Governance and Corporate Performance as Moderating Variable

This research is a quantitative causality study that analyzes the effect of fraud disclosure, internal control, information technology on corporate performance and corporate governance as moderating variables. The results of this study are expected to contribute to the existing literature by overcoming some of the limitations articulated from previous studies. First, it offers new empirical insights on company risk and disclosure of fraud by management by basing research in behavioral theories that emerge from the board of directors and corporate governance, where corporate decision making is not only assumed to be underlined by formal approaches and CG mechanisms, but also CG arrangements informal, limited rationality, political bargaining, routine and satisfying behavior. The fixed-effect regression model is used to study the relationship of fraud disclosure, internal control and information technology of financial performance while the random-effect regression model is used from non-financial performance. Panel data of 192 sample observations from public companies in the banking sector which are listed on the Indonesia Stock Exchange and are included in the Top 50 ASEAN Scorecard. The findings show the disclosure of fraud, internal control and information technology have a positive effect on company performance, both financial and non-financial performance and corporate governances strengthen the financial performance but weaken the non-financial performance Keywords: Disclosure of Fraud, Internal Control, Information Technology, Corporate Performance, Corporate Governance DOI: 10.7176/EJBM/12-33-09 Publication date: November 30 th 2020

IPO information acquisition and investor protection

PurposeThe purpose of this study is to examine investors’ intention and behavior concerning ex ante information acquirement and ex post claims from the micro-level perspective with the deepening of the initial public offering (IPO) reform of China.Design/methodology/approachThe authors made surveys and collected 932 valid questionnaires from investors in China. The authors also conducted interviews with sophisticated investors, investment bankers and government regulators to obtain first-hand information. Based on the survey results, the authors make the empirical analysis.FindingsInvestors’ attention to the first-hand information of the IPO prospectuses is inadequate. Individuals rely more on second-hand information, while institutions conduct more surveys. The higher the institutional practitioners’ degree of education, the more surveys they make. Only 1/3 investors intend to seek judicial remedy when getting fraud information due to high litigation costs and proof collecting difficulties. The investors who read more about prospectuses in advance are more likely to seek judicial protection afterwards. Compared with investors who know less about government administrative protection measures, those who know more have a low probability to choose “not to seek judicial protection.”Originality/valueThe authors enrich the research studies of IPO information acquisition and investor protection by conducting surveys to get first-hand data. Previous literature mostly makes empirical tests by using proxy variables.

One Person, One Vote: Estimating the Prevalence of Double Voting in U.S. Presidential Elections

Beliefs about the incidence of voter fraud inform how people view the trade-off between electoral integrity and voter accessibility. To better inform such beliefs about the rate of double voting, we develop and apply a method to estimate how many people voted twice in the 2012 presidential election. We estimate that about one in 4,000 voters cast two ballots, although an audit suggests that the true rate may be lower due to small errors in electronic vote records. We corroborate our estimates and extend our analysis using data from a subset of states that share social security numbers, making it easier to quantify who may have voted twice. For this subset of states, we find that one suggested strategy to reduce double voting—removing the registration with an earlier registration date when two share the same name and birthdate—could impede approximately 300 legitimate votes for each double vote prevented.

Panama Papers: How Data Science Fought Corruption

The Panama Papers are eleven million leaked electronic documents that detail financial and attorney–client information for more than two hundred thousand offshore entities. The documents were leaked in April 2016 by an anonymous whistle-blower from the database of Panamanian law firm and corporate service provider Mossack Fonseca. In this case study, we discuss how a team of international network of journalists collaborated using data mining tools to unearth vital financial fraud information from this large chunk of unstructured data. The case study begins with a prologue of dialogue between the whistle-blower and protagonist. The case is divided into nine mini chapters where we start with how a data team were formed followed by the tools used to clean and annotate the unstructured data into a graph-based database and finally using expert network of journalists to generate financial insights from the various nodes and links of the graph. We conclude the case by providing word clouds that highlights Indian connections.

Does prior knowledge of food fraud affect consumer behavior? Evidence from an incentivized economic experiment.

This study uses a laboratory experiment to examine whether prior knowledge of food fraud persistently affects consumer behavior. We invited regular consumers of olive oil to participate in an olive oil valuation experiment. We used a within-subject design to compare consumers' willingness to pay (WTP) for Italian extra virgin olive oil (EVOO) before and after receiving information about labeling scandals in the Italian olive oil industry. After the first round of bidding, but before introducing information about labeling scandals or otherwise mentioning food fraud, we surveyed participants about whether they had heard of food fraud. Results indicate that prior knowledge of food fraud plays an important role in explaining consumers' valuation behavior, both in the pre-information baseline bidding and in how they update their valuation in response to information about a food fraud scandal. Consumers who reported prior knowledge of food fraud partially accounted for the possibility of food fraud in their initial pre-information valuation, submitting significantly lower bids than participants who did not report prior knowledge. They also reacted less to olive oil fraud information than consumers who reported no prior knowledge of food fraud. Findings of this study highlight the potential long-term consequences of increasing consumer awareness of food fraud incidents on consumer WTP for products in industries that have experienced food fraud scandals.