Types Of Fraud Research Articles

부동산 전세 사기 방지와 개선방안 연구

Real estate Lease fraud refers, to fraudulent activities such as not actually renting the relevant real estate or disadvantaging the tenant in violation, of the terms of the JEONSE contract after receiving a JEONSE deposit while signing a JEONSE contract. If you don't actually rent, a property with a lease deposit, the tenant will not receive the deposit back and will have difficulty finding a place to live. Such real estate Lease fraud is recognized as a social problem, due to the problem of damaging the stability of the real estate market and infringing on the rights, and interests of tenants. Real estate Lease fraud is emerging as a social problem that undermines the, stability of the real estate market and violates the rights and interests of tenants. To solve this problem, institutional improvement is needed to prevent, real estate Lease fraud. In this paper, we would like to present institutional improvement measures to, solve these problems through the analysis of the concept and type of real estate Lease fraud and the causes, of real estate Lease fraud. In addition, through overseas case analysis, we would like, to investigate the operation and effectiveness of the real estate Lease fraud prevention system to find, ways applicable to Korea. To this end, the introduction will briefly explain the research background, research purpose, and research method, and then deal with, the concept and type of real estate lease, fraud, cause analysis, and overseas case analysis.

Fraud Detection of Credit Card Using Machine Learning

Abstract: This paper is focused on credit card fraud detection in real world scenarios. Nowadays credit card frauds are increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed paper we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions.

LITERATURE REVIEW: FRAUD AUDITING IN INDONESIA

Financial statement fraud or corruption is one of the most common types of fraud committed by company executives. Pressure, opportunity, rationalization, arrogance, and the ability to commit fraud are all factors that contribute to financial statement fraud. The role of the auditor is critical in detecting signs of fraud in financial reports. Therefore, the researchers wish to conduct a literature review on audit fraud, focusing on the role of auditing in identifying fraudulent corporate financial statements in Indonesia. Auditors play a crucial role in detecting fraud in a financial report, according to the findings of this study, which the researchers hope will provide future researchers with insight and references for research in the field of fraud auditing and inform the public of this fact.

Types of Frauds and their Impact on Retail Investors in Stock Market: A Quantitative Study

Types of Frauds and their Impact on Retail Investors in Stock Market: A Quantitative Study

Forensic Accounting Techniques and Attainment of Results in Federal Government Parastatals in Nigeria

Economic and financial fraud instances have risen over the past few years. However, fraud is a dynamic phenomenon, and new ways to defraud people, businesses, even government and the environment as a whole, appear every day. Adopting significant accounting techniques and fraud detection measures that are capable of identifying and preventing any type of fraud, regardless of how complex, is therefore imperative. The forensic accounting technique is one of these methods. This is one of the types of techniques that is primarily responsible for the investigation of fraud and is created primarily in the field of public and private finances. This study therefore examines the effect of forensic accounting techniques on attainment of results in Federal Government parastatals in Nigeria. The study adopted survey research design. The population of the study was 5 federal government agencies. Purposive sampling technique was used to select 2 agencies. The target population of the study was 131 accountants in the two agencies chosen- Nigeria Port Authority and Nigeria Civil Aviation Authority. Total enumeration was adopted. A validated and structured questionnaire was used to administer 131 copies of the questionnaire to the respondents. 126 copies were retrieved which represented 96% response rate. The Cronbach alpha reliability coefficients ranged from 0.740 – 0.892. The data were analyzed using descriptive and inferential (multiple regression) statistics at 5% level of significant. The result found that forensic accounting techniques had significant effect on output maximization in public sector of Nigeria (Adj. R2 = 0.183; F(6, 125)= 7.995, p < 0.05). The study concluded that forensic accounting techniques had a significant positive effect on attainment of result (effectiveness). The study recommended that government should adopt forensic accounting investigation techniques in corruption investigation significantly achieve attainment of results in government parastatals.

Umbrella terms conceal the sale of threatened shark species: A DNA barcoding approach

Food fraud in the seafood industry is a growing concern, including the authenticity and provenance of seafood products that are often unable to be confidently verified along complex, and at times obscure, supply chains. Among the diverse types of seafood fraud, mislabelling and species substitution are common, and have potential implications on human health, the economy, and species conservation. In particular, shark meat can be difficult to monitor, associated with the use of an array of convoluted nomenclature or ambiguous umbrella terms. In Australia, shark meat fillets are a popular seafood item sold under the umbrella term flake in takeaways, and fish and chip shops. We collected samples of flake as sold to consumers at 104 retailers in South Australia and used DNA barcoding to determine the shark species being traded. At least nine distinct species were identified, with only 27% of all samples identified as gummy shark (Mustelus antarcticus), a species that has sustainable fisheries, is locally caught, and is one of two species that is recommended to be labelled as flake in Australia. Four species were threatened taxa, including the CITES Appendix II listed short-fin mako (Isurus oxyrinchus) and smooth hammerhead (Sphyrna zygaena). Of all the samples and shops analysed, only 11% of retailers correctly identified the species they sold, another 20% were mislabelled and the remaining only had ambiguous labelling. Ultimately, the umbrella term flake allowed for species misrepresentation but DNA barcoding was an effective tool to test ambiguous labelling in processed and cooked shark meat products, and can guide policy, management, and compliance efforts to mitigate mislabelling, empowering consumers to make informed decisions and champion sustainable seafood.

The Rising Tide of Financial Crime: A Ponzi Scheme Case Analysis

Ponzi scheme is one of the most common types of fraud perpetrated by con artists. The reason for this is that the mode is relatively easy to conceal. In this regard, criminal law plays a significant role in countering fraudulent practices. It serves as an initial warning to individuals who intend to engage in such behavior that they may face harsh penalties if they do so. The study aims to compare and analyze the Ponzi and pyramid schemes in the framework of criminal law enforcement. It also thoroughly investigates the criminal rules and their application to specific cases associated with Ponzi and pyramid schemes in Indonesia. This normative or doctrinal legal research employs statutory, conceptual, and case approach. The study reveals that there is some confusion in the community as well as the legal enforcement officers on the practice of Ponzi and pyramid schemes. As a result of this discrepancy, law enforcement officers may be incorrect in ensnaring an allegation against the offenders, or they may even consider that offenders are innocent. It is also exacerbated by the lack of a single rule that particularly governs the issue of Ponzi schemes, which may result in a legal vacuum. This circumstance might therefore be used by irresponsible persons to create such business companies that use Ponzi schemes and conceal their actions with crypto investment or robot trading software, as recently occurred in Indonesia.

FRAUD AS A THREAT TO THE ECONOMIC SECURITY OF ECONOMIC ENTITIES

This article examines the essence of fraud, its conceptual apparatus. Based on the analysis of the types of fraud, a generalized classification has been compiled. Organizational and general social fraud prevention measures are proposed to ensure the economic security of economic entities.

Real-Time Phishing Website Detection using Machine Learning and Updating Phishing Probability with User Feedback

Phishing attacks remain a significant threat to internet users worldwide. Cybercriminals often send out phishing links through various channels such as emails, social media platforms, or text messages, to trick users into disclosing their sensitive infor-mation such as passwords, usernames, or credit card details. This stolen information is then used to perpetrate various types of fraud or sold on the dark web for profit. To combat this problem, various machine learning-based solutions have been developed for detect-ing phishing websites. However, these solutions vary in their effec-tiveness, with some focusing on URL-based algorithms while oth-ers focus on website content. This paper proposes a machine learn-ing-based approach to real-time phishing website detection, with a focus on the website's URL, domain page, and content. The pro-posed framework will be implemented as a browser plug-in, which can identify phishing risks as users visit websites. The framework integrates several techniques, including blacklist interception, whitelist filtering, and machine learning prediction, to improve ac-curacy, reduce false alarm rates, and minimize computation times. The proposed approach also incorporates user feedback to update the phishing probability over time, thereby increasing the accuracy of detecting phishing websites. This feedback loop involves users reporting suspected phishing websites to the system, which then updates the phishing probability calculation with new information to improve its accuracy. The significance of this research lies in its ability to provide real-time phishing detection capabilities, which can help protect internet users from falling victim to phishing at-tacks. Furthermore, the use of machine learning-based algorithms and user feedback ensures that the system is continuously updated to remain effective against new and emerging threats.

An overview on the Impact of Food Fraud Incidences in Various Countries and its Detection Methods, Assessment Techniques and Preventive Measures

Food fraud is not just a local issue but perhaps a global phenomenon. If the food available in the market are undetected or poorly controlled, this can harm consumer health. Food fraud causes a lack of traceability of supply chains and may eventually be a risk to food safety. The purpose of this paper is to acquaint the various types of food fraud and to evaluate the detection methods in identifying the adulterants. It also addresses the importance of vulnerability assessment of food fraud and key actions required for its prevention. Fighting food fraud will remain a race between the fraudsters and scientists developing new methods to prevent them. The review is unique that it summarized food fraud types, basic and instrument-based detection techniques for adulterants identification and it also focuses on the international governing bodies concerned with food laws and regulations. This study also provides perceptions of the interplay between vulnerability assessment and food fraud prevention.

PENYULUHAN MASYARAKAT MELEK DIGITAL DALAM MEMBANGUN KESADARAN DAN PERLINDUNGAN HUKUM TERHADAP PENIPUAN DAN PENCURIAN DIGITAL

Internet and social media as a result of globalization and technological revolution have become a new media in committing crime. Criminal Investigation Unit in 2022 there were 8,831 cases of cyber crime dominated by authentic data manipulation (3,723 cases) and fraud through electronic media (2,131 cases). Therefore it is necessary to have legal protection from state against security of social media as stated in Article 28C paragraph (1), Article 28F, and Article 28G paragraph (1) of the 1945 Constitution. Community service activities conducted to the people of RT.001/RW.006, Srengseng Village, Kembangan District, West Jakarta, with theme of digital literacy community to build awareness and legal protection against fraud and digital theft. This activity is in the form of legal counseling, with pre-survey stages, socialization, and monitoring and evaluation. This location was chosen because the community was a legal awareness group need to increase public understanding of digital security. The results achieved of legal counseling are the community to know the scope, form, type, mode, and workings of fraud and digital theft, know legal aspects and protection available, as well as efforts that need to be made to avoid fraud and theft digital so that people are expected to be able to literate on digital security. Suggestions to the government to increase socialization concerning institutions that handle fraud and digital theft, and scope of socialization needs to be expanded to increase digital literacy and legal awareness of community.

Financial statement fraud, recidivism and punishment

This study examines the relationship between the high reported levels of recidivism committed by Chinese firms and different punishments between 2007 and 2019. We find that there are six major types of financial statement fraud with many firms employing multiple fraud techniques simultaneously. When administrative and supervisory measures are used to punish fraudulent firms, recidivism is significantly reduced. In contrast, when self-regulatory measures are used to punish fraudulent firms, we report recidivism is not reduced yet increases. The results imply administrative measures are the most effective measures to tackle reoffending and self-regulatory measures are the least effective punishment.

A Review Analysis of Earnings Management Report Disclosure

Occupational fraud costs the average firm 5% of its annual income. This equates to a $3.7 trillion yearly loss due to fraud worldwide. The bulk of reported losses (51.9%) were less than $200,000. Financial statement fraud is the most expensive type of occupational fraud, with an average loss of $1,000,000. The most popular methods of detecting fraud are tips (42.2%), management reviews (16.0%), and internal audits (14.1%). Surprisingly, external audits (done by CPA firms) account for just about 3.0% of fraud detection. The majority of cases of fraud identified through tips come from workers (49.0%), followed by consumers (21.6%). Financial reports have been used to anticipate stock return, and firms with top management counsel (TMC) are less likely to see their stock price fall. Therefore, this paper offers a review of the research on earnings management conducted in earlier studies. The problem arising from earnings management will be covered under dilemma in earnings management disclosure studies. Next, evidence of earning management activities is discussing on the previous studied of earnings management. This study also investigates the approaches employed by early researchers to address this problem. The finding discovered by the researcher will be covered under the discussion on earnings management disclosure. Researchers can use this review paper as a reference for researching earnings management in annual reports

Phishing Attacks on Cryptocurrency Investors in the Arab States of the Gulf

With the rapid development of technology in all fields, including the financial field, people have flocked to invest in cryptocurrencies, sometimes without prior knowledge or experience. This has prompted hackers to prey on inexperienced investors through many types of fraud and attacks, especially phishing attacks. Cryptocurrency investment transactions take place without intermediaries such as banks and monetary institutions. Investing in cryptocurrencies is a form of peer-to-peer transaction and takes place without the involvement of physical wallets. This study addresses cases where people may become victims of phishing attacks due to the nature of cryptocurrency investments. The aim of this study was to understand the concepts of various phishing attacks on cryptocurrencies and to measure the awareness of cryptocurrency investors in the Arab Gulf countries regarding the security risks associated with cryptocurrency investments. This research was conducted by distributing a questionnaire among cryptocurrency investors and collecting and analyzing all the survey responses. The results reveal a lack of awareness about how to deal with the security risks associated with cryptocurrency investments. The research concludes that the majority of cryptocurrency investors are unaware of how to deal with phishing attacks. Finally, we address future research directions and recommend actions that can be taken to increase investors’ awareness of this issue.

In their own words: deception detection by victims and near victims of fraud.

Research on deception detection has usually been executed in experimental settings in the laboratory. In contrast, the present research investigates deception detection by actual victims and near victims of fraud, as reported in their own words. Our study is based on a nationally representative survey of 11 types of (mostly) online fraud victimization (N = 2,864). We used qualitative information from actual victims and near victims on why they didn't fall for the fraud, or how, in hindsight, it could have been prevented. The main detection strategies mentioned by near victims (N = 958) were 1) fraud knowledge (69%): these near victims clearly recognized fraud. Other strategies related to fraud knowledge were: noticing mistakes (27.9%), rules and principles about safe conduct (11.7%), and personal knowledge (7.1%). A second type of strategy was distrust (26.1%). A third strategy was 'wise through experience' (1.6%). Finally, a limited number of respondents (7.8%) searched for additional information: they contacted other people (5.5%), sought information online (4%), contacted the fraudster (2.9%), contacted their bank or credit card company (2.2%), or contacted the police (0.2%). Using knowledge as a strategy decreases the probability of victimization by a factor of 0.43. In contrast, all other strategies increased the likelihood of victimization by a factor of 1.6 or more. Strategies generally were uncorrelated, several strategies differed by type of fraud. About 40% of the actual victims (N = 243) believed that their victimization might have been prevented by: 1) seeking information (25.2%), 2) paying more attention (18.9%), 3) a third party doing something (16.2%), 4) following safety rules or principles, like using a safer way of paying or trading (14.4%), or by 5) 'simply not going along with it' (10.8%). Most of these strategies were associated with a higher, not lower, likelihood of victimization. Clearly, knowledge of fraud is the best strategy to avoid fraud victimization. Therefore, a more proactive approach is needed to inform the public about fraud and attackers' modus operandi, so that potential victims already have knowledge of fraud upon encountering it. Just providing information online will not suffice to protect online users.

Forecasting of digital financial crimes in Russia based on machine learning methods

In the modern world, economic relations, business, and markets are increasingly being transferred to the online world. Accordingly, the percentage of a new type of financial fraud – digital crimes – is also growing. In Russia, they are one of the leaders in economic crimes. Constant development and improvement of digital technologies make for the emergence of increasingly sophisticated types of digital fraud. Information security management systems are among the most important parts of both public and private internal policy of the enterprises that use them. Every year, the issue of combating cyber attacks is becoming more acute. For example, in 2021, the volume of transactions without the consent of bank customers increased by 38.8% compared to 2020. For cyber risks not to lead to such serious consequences, it is necessary to determine the influence of external factors on the dynamics of digital financial crimes in Russia. We will develop a predictive system based on machine learning methods to predict the number of digital financial crimes.

Detection of Salmonidae ingredient using mini-DNA barcoding in conjunction with a rapid visual inspection method

Using species outside Salmonidae to prepare salmon products has been widely revealed as a new type of fraud in China and other countries with less stringent labeling stipulations, highlighting the necessity to establish proper methods for rapid screening of Salmonidae ingredient. The present work selected mitochondrial Cytb gene as the target, and developed a novel mini-DNA barcoding assay to identify the salmonids ingredient in processed fish products. Results showed that the novel method is highly specific for salmonids detection and only the seven common salmonids species were successfully amplified. With the inclusion of HRM analysis, we can make an accurate differentiation of Salar salar, Oncorhynchus mykiss, and the other wild-caught Oncorhynchus species, which are deep in the scandal of species mislabeling. Moreover, a closed-tube visual detection can also be achieved by the preaddition of HNB (hydroxyl naphthol blue) and SYTO 9 in the reaction mixture, allowing an easy and rapid way to distinguish positive from negative. Finally, the novel assay was successfully applied on commercial products and the absence of salmonids ingredient indicated mislabeling with 4 commercial products. Overall, mini-DNA barcoding is a robust tool for fish species identification and the novel method is effective in screening Salmonidae ingredient.

English

Phishing is a type of online fraud which enables attackers to trick individuals into giving away confidential data like login credentials or financial data. A phishing website utilizes a URL that is comparable to a reasonable website to trick users into thinking it is legitimate, or it may comprise suspicious links or forms which has been developed for collecting sensitive data from users. Machine learning (ML) can be utilized to categorise websites as phishing or legitimate to protect users from falling victim to these attacks. There are several approaches to using machine learning for phishing website classification. This article focuses on the design of Chicken Swarm Optimization with Improved Random Forest for Phishing Website Classification (CSOIRF-PWC) technique. The CSOIRF-PWC technique aims to discriminate the legitimate and phishing websites accurately. To execute this, the presented CSOIRF-PWC approach initially performs the data normalization process. Next, the classification of phishing websites takes place using the IRF classifier. For improving the classification performance of the RF classifier, the parameter tuning process is performed through the CSO algorithm, which supports attaining improved classification performance. The simulation values of the CSOIRF-PWC methodology are investigated on two datasets, and the outputs are investigated under diverse measures. The comprehensive comparative outcomes emphasized the enhanced performance of the CSOIRF-PWC system over other methodologies in terms of several measures.

Fraud in a digital society in the context of social change

The article analyzes the problems of fraud in the modern digital society in the context of social changes caused by both the COVID-19 pandemic and the current geopolitical situation. The authors study in detail the types of financial fraud in this area. They pay special attention to the types of digital fraud: telephone fraud and IP-telephony, SMS fraud, phishing, sniffering (message interception), skimming (theft of card data), which are associated with the emergence of cryptocurrencies. The article presents the data of the authors’ sociological research conducted by the method of an online questionnaire survey (n = 765). The study showed that only 44 % of respondents are sufficiently informed about the types of fraud to protect themselves from modern internet scammers. An analysis of data on certain types of digital fraud showed that 20 % of respondents lost money due to telephone or Internet fraud, 16 % of respondents personally encountered IP telephony, and 40 % have no idea about such a type of fraud as sniffering and existing anti-virus protection against it. The research also showed the vulnerability, first of all, of the male half of the population over 40 years of age in the field of internet fraud. Therefore, public authorities, together with the media, business and other actors of Russian society, need to strengthen the policy in the field of improving the level of financial and digital literacy of the population.

Can digital transformation prohibit corporate fraud? Empirical evidence from China

ABSTRACT The pace of corporate digital transformation has been accelerating all over the world. This paper investigates the impact of digital transformation on corporate fraud with probit model in Chinese scenario. The results show that the digital transformation of enterprises can help curb the occurrence of fraud in general. However, the heterogeneity test results suggest that the application of digital technology may increase the possibility of some types of fraud as well. Considering possible endogeneity issues, this paper checks the results with the methods of Heckman two-step model and IV probit model and the conclusion remains robust. The empirical results suggest that accelerating the digital transformation of enterprises will help improve the quality of enterprises, optimize the capital market environment, but it is very important to guard against delayed disclosure in digitalization era.