Fraud Cases Research Articles

Enhancing fraud detection efficiency in mobile transactions through the integration of bidirectional 3d Quasi-Recurrent Neural network and blockchain technologies

Cases of financial fraud are increasing despite recent technical breakthroughs. It is tough to find authentic financial transaction data due to privacy issues and a lack of inter-organization synergy. However, for technologies based on data, such as machine learning to function accurately inside practical systems, they require actual data. This manuscript proposes a Fraud Detection (FD) Efficiency in Mobile Transactions through the Integration of Bidirectional 3D Quasi-Recurrent Neural Networks and Proof of Voting consensus Blockchain Technologies (Bi-3DQRNN-PoV-FD-MT). A dataset of Bitcoin transactions is employed in the suggested model. The banking sector’s Bitcoin transactions serve as the foundation for this dataset. Then the data balancing is performed using a Self-Adaptive Synthetic Over-Sampling Technique (SASOS). Then, the proposed framework utilizes the Bi-3DQRNN to determine if the information is fraudulent or not. Moreover, an intelligent Enhanced Artificial Gorilla Troops (EAGTO) Optimization algorithm is introduced to tune the weights of the model parameters. A blockchain-based proof of Voting (PoV) consensus algorithm is integrated with the proposed model for forecasting upcoming transactions. The developed scheme is instigated in Python and the performance provides 12.09%, 8.91%, and 6.92% higher accuracy compared with existing techniques like K-nearest neighbor- Distributed Blockchain Consortium (KNN-DBC), Decision tree-Ethereum blockchain-enabled smart contract (DT-EBSC) and Heterogeneous Graph Transformer Networks-Ethereum smart contract (HGTN-ESC).

Cyber Fraud with Profile Cloning Mode in The Perspectives of Cyberculture and Space Transition Theory

This study aims to investigate whether there are still any society who do not understand cyber culture on social media which then has the potential to become victims of cyber fraud crimes. In the digitalization era 4.0, people tend to use technology to do various activities and meet their needs, including online transactions for goods and services using various digital platforms and social media. The high activity on the Internet is also accompanied by opportunities for crimes by utilizing the internet media. One of the cases that is increasingly occurring in the digitalization era is cyber fraud with profile cloning mode. The author is interested in studying cyber fraud in Wajo Police because the mode used was profile cloning, which used the profile of a law enforcer to make fictitious buying and selling. This study used a qualitative approach and a case study method to conduct an in-depth analysis of cyber fraud cases with profile cloning mode. The results of the study show that there are still people who do not understand cyberculture on social media, so they have the potential to be victims of cyber fraud crimes. Moreover, the results of the study also show that perpetrators commit cyber fraud with profile cloning mode due to the pressure in real life and the ease of cloning accounts on social media. Thus, such circumstances provoke perpetrators to shift their evil deeds to cyberspace.

Forecasting fish prices with an artificial neural network model during the tuna fraud

Agricultural price forecasting plays an important role in stabilising markets and ensuring food security. It provides insights for various stakeholders to optimise planting choices, allocate resources efficiently and mitigate potential risks. However, price forecasting during food safety incidents poses unique challenges. This study focused on a case of tuna fraud in Spain in 2017, which caused 105 people to fall ill and influenced consumer behaviour in fish purchases. To forecast fish prices during an incident of fraud, we used an artificial neural network model (ANN) based on the price of tuna and its substitutes, salmon and hake, as well as a communication index based on the number of posts regarding the tuna fraud from the social media platform X (formerly Twitter). ANN was compared with a threshold vector autoregressive model (TVAR), a classical time series econometric model that offers valuable insights into price dynamics. The results showed that, in the short term, TVAR offers a better price forecast for tuna and salmon, considering the impacts of the X platform. In the medium term, ANN outperformed TVAR. This study contributes to the ANN literature regarding agrifood price forecasting during food safety incidents.

Ethical Implications and Ramifications in the Case of Truong My Lan

This article examines the ethical and systemic failures surrounding the massive financial fraud orchestrated by Vietnamese real estate tycoon Truong My Lan. This study aims to analyze the intricate web of relationships, rights, duties, and potential responses to prevent such unethical practices in the future. Using a case study approach, the research critically evaluates the most appropriate responses and considers future preventive measures. The study draws on various scholarly sources and legal frameworks, including the Vietnamese Enterprise Law, the Labor Code, and international conventions such as the United Nations Convention Against Corruption. Findings suggest that strengthening regulatory oversight, enhancing corporate governance, and fostering a culture of transparency and accountability are crucial steps in addressing and preventing financial fraud. The research also highlights the importance of technological integration, public education, and international collaboration in building a resilient financial system. This study contributes to the broader discourse on business ethics and corporate governance by comprehensively analyzing one of Vietnam's most prominent fraud cases and proposing actionable measures to mitigate similar risks in the future.

Juridical Analysis Of The Role Of Investigators In The Perspective Of Combating Criminal Acts Of Fraud Through Check Securities

Fraud through cheque securities is a form of economic crime that harms many parties, both individuals and financial institutions. The background of this problem is the increasing number of fraud cases involving blank checks in Batam City, which causes significant financial losses and reduces public confidence in these payment instruments. This study aims to analyze the role of investigators in eradicating fraud through cheque securities, with a focus on the Barelang Police Criminal Investigation unit. This study uses a normative juridical method with a case study approach, involving analysis of primary and secondary data in the form of interviews with investigators, literature reviews, and document studies related to check fraud cases in the jurisdiction of the Barelang Police. The results of the study show that investigators have a crucial role in handling fraud cases through cheque securities, starting from receiving reports and complaints, collecting and securing evidence, to preparing case files for prosecution. However, there are several obstacles that reduce the effectiveness of investigations, such as lack of resources, lack of specialization in financial crimes, and limited access to information. To overcome these obstacles, this study provides several recommendations, including increasing the capacity and expertise of investigators through ongoing training, increasing budgets and technological resources, and strengthening cooperation between investigators and other financial institutions and law enforcement. The suggestions put forward in this study are that police investigators should improve internal and external coordination to speed up the investigation process, other law enforcers such as prosecutors and judges should increase collaboration with investigators to ensure a fair and transparent judicial process, and the public, especially victims of cheque fraud, should be educated on the steps to be taken when becoming victims of fraud. With the implementation of this recommendation, it is hoped that the handling of fraud cases through check securities can be more effective and public trust in the justice system can increase.

Women Support Women? How Investors Respond to High-Profile Female Fraud Case Exposure

Women Support Women? How Investors Respond to High-Profile Female Fraud Case Exposure

Optimizing credit card fraud detection: a deep learning approach to imbalanced datasets

Imbalanced datasets pose a significant challenge in credit card fraud detection, hindering the training effectiveness of models due to the scarcity of fraudulent cases. This study addresses the critical problem of data imbalance through an in-depth exploration of techniques, including cross-entropy loss minimization, weighted optimization, and synthetic minority oversampling technique-based resampling, coupled with deep neural networks (DNNs). The urgent need to combat class imbalances in credit card fraud datasets is underscored, emphasizing the creation of reliable detection models. The research method delves into the application of DNNs, strategically optimizing and resampling the dataset to enhance model performance. The study employs a dataset from October 2018, containing 284,807 transactions, with a mere 492 classified as fraudulent. Various resampling techniques, such as undersampling and SMOTE oversampling, are evaluated alongside weighted optimization. The results showcase the effectiveness of SMOTE oversampling, achieving an accuracy of 99.83% without any false negatives. The study concludes by advocating for flexible strategies, integrating cutting-edge machine learning methods, and developing adaptive defenses to safeguard against emerging financial risks in credit card fraud detection.

Sanksi Hukum Pidana Terhadap Pelaku dan Perlindungan Hukum bagi Korban Investasi Bodong

The problem that the author wants to look at is what are the criminal sanctions for perpetrators of fraudulent investments and what are the legal protections for victims of fraudulent investments according to Indonesian criminal law. The application of legal sanctions for perpetrators of online investment fraud has not been explicitly regulated in a special law which can provide criminal penalties for perpetrators but is regulated in the Criminal Code Article 372 with a maximum prison sentence of four years or a maximum fine of nine hundred. rupiah, and Article 378 with a maximum prison sentence of 4 years. This research focuses on criminal cases of investment fraud. The main weakness in the legal sector for victims of fraudulent investments is the problem of legal uncertainty. This legal vacuum can result in the effectiveness and optimization of protection for victims of fraudulent investments not being optimal. The fact is that there is still a legal gap that perpetrators of fraudulent investments often escape heavy sanctions. To provide valuable results, the research methodology in this article uses normative juridical analysis, which is one of the methods and techniques of social science analysis. In this regard, normative juridical research is only based on document research using secondary data consisting of statutory regulations, court decisions, legal theory and royalties. The purpose of this research is to provide a discussion regarding the implementation of legal actions in current cases. So, through this research, it can be seen that law according to its function can play a role in resolving several criminal cases in Indonesia, such as the fraudulent investments that the author discusses.

The Current Situation and Prevention of AI Telecom Fraud: Starting from Personal Information Protection

AI telecom fraud is an act of using artificial intelligence deep cooperation and analysis technology to create false information to lure and deceive individuals and illegal possession of money. With the rapid development of the Internet and AI, the number of AI telecom fraud cases has exploded, so how to prevent AI telecom fraud has become an important issue in society. AI telecom fraud has led to a series of problems, such as the illegal collection and malicious disclosure of personal information, criminals using AI to analyze personal information for fraud, and the industry does not adequately supervise the collection and use of personal information by companies. This article uses case analysis, normative analysis, and comparative research methods to analyze the above three problems and proposes the solution, including establishing a complete legal regulatory system for personal information protection and the prevention and control of AI telecom fraud, and strengthening industry supervision. It is hoped that by applying the above-mentioned solutions, the protection of personal information and the prevention and control of AI telecom fraud can be strengthened, and the development of AI telecom fraud prevention and control in China can be promoted.

THE EFFECT OF BEHAVIORAL CONTROL, ORGANIZATIONAL COMMITMENT, SELF KEFFICACY, AND MORAL INTENSITY ON WHISTLEBLOWING INTENTION

Village Credit Institutions (LPD) play a strategic role in helping micro and small businesses and rural communities. However, several problems such as cases of fraud still occur, so a whistleblowing system is important to detect and prevent unethical practices. This study attempts to gain insight into the influence of behavioral control, organizational commitment, self-efficacy, and moral intensity on whistleblowing intentions. Researchers looked at 75 employees from 9 LPDs in South Kuta District. Probability sampling is utilized in the sampling procedure with a simple random sampling technique, and data is gathered via a survey questionnaire. Data analysis using multiple linear regression shows that behavioral control, organizational commitment, self-efficacy, and moral intensity have a favorable impact on the intents of whistleblowers. Implications related to the theory of planned behavior show that whistleblowing intentions are influenced by the belief that reporting violations helps maintain the integrity and reputation of the organization. This research can be input for LPDs in South Kuta District.

Kompetensi Auditor Sebagai Faktor Pendukung Audit Investigasi

Fraud detection is urgent for many companies or agencies. Cases of fraud and irregularities are increasingly rampant in Indonesia. Cases of corruption, misappropriation of assets, abuse of authority, and fraudulent financial statements often occur in various government agencies and companies. This study aims to determine auditor competence as a supporter of investigative audits. An interpretive qualitative method with a phenomenological approach was used in this study. Data collection techniques were carried out through interviews and observation. Data analysis was carried out by compiling questions, recording interview results, and analyzing and organizing them. The results showed that auditor competence has an influence on investigative audits. Qualified forensic analysis skills, interview skills to obtain information, and critical thinking to solve every problem in the field. Auditor competence in terms of understanding and knowledge related to regulations and laws is very important because it is the basis or guideline for conducting investigative audits. Because auditor competence is an important factor in supporting investigative audits, competency development is very necessary. Training, seminars, or continuing education can be a form of development that can be implemented by auditors. Qualified competence will improve the quality of the audit.

The Impact of Incentives on Data Collection for Online Surveys: Social Media Recruitment Study.

The use of targeted advertisements on social media platforms (eg, Facebook and Instagram) has become increasingly popular for recruiting participants for online survey research. Many of these surveys offer monetary incentives for survey completion in the form of gift cards; however, little is known about whether the incentive amount impacts the cost, speed, and quality of data collection. This experiment addresses this gap in the literature by examining how different incentives in paid advertising campaigns on Instagram for completing a 10-minute online survey influence the response rate, recruitment advertising cost, data quality, and length of data collection. This experiment tested three incentive conditions using three Instagram campaigns that were each allocated a US $1400 budget to spend over a maximum of 4 days; ads targeted users aged 15-24 years in three nonadjacent designated market areas of similar size to avoid overlapping audiences. Four ad creatives were designed for each campaign; all ads featured the same images and text, but the incentive amount varied: no incentive, US $5 gift card, and US $15 gift card. All ads had a clickable link that directed users to an eligibility screener and a 10-minute online survey, if eligible. Each campaign ran for either the full allotted time (4 days) or until there were 150 total survey completes, prior to data quality checks for fraud. The US $15 incentive condition resulted in the quickest and cheapest data collection, requiring 17 hours and ad spending of US $338.64 to achieve 142 survey completes. The US $5 condition took more than twice as long (39 hours) and cost US $864.33 in ad spending to achieve 148 survey completes. The no-incentive condition ran for 60 hours, spending nearly the full budget (US $1398.23), and achieved only 24 survey completes. The US $15 and US $5 incentive conditions had similar levels of fraudulent respondents, whereas the no-incentive condition had no fraudulent respondents. The completion rate for the US $15 and US $5 incentive conditions were 93.4% (155/166) and 89.8% (149/166), respectively, while the completion rate for the no-incentive condition was 43.6% (24/55). Overall, we found that a higher incentive resulted in quicker data collection, less money spent on ads, and higher response rates, despite some fraudulent cases that had to be dropped from the sample. However, when considering the total incentive amounts in addition to the ad spending, a US $5 incentive appeared to be the most cost-effective data collection option. Other costs associated with running a campaign for a longer period should also be considered. A longer experiment is warranted to determine whether fraud varies over time across conditions.

Fraud and Performance of Listed Deposit Money Banks in Nigeria: Exploring the Combined Effects of Fraud Triangle and Fraud Diamond Theories

The rate of increase in fraud cases and the attendant decline in performance of the Deposit Money Banks listed in Nigeria demand urgent examination into the phenomena. In order to appreciate the probable nexus between fraud and performance of Deposit Money Bank in Nigeria, this scholarly work demonstrated the influence of fraud on the performance Deposit Money Bank in Nigeria. In doing this, the study used correlational and expo facto research design, utilizing secondary data pooled out from the Nigerian Deposit Insurance Commission (NDIC) and published financial reporting of the DMBs. The study targeted all the 29 DMBs (comprising of, 5 Merchant Banks and 2 Non-Interest Banks and 22 Commercial Banks) as at 2019 as published by NDIC for ten years period of (2010-2019). Multiple regression methods was deployed to calculate approximately the model of the work. Results from the study demonstrated that fraud triangle and diamond theories (measured by expected loss from loss from fraud, number of fraud cases and staff participation in deception scheme) have negative and significant effect on performance (measured by ROA) of DMBs in Nigeria. Flowing from the result, this scholarly work has suggested that DMBs have a duty to put in place effective and efficient internal control procedures and establishment of workable fraud unit or department that is saddled with the responsibility of detecting and scrutinizing operational activities that may possibly be prone to fraud. In this way, bank assets would be safe-guarded and, consequently enhanced performance.

IoT Privacy Risks Revealed.

A user's devices such as their phone and computer are constantly bombarded by IoT devices and associated applications seeking connection to the user's devices. These IoT devices may or may not seek explicit user consent, thus leaving the users completely unaware the IoT device is collecting, using, and/or sharing their personal data or, only marginal informed, if the user consented to the connecting IoT device but did not read the associated privacy policies. Privacy policies are intended to inform users of what personally identifiable information (PII) data will be collected about them and the policies about how those PII data will be used and shared. This paper presents novel tools and the underlying algorithms employed by the Personal Privacy Assistant app (UTCID PPA) developed by the University of Texas at Austin Center for Identity to inform users of IoT devices seeking to connect to their devices and to notify those users of potential privacy risks posed by the respective IoT device. The assessment of these privacy risks must deal with the uncertainty associated with sharing the user's personal data. If privacy risk (R) equals the consequences (C) of an incident (i.e., personal data exposure) multiplied by the probability (P) of those consequences occurring (C × P), then efforts to control risks must seek to reduce the possible consequences of an incident as well as reduce the uncertainty of the incident and its consequences occurring. This research classifies risk according to two parameters: expected value of the incident's consequences and uncertainty (entropy) of those consequences. This research calculates the entropy of the privacy incident consequences by evaluating: (1) the data sharing policies governing the IoT resource and (2) the type of personal data exposed. The data sharing policies of an IoT resource are scored by the UTCID PrivacyCheck™, which uses machine learning to read and score the IoT resource privacy policies against metrics set forth by best practices and international regulations. The UTCID Identity Ecosystem uses empirical identity theft and fraud cases to assess the entropy of privacy incident consequences involving a specific type of personal data, such as name, address, Social Security number, fingerprint, and user location. By understanding the entropy of a privacy incident posed by a given IoT resource seeking to connect to a user's device, UTCID PPA offers actionable recommendations enhancing the user's control over IoT connections, interactions, their personal data, and, ultimately, user-centric privacy control.

Fraud Detection of the Electricity Consumption by combining Deep Learning and Statistical Methods

An important issue for the electricity distribution companies is the non-technical loss (NTL), also known as electricity fraud. This issue has a significant impact on the economies of all countries in the world. In this context, we studied the problem of the imbalance between the electrical energy invoiced and the electrical energy supplied within the Algerian economic sector. This article presents an approach to detecting electrical fraud using a combination of Long Short-Term Memory (LSTM) and robust Exponential and Holt-Winters Smoothing (EHWS) methods in order to enhance the accuracy and efficacy of fraud detection mechanisms. The proposed approach investigates the fraudulent behaviour of electricity consumers and unfolds in several key phases. In the first step, monthly consumption forecasts are made and the model with the most accurate results is selected. Then, the phase of detection of anomalies in economic meters and detection of cases of fraud by economic customers begins. This phase relies on the robust exponential and Holt-Winters Smoothing methods for uncovering irregular patterns indicative of potential fraud instances. The proposed model was trained and evaluated, and several experiments were carried out using a large dataset of real users from the economic sector. The dataset comprised approximately 2,000 customers and encompassed 14 years of monthly electricity usage in Bejaia, Algeria. The results of the experiments demonstrate promising performance, underscoring the efficacy of our proposed solution in effectively detecting instances of fraud, leading us to conclude that this proposition is robust and can help improve the accuracy of locating abnormal consumer behaviour and increase the company's profits.

Reconstruction of the role internal auditors in preventing fraud in the regional government: case study inspectorate of Malang City

This study aims to explain the practice of fraud prevention by internal auditors of the local government of Malang City. The research method used is critical ethnomethodology in the local government environment of Malang City. Seven informants served as key informants. The results showed that the internal auditors of the local government of Malang City have not been able to carry out their role optimally, due to the weak legitimacy of internal auditors, community involvement in fraud prevention programs, and partial fraud prevention activities. As a result, the research findings provide an overview of the regional inspectorate's institutional integration with elements authorized to handle fraud cases, such as BPKP and BPK. The findings of this study suggest that the Malang City Inspectorate should gain legitimacy through regulations, government support, and effective and efficient consultation to identify and prevent fraud.

Hybrid Features for the Identification and Categorization of Skin Cancer

The halal food control system is one of the critical aspects that need attention in sustaining the Malaysian halal industry. In practice, the existing halal food control system involves the roles of several stakeholders and is implemented independently according to their respective jurisdictions. No integrated control mechanism coordinates the roles of all stakeholders, thus resulting in fraud cases to easily happen in the industry. To effectively coordinate these roles, an integrated halal food control system must be developed. Therefore, this concept paper aims to discuss the roles of stakeholders involved in the halal food control system and to propose a conceptual halal food stakeholder control system model based on the objectives of the Islamic law framework. Information for this paper is gathered based on a literature review of previous studies related to the halal food control system. The conceptual model is expected to be a reference and guideline for all stakeholders involved and will serve as a theoretical foundation for future research in this area. Additionally, it can enhance consumer trust in Malaysia’s halal certification and support the country’s goal of becoming a leader in the global halal food industry.

The Role of Islamic Work Ethics in Moderating Influence Love of Money and Machiavellian Nature to Fraud Accounting in Management of Village Funds

The number of fraud cases in Indonesia has been increasing annually, and it has started to spread to the countryside. This turns into an irony as the hamlet continues to grow and evolve into a sophisticated community. The goal is to investigate how a love of money and other Machiavellian traits affect the tendency for fraud accounting when using village funds, as well as how the Islamic work ethic influences the interaction between the village apparatus and the community in Wonokerto District. The SEM-PLS technique is being used in this quantitative study. Primary data were employed, and 66 persons made up the sample size through the use of purposive sampling procedures in conjunction with the questionnaire method of data collecting. According to the findings, the inclination of accounting fraud was significantly influenced by both love of money and Machiavellian tendencies, and the Islamic work ethic was found to be ineffective in mitigating this effect.

A Hybrid Neural Network-Based Fast Financial Fraud Detection Model

With the increasing number of financial transactions, financial fraud has become increasingly serious for financial institutions and the public. The core idea of this model is to integrate multiple neural network structures and utilize their respective advantages to improve the performance of fraud detection. Firstly, we employed the convolutional neural network with interpretable blocks (CNNIB) convolutional neural network (CNN) to extract key features from the data to capture patterns and patterns in fraud cases. Secondly, we introduced the autoencoder generative adversarial network (AE-GAN) adversarial network to perform feature analysis on sequence data to capture temporal features in transaction sequences. Finally, we used differential detection for classification to determine whether transactions were fraudulent. An independent detection module was established to accelerate the recognition of financial fraud, and parameter indicators were optimized. Finally, a hybrid neural network model was established. The experimental results indicate that our model has achieved significant results in quickly detecting financial fraud; compared with traditional single neural network models, hybrid neural network models have significant improvements in accuracy and efficiency. In addition, we conducted in-depth analysis of the model and revealed its performance stability under different training set sizes and data distributions. Our research findings provide an effective tool for financial institutions to quickly identify financial fraud.

Potential COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches.

Some COVID-19 testing centres have reported manipulated test numbers for antigen tests/rapid tests. This study compares statistical approaches with traditional fraud detection methods. The extent of agreement between traditional and statistical methods was analysed, as well as the extent to which statistical approaches can identify additional cases of potential fraud. Outlier detection marking a high number of tests, modeling of the positivity rate (Poisson Regression), deviation from distributional assumptions regarding the first digit (Benford's Law) and the last digit of the number of reported tests. The basis of the analyses were billing data (April 2021 to August 2022) from 907 testing centres in a German city. The positive agreement between the conventional and statistical approaches ('sensitivity') was between 8.6% and 24.7%, the negative agreement ('specificity') was between 91.3% and 94.6%. The proportion of potentially fraudulent testing centres additionally identified by statistical approaches was between 7.0% and 8.7%. The combination of at least two statistical methods resulted in an optimal detection rate of test centres with previously undetected initial suspicion. The statistical approaches were more effective and systematic in identifying potentially fraudulent testing centres than the conventional methods. Testing centres should be urged to map paradata (e.g. timestamps of testing) in future pandemics.