Potential Fraud Research Articles

The Effect of Good Governance on Preventing Potential Fraud in the Management of BOK Funds at Puskesmas in Mojokerto District

The Effect of Good Governance on Preventing Potential Fraud in the Management of BOK Funds at Puskesmas in Mojokerto District

Akuntabilitas Berbasis Religiosity Value: Berperakah Mencegah Fraud Accounting?

This research aims to carefully investigate the concept of accountability in the context of preventing and addressing potential fraud within the structure of student organizations. The research method employed in this study is qualitative phenomenology, which aims to provide a deeper and richer understanding of how accountability operates within an organizational environment. Additionally, the qualitative phenomenological approach is used to uncover the meaning of experiences directly encountered by individuals. Phenomenology is chosen to gather information regarding fraud occurring within student organizations and reveal the meaning of accountability from the perspective of students' religiosity. This research utilizes primary data obtained from in-depth interviews with subjects of the study. The informants used in this research are the presidents of universities in Pontianak. Based on the research findings, it can be concluded that: 1) Transparency and communication among organization leaders are crucial for preventing miscommunication, strengthening the organizational environment, and reducing the potential for accounting fraud by closing information gaps. 2) The accountability attitude of organization leaders, especially through the production of accurate financial reports, can detect and address accounting fraud. 3) Religion plays a significant role in shaping individual morality and influencing the implementation of accountability. 4) The implementation of accountability in organizations involves morality driven by religious teachings. 5) Religious teachings prohibit fraudulent actions, thus the more devout one is to their religion, the less likely they are to engage in accounting fraud.

PROTECTION OF MINORITY SHAREHOLDERS FROM FRAUD IN LIMITED LIABILITY COMPANY DIVIDEND DISTRIBUTION

<p>This research investigates the potential fraudulent actions carried out by majority shareholders and other corporate organs in the context of stock distribution. The primary focus is on the continuity of practices that may disadvantage minority shareholders and undermine the legal protections they should receive. Majority shareholders, often wielding significant influence in the General Meeting of Shareholders (RUPS), may exploit their power to manipulate decisions related to stock distribution. The presence of a quorum that can be achieved without the participation of minority shareholders increases the risk of fraud in this process. The aim of this research is to identify various fraudulent actions that may occur in stock distribution by majority shareholders and related corporate organs. Using a legal analysis approach and case studies, this research also explores ways in which minority shareholders might prevent or respond to fraudulent actions. Furthermore, the research examines the extent to which Law No. 40 of 2007 concerning Limited Liability Companies (UU PT) provides a legal basis to prevent and respond to fraudulent actions in stock distribution. Practical implications of potential fraud are analyzed to provide recommendations that can strengthen legal protections for minority shareholders. This research is expected to contribute to a deeper understanding of the dynamics of fraud in stock distribution, outline the legal challenges faced by minority shareholders, and formulate concrete steps to prevent and respond to such fraud in companies.<em></em></p>

The Evaluating Impact of Artificial Intelligence on Risk Management and Fraud Detection in the Commercial Bank in Bangladesh

The integration of Artificial Intelligence (AI) in the banking sector represents a significant leap forward in the realms of risk management and fraud detection. This paper explores the transformative effects of AI in these areas, emphasizing both the improvements and the challenges brought about by its implementation. In risk management, AI's influence is diverse and profound. Advanced algorithms allow for the creation of more sophisticated credit risk assessment models by detecting subtle patterns in large datasets that might be overlooked by human analysts. This capability enhances the accuracy of credit risk evaluations. Additionally, real-time monitoring of transactions helps in the immediate mitigation of risks, which is particularly crucial when dealing with market and liquidity risks. AI also significantly aids in automating compliance with regulatory requirements, reducing the likelihood of human errors and enabling quicker adaptation to changes in regulations. Operational risks are also minimized through AI's ability to automate routine tasks and strengthen cybersecurity measures. AI systems are adept at identifying anomalies that may indicate fraud by scrutinizing transaction data and customer behavior. The predictive capabilities of AI enable banks to anticipate and prevent potential fraud schemes. Moreover, AI systems can adapt and evolve in response to changing tactics used by fraudsters, maintaining their effectiveness over time. AI enhances customer authentication processes through the use of advanced technologies such as biometric verification, providing an additional layer of security. However, the implementation of AI in banking raises significant concerns regarding data privacy and security due to the sensitive nature of banking information. Furthermore, AI models can inherently carry biases that lead to discriminatory outcomes, necessitating ongoing monitoring and adjustments to these models. The complexity and lack of transparency in AI systems also pose challenges, particularly when AI-driven decisions have significant impacts on customers. The evolving regulatory frameworks for AI in banking present another layer of complexity, as banks must continuously adapt to comply with new and changing regulations. This paper highlights the need for a balanced approach to leveraging AI's potential in banking, addressing both its transformative benefits and the ethical and regulatory challenges involved. By doing so, banks can harness AI to enhance their operations while ensuring fairness, transparency, and compliance with regulatory standards.

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.

PATIENT CARE AND FINANCIAL INTEGRITY IN HEALTHCARE BILLING THROUGH ADVANCED FRAUD DETECTION SYSTEMS

Healthcare fraud poses a significant challenge, leading to substantial financial losses and compromising the quality of patient care. This study assesses the efficacy of advanced fraud detection systems, including data analytics, machine learning, predictive modeling, and natural language processing (NLP), in enhancing the detection and prevention of fraudulent activities in healthcare. By leveraging these technologies, healthcare organizations can process large volumes of complex data, adapt to evolving fraud patterns, and provide real-time monitoring. The findings indicate that data analytics effectively uncovers hidden patterns and anomalies, while machine learning and AI improve predictive accuracy by continuously learning from historical data. Predictive modeling enables proactive fraud prevention by forecasting potential fraud scenarios, and NLP extends detection capabilities to unstructured data such as clinical notes. The integration of these advanced technologies has resulted in significant financial savings and improved patient care, as demonstrated by case studies highlighting substantial reductions in fraudulent claims. The study concludes that adopting advanced fraud detection systems is essential for maintaining financial integrity and ensuring high-quality patient care in the evolving healthcare landscape.

Evaluating the impact of hexagon fraud on potential financial statement fraud with audit quality as a moderator

Evaluating the impact of hexagon fraud on potential financial statement fraud with audit quality as a moderator

DISMANTLING THE MULTI-LEVEL MARKETING SCHEME OF HERBAL MEDICINES: BETWEEN CONVENTIONAL REGULATIONS AND SHARIA PRINCIPLES

The practice of Multi-Level Marketing (MLM) of herbal medicines is increasingly rife in Indonesia and there is the potential for fraud that often occurs in this practice. This study aims to dismantle the MLM scheme of herbal medicine from the point of view of conventional regulation and sharia principles. The methodology used includes literature study, legal analysis, and interviews with experts. The results revealed that there is an herbal medicine MLM scheme that violates conventional regulations and sharia principles. The conclusion of this study is the importance of strict supervision of herbal medicine MLM and the need to educate the public about legal and sharia MLM schemes. The practice of Multi-Level Marketing (MLM) of herbal medicines is increasingly widespread in Indonesia. MLM herbal medicine is a marketing system where members get a commission from selling products and recruiting new members. This shows the need for research to dismantle herbal medicine MLM schemes and uncover potential frauds that occur. In addition, there are also sales of illegal products that do not have official permission from the Food and Drug Supervisory Agency (BPOM). Pyramid schemes are also one of the findings of this study, where many herbal medicine MLM players jointly profit by recruiting new members without making substantial product sales. These findings point to the need for close supervision of herbal medicine MLM practices to protect consumers and prevent potential violations of sharia laws and principles. After conducting this research, it can be concluded that herbal medicine MLM needs to get close supervision. Research findings show violations of conventional regulations and sharia principles in herbal medicine MLM practices. Therefore, it is necessary to educate the public about legal and sharia MLM schemes so that they can understand the rights and obligations as smart consumers. In addition, there is also a need for increased regulations to monitor the practice of herbal medicine MLM in order to protect consumers and maintain the integrity of the MLM business in Indonesia.

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.

Advancing fraud detection in New Zealand Mānuka honey: Integrating hyperspectral imaging and GANomaly-based one-class classification

New Zealand Mānuka honey has become a prime target for adulteration due to its high commercial value. Given the diverse possibilities of fraudulent activities, training a supervised model that exhaustively covers all potential fraud scenarios is challenging. This study presents a new method for detecting fraudulent behavior in New Zealand Mānuka honey by combining hyperspectral imaging (HSI) with the GANomaly-based One-Class Classification method. We collected 18 different UMF-graded pure Mānuka honey samples from five New Zealand brands, which were used for training. The model was tested on fraudulent honey, including aged and syrup-adulterated honey, and compared with the traditional One-Class Classification methods. The results demonstrate that the HSI combined with the GANomaly method achieved 100% discrimination for all test samples, outperforming the standard rival techniques. In conclusion, this research developed a versatile model capable of detecting honey fraudulent behavior, showing significant practical implications for honey quality assessment.

A Critical Analysis of Machine Learning Techniques for Online Transaction Security

The widespread adoption of Unified Payments Interface (UPI) as the preferred digital transaction method has led to a sharp increase in fraudulent activities, presenting a considerable challenge to the security and reliability of online payments.Top of Form To address this pressing concern, our project focuses on developing a sophisticated fraud detection system tailored specifically for UPI transactions. We utilize Convolutional Neural Networks (CNNs), an advanced deep learning architecture, to intricately capture the sequence of operations involved in UPI transaction processing. Through extensive data collection and preprocessing, we curate a diverse dataset comprising various transaction types, amounts, and temporal patterns, ensuring comprehensive coverage of transaction behaviour. The CNN is meticulously trained on this dataset, leveraging its innate ability to discern subtle patterns and dependencies within transaction sequences. Notably, our approach emphasizes the importance of minimizing false positives to prevent genuine transactions from being erroneously flagged as fraudulent. In real-time, incoming UPI transactions are subjected to scrutiny by the trained CNN, which evaluates their conformity to learned normal behaviour patterns. Transactions exhibiting deviations indicative of potential fraud are promptly flagged for further investigation, thus enabling proactive mitigation of fraudulent activities. Most importantly, our study contains a comprehensive comparative analysis that compares the efficacy of conventional machine learning methods such as Support Vector Machines (SVM), Random Forests, and Logistic Regression with CNN-based fraud detection. Through meticulous evaluation, we demonstrate the superior efficacy of CNNs in detecting fraudulent UPI transactions, owing to their inherent capacity to capture intricate spatial and temporal dependencies. Our findings underscore the transformative potential of deep learning techniques in bolstering the security and resilience of digital payment ecosystems, thereby safeguarding users and fostering trust in online transactions.

Empowering generation z accountants in the era of data complexity and open innovation: Nurturing big data analytics, diagnostic, and forensic accounting skills

This research fills a significant knowledge gap by exploring the development of Big Data Analytics Skills (BAS), Diagnostic Skills (DS), and Forensic Accounting Skills (FAS) among Generation Z accounting students in Thailand. Employing a quantitative research approach, the study conducted an online survey of fourth-year accounting students from this generation, utilizing the convenience sampling technique. The investigation aimed to assess the impact of BAS and DS on FAS within the accounting student cohort. The sample consisted of 465 participants. Path analysis conducted using the SmartPLS program affirmed significant relationships: BAS significantly influences both DS (H1, β=0.817, p<0.001) and FAS (H2, β=0.391, p<0.001), while DS notably impacts FAS (H3, β=0.484, p<0.001), concurrently serving as a mediator between BAS and FAS (H4, β=0.395, p<0.001). These findings suggest the imperative need for curriculum development and tailored training programs aimed at enriching these skills among emerging accountants. Such skill enhancement is vital to equip them to grapple with the challenges inherent in the era of big data and to bolster their competence in the detection and prevention of financial fraud and deceptive practices. The study not only holds significance for the development of skills essential for future accountants but also makes substantial contributions towards enhancing transparency and accountability within the accounting and finance sector. Furthermore, it opens avenues for potential improvements in educational and training curricula in the field of accounting, ensuring alignment with the demands of the big data era and better preparing emerging accountants to effectively address the specter of potential fraud.

PENGARUH KOMPETENSI APARATUR,PARTISIPASI MASYARAKAT, DAN PENGENDALIAN INTERNAL TERHADAP AKUNTABILITAS PENGELOLAAN DANA DESA (Studi Kasus Pada Kecamatan Waru, Kabupaten Sidoarjo)

The aim of this research is to test, prove and analyze the influence of apparatus competency, community participation and internal control on accountability in village fund management. This research uses primary data in the form of distributed questionnaire data. The sample used was 100 respondents in Waru District, random sampling method. The analysis used in this research is partial least squares. Based on the research results, it can be concluded that the apparatus competency variable has no effect on the accountability of village fund management and the community participation variable. Meanwhile, the internal control variable influences the accountability of village fund management. The implications and suggestions are that strong internal controls can help prevent potential fraud and misuse of funds. This can create an environment free from unethical and harmful practices. With increased accountability, active community participation, and transparent use of funds, it is hoped that community trust in local government and the village fund management process will increase.

Literature Review: the Relationship Between Internal Control System of the Government (SPIP), Good Government Governance (GGG), And Fraud Prevention

The aim of this article is to clarify the connection between the government's internal control system, good government governance, and endeavors to thwart fraud, alongside the influencing factors. The methodology utilized encompasses a review of literature utilizing data drawn from international and national journals, with a total of 23 journals as the sample. From the analysis, it was determined that good government governance serves as an intermediary between the internal control system of government and the action of fraud prevention. In simpler terms, the more robust the internal control, the more adept it is at fostering effective and high-quality good governance, ultimately bolstering efforts to prevent fraud. Elements that must be taken into account for effective fraud prevention encompass: (1) Enhancing internal government supervision involves documenting and reinforcing the organization's dedication to the relevant code of ethics and conduct; (2) Ensuring a distinct division of roles and responsibilities; (3) Interpreting regulations comprehensively and ensuring consistent, unambiguous, and clear meanings; (4) It is essential to have qualified personnel matching their fields and expertise, whether through recruitment or competency development like education and training; (5) Utilizing information technology; (6) Conducting effective risk assessment and monitoring is crucial for overseeing organizational and identifying potential fraud risks.

LESI HERTATI EXPLORING HUMAN CAPITAL AT THE EDUCATION LEVEL THE ROLE OF ACCOUNTING INFORMATION SYSTEM APPLICATIONS IN OVERCOMING FRAUD DETECTION IN THE SHOPEE APPLICATION

This research aims to explore the importance of human capital such as level of education, understanding and expertise in order to detect fraud in accounting information system software in order to detect fraud. The success of an accounting information system in detecting and preventing fraud does not only depend on technology, but also on the quality of human resources involved in the use, management and development of the system. This study uses descriptive and verification analysis of the role and contribution of human capital, such as knowledge, skills and attitudes, in increasing users of accounting information systems related to fraud detection in the Shopee application. Data was collected through interviews with Indo Global Mandiri University students who use Shopee. The analytical tool used in this research is SEM PLS. The research results prove that understanding of finance, knowledge of information technology, and high analytical skills of human resources can detect fraud in the Shopee application. The research results prove that human capital can be the key to success in increasing the level of education related to accounting information systems that focus on fraud detection in the Shopee application. The role of effective human capital is to increase resilience to potential fraud risks in an increasingly complex business environment.

CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

The project "Credit Card Fraud Detection with FBA" aims to develop a robust and efficient system for identifying and preventing fraudulent activities associated with credit card transactions within the context of Fulfilment by Amazon (FBA). With the increasing prevalence of online transactions, credit card fraud has become a significant concern for both consumers and businesses. FBA, being a prominent e-commerce platform, requires a sophisticated fraud detection mechanism to ensure secure and trustworthy transactions. The project utilizes advanced machine learning algorithms to analyse transaction data and detect patterns indicative of potential fraud. These algorithms are trained on historical data, enhancing their ability to recognize anomalies in real-time transactions. The system incorporates a multi- layered approach, utilizing address verification, device fingerprinting, and IP geolocation to validate the legitimacy of transactions and minimize false positives. Real-time monitoring is a key component of the system, allowing for immediate detection and response to suspicious activities. Manual review processes are integrated, involving trained personnel to investigate flagged transactions and make informed decisions based on additional information or customer verification. This human element adds an extra layer of scrutiny, contributing to the reliability of the fraud detection system. The project aims to create a dynamic and adaptive fraud detection system that evolves wit emerging fraud tactics. Regular updates and improvements will be implemented to address new challenges and enhance the overall security of credit card transactions within the FBA ecosystem. The successful implementation of this project will contribute to a safer and more secure online shopping experience for FBA users, fostering trust and confidence in the platform. Index terms: Credit card fraud detection, Fulfilment by Amazon (FBA), Machine learning algorithms, Fraudulent activities, Online transactions, Real-time monitoring, Multi-layered approach, Address verification, Device fingerprinting, IP geolocation, Manual review processes, Dynamic and adaptive system, Emerging fraud tactics, Security enhancements, Online shopping experience, Trust and confidence building

Health insurance fraud detection based on multi-channel heterogeneous graph structure learning

Health insurance fraud is becoming more common and impacting the fairness and sustainability of the health insurance system. Traditional health insurance fraud detection primarily relies on recognizing established data patterns. However, with the ever-expanding and complex nature of health insurance data, it is difficult for these traditional methods to effectively capture evolving fraudulent activity and tactics and keep pace with the constant improvements and innovations of fraudsters. As a result, there is an urgent need for more accurate and flexible analytics to detect potential fraud. To address this, the Multi-channel Heterogeneous Graph Structured Learning-based health insurance fraud detection method (MHGSL) was proposed. MHGSL constructs a graph of health insurance data from various entities, such as patients, departments, and medicines, and employs graph structure learning to extract topological structure, features, and semantic information to construct multiple graphs that reflect the diversity and complexity of the data. We utilize deep learning methods such as heterogeneous graph neural networks and graph convolutional neural networks to combine multi-channel information transfer and feature fusion to detect anomalies in health insurance data. The results of extensive experiments on real health insurance data demonstrate that MHGSL achieves a high level of accuracy in detecting potential fraud, which is better than existing methods, and is able to quickly and accurately identify patients with fraudulent behaviors to avoid loss of health insurance funds. Experiments have shown that multi-channel heterogeneous graph structure learning in MHGSL can be very helpful for health insurance fraud detection. It provides a promising solution for detecting health insurance fraud and improving the fairness and sustainability of the health insurance system. Subsequent research on fraud detection methods can consider semantic information between patients and different types of entities.

The Role of Auditors in Fraud Prevention and Detection in Companies

The role of auditors in fraud prevention and detection in companies is a critical aspect of corporate governance and financial integrity. Auditors, often external to the company, are responsible for reviewing financial records, internal controls, and operational processes to ensure accuracy, transparency, and compliance with regulatory standards. Fraud poses a significant threat to businesses, ranging from financial misstatements to cyber fraud. Auditors play a crucial role in identifying and preventing such fraudulent activities by applying their expertise in accounting principles, risk assessment, and forensic analysis. In their role, auditors are tasked with assessing a company's financial statements and internal controls to detect any signs of fraud or mismanagement. They conduct thorough examinations of financial records and transactions, looking for irregularities or inconsistencies that may indicate potential fraud. Auditors also evaluate the effectiveness of internal controls designed to prevent and detect fraud, assessing their design and implementation. Moreover, auditors often employ specialized techniques like data analytics and forensic accounting to identify patterns of suspicious activities or potential fraud schemes. By analyzing large datasets and transaction records, auditors can uncover anomalies that might otherwise go unnoticed. In addition to detecting fraud, auditors also contribute to its prevention by recommending improvements to internal controls and processes. They provide valuable insights and recommendations to management and stakeholders on how to strengthen governance practices and reduce the risk of fraud occurring within the organization. Overall, auditors play a critical role in safeguarding the integrity of financial reporting and maintaining trust in corporate operations. Their expertise and independence are essential in ensuring transparency and accountability in the face of evolving fraud risks and regulatory challenges.

Constructing a Fraud Risk Early Warning Model for Chinese Listed Companies Driven by Heterogeneous Data

In this paper, we propose a model for early warning of fraud risk among Chinese listed companies, leveraging the capabilities of heterogeneous data analysis. Given the complexity and multidimensionality of corporate fraud, our model integrates multiple data sources including financial reports, market behavior, and the sentiment of annual report texts, representing diverse sets of heterogeneous data. This approach employs advanced data processing techniques to handle and amalgamate heterogeneous data, ensuring robustness and accuracy. Utilizing machine learning algorithms, the model not only detects potential fraud signals but also quantifies the level of risk, providing stakeholders with a dynamic predictive tool. This research offers a comprehensive data-driven approach to fraud detection in the corporate sector, underscoring the importance of meticulous risk assessment using various data streams. It marks a critical step in proactive fraud management in an increasingly complex financial environment.

Real Time Credit Card Fraud Detection and Reporting System Using Machine Learning

This study addresses the critical issue of real-time credit card fraud detection using machine learning. The primary goal is to develop a model that promptly identifies fraudulent transactions and alerts users. Two algorithms—Random Forest and Decision Tree Classifier were used, alongside various sampling techniques to balance the dataset and enhance performance. Six models were created, each with different accuracy levels in fraud detection. Key findings include a higher incidence of fraud among individuals over 75 years, likely due to less familiarity with modern transaction methods. Additionally, a majority of transactions involved females, indicating a potential higher fraud risk in these transactions. The Random Forest -SMOTE [Hyperparameter Tuned] model was the most effective, achieving a 97% accuracy rate, 95% F1 score, and 98% precision rate. For practical application, this model was integrated with Twilio for real-time fraud alerts, proving successful in sending timely, accurate notifications. The study highlights valuable insights and a robust solution for real-time fraud detection and response. Regular performance evaluations of the model are recommended to maintain its effectiveness against evolving fraud patterns.