Potential Fraud Research Articles

SPECIALLY PROTECTED ENVIRONMENTAL AREAS X REGISTRATION IN REAL ESTATE OFFICE

Areas with special environmental protection designations have legal regulations to ensure biodiversity conservation and sustainable resource management. Among these areas are the Private Natural Heritage Reserves (RPPNs), Legal Reserves (RLs), and Environmental Easement Areas (ASAs). This study focuses on the importance of recording these zones in property registries at the Deed Offices. This practice centralizes crucial information about the property, including judicial and administrative aspects and obligations associated with the property, ensuring transparency and information for potential buyers and interested third parties. The employed methodology involved documentary research and systematic data analysis. It emphasizes the importance of intensifying the adoption of the concentration principle in property registration, through recording, to provide greater legal security, visibility, protection of areas, and to mitigate potential fraud. This study serves as a guide for registry professionals and the general public on the legal significance of recording in protected areas.

The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

Background: Fraud in financial transaction is at the root of corruption issues recorded in organization. Detecting fraud practices has become increasingly complex and challenging. As a result, auditors require precise analytical tools for fraud detection. Grouping financial transaction data using K-Means Clustering algorithm can enhance the efficiency of applying Benford Law for optimal fraud detection. Objective: This study aimed to introduce Multiple Benford Law Model for the analysis of data to show potential concealed fraud in the audited organization financial transaction. The data was categorized into low, medium, and high transaction values using K-Means Clustering algorithm. Subsequently, it was reanalyzed through Multiple Benford Law Model in a specialized fraud analysis tool. Methods: In this study, the experimental procedures of Multiple Benford Law Model designed for public sector organizations were applied. The analysis of suspected fraud generated by the toolkit was compared with the actual conditions reported in audit report. The financial transaction dataset was prepared and grouped into three distinct clusters using the Euclidean distance equation. Data in these clusters was analyzed using Benford Law, comparing the frequency of the first digit’s occurrence to the expected frequency based on Benford Law. Significant deviations exceeding ±5% were considered potential areas for further scrutiny in audit. Furthermore, the analysis were validated by cross-referencing the result with the findings presented in the authorized audit organization report. Results: Multiple Benford Law Model developed was incorporated into an audit toolkit to automated calculations based on Benford Law. Furthermore, the datasets were categorized using K-Means Clustering algorithm into three clusters representing low, medium, and high-value transaction data. Results from the application of Benford Law showed a 40.00% potential for fraud detection. However, when using Multiple Benford Law Model and dividing the data into three clusters, fraud detection accuracy increased to 93.33%. The comparative results in audit report indicated a 75.00% consistency with the actual events or facts discovered. Conclusion: The use of Multiple Benford Law Model in audit toolkit substantially improved the accuracy of detecting potential fraud in financial transaction. Validation through audit report showed the conformity between the identified fraud practices and the detected financial transaction. Keywords: Fraud Detection, Benford’s Law, K-Means Clustering, Audit Toolkit, Fraudulent Practices.

Real Time Face Recognition for Mobile Application Based on Mobilenetv2

Real-time facial recognition is one of the technologies with significant applications in a variety of contexts, including supporting the process of employee attendance. Attendance is a crucial aspect of company administration that influences productivity and operational effectiveness. Traditional attendance mechanisms are susceptible to fraud and errors, so businesses must adopt digital attendance solutions. Face recognition with landmark-based anti-spoofing using MobileNetV2 on mobile devices is intended to be an innovative solution for attendance management. This system employs CNN with MobileNetV2 architecture to detect and identify employee faces in real time. MobileNetV2 is advantageous because it makes efficient use of mobile device resources without sacrificing precision. The research results demonstrate the extraction of eye and lip landmark points with the Blazeface model integrated in the Dart programming language using the Flutter framework. The implementation of the mobile system yields an application called FaceON that can aid in the prevention of potential fraud by employing anti-spoofing techniques. Before a visage can be verified, users must overcome obstacles such as winks and smiles. The contribution of this study is that this system is a dependable and innovative solution for employee attendance management in the digital age

Prediction of automobile insurance fraud claims using machine learning

Automobile insurance fraud is a significant issue for insurance firms, causing financial losses and higher premiums for policyholders. This study aims to create a predictive model for accurately identifying potential vehicle insurance fraud claims. Understanding fraud detection processes and operationalizing information communication technology is crucial for implementing corrective actions, but personally reviewing insurance claims is time-consuming and costly. This study explored machine learning algorithms to detect fraudulent vehicle insurance claims. The research evaluated AdaBoost, XGboostNB, SVM, LR, DT, ANN, and RF. AdaBoost and XGBoost classifiers outperformed other models with 84.5% classification accuracy, while LR classifiers performed poorly with balanced and unbalanced data. The ANN classifier performed better with unbalanced data. Performance evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of the models. The results demonstrate the effectiveness of machine learning in distinguishing between genuine and fraudulent claims, providing insurance companies with a powerful tool to proactively combat fraud and improve their overall risk management strategies. The findings of this research contribute to the insurance industry’s efforts to enhance fraud detection systems, reduce financial losses, and offer more competitive insurance premiums to honest policyholders.

Assessing the Effectiveness of Fraud Detection Models

Our research seeks to determine whether the detection of potential fraud in China-listed U.S. companies could be measured by the U.S. M-Score and F-Score models. Then, we group the data for 18 pairs of companies to make a t-test defined as the average difference between firms' M-scores or F-scores before and after the fraud. Moreover, more detailed information about these companies, financial fraud methods and motives, and ways to prevent financial fraud. Through our preliminary research, these two U.S. detection models can be used to detect financial fraud for Chinese-listed U.S. companies in statistical significance. With this premise, we also do an additional t-test to prove the standing adverse effects of committing fraud on firms.

Potential fraud and its’ prevention in the implementation of national health insurance at Dadi Regional Hospita

The National Health Insurance program started running in Indonesia on January 1, 2014. Along with the increasing number of National Health Insurance participants and health facilities, more criticism has emerged from various parties, including from National Health Insurance providers regarding the alleged fraud. Fraud can have a negative effect on finances, the standard of medical care, and the perpetrator's reputation. This study's goal was to assess the likelihood that fraud may occur at Dadi Regional Hospital in Makassar, Indonesia. The study also explored the procedures and policies in place for preventing the fraud. This qualitative study employed phenomenological design. The results of the study show that there is a fraud prevention system at Dadi Regional Hospital in the form of policy and guideline formulation, a culture of prevention, implementation of quality control, and cost control. Even though they already have a prevention system in place, there is still some lack of information being disseminated about the fraud prevention team established. There are still frequent discrepancies between the diagnosis code input in the National Health Insurance system and the patient's diagnosis Hence, the Internal Monitoring Unit is still in the process of establishing a fraud prevention system

Fraud Pentagon dan Kecurangan Laporan Keuangan: Potret pada Perusahaan Sektor Kesehatan di Indonesia

Financial statement fraud is an intentional misstatement related to the reporting of company conditions to obscure financial statement users in making decisions. This research was conducted to examine the influence of the five pentagon fraud elements namely pressure, opportunity, rationalization, ability, and arrogance on fraudulent financial statements. This research uses the Beneish M-Score Model to detect potential fraud in financial reports. The data used in this study is secondary data derived from the annual financial reports of health sector companies listed on the Indonesia Stock Exchange during the 2016-2020 period. The sampling method used a purposive sampling method and the total research sample was 13 companies. Hypothesis testing was carried out using logistic regression analysis with SPSS 26.0 software statistical tools. The research results show that pressure has an influence on financial report fraud. Meanwhile, opportunity, rationalization, ability and arrogance have no effect on fraudulent financial reporting.
 Keywords: Fraud Pentagon; fraudulent financial reporting; Logistic Regression

Forensic Accounting and Investigative Audit on the Effectiveness of Implementing Audit Procedures in Fraud Disclosure

Forensic accounting is a branch of accounting that focuses on investigating, disclosing, and preventing fraud and illegal activities in the business environment. Its main purpose is to identify, collect, analyze and present evidence that can be used in legal proceedings. Meanwhile, an investigative audit is an approach to auditing that is focused on investigating indications of fraud or non-compliance in the financial statements or business processes of an entity. The effectiveness of implementing audit procedures in disclosing fraud involves the application of a careful and careful methodology to identify signs or evidence of fraud in the financial statements and identify signs of fraud, the auditor uses knowledge about the mode of fraud and analysis of data to identify potential indications of fraud in transactions or financial reports, among others, by using forensic techniques. Auditors can use forensic data analysis techniques, digital examinations, and interviews to gather more in-depth and accurate evidence. Carry out segregation of duties. The auditor must examine the internal control system and ensure that there is adequate segregation of duties to prevent opportunities for fraud as well as test the substance of transactions to ensure that transactions reported in the financial statements actually occur and have a valid basis, in collaboration with the forensic team. In complex cases, the auditor needs to work with a forensic team that has special expertise in fraud analysis to detect fraud and the auditor prepares a report that provides a clear description of the fraud, along with recommendations for corrective measures and fraud prevention. The results of investigative audits are used to identify opportunities for improvement in the internal control system so that fraud can be minimized. Forensic accounting and investigative auditing have an important role in helping businesses detect, address and prevent fraud through a careful and evidence-based approach, effective audit procedures can reveal potential fraud and provide guidance for necessary improvements in internal controls.

The perception of accountants/auditors on the role of corporate governance and information technology in fraud detection and prevention

PurposeThis paper aims to investigate the perceptions of financial accountants and both internal and external auditors regarding the impact of corporate governance (CG) and information technology (IT) on the detection and prevention of fraud within organizations.Design/methodology/approachPrimary data were collected from 250 financial accountants, internal auditors and external auditors through questionnaires. The non-probability snowball sampling technique was used for data collection, with the sample t-test, one-way ANOVA and paired sample t-test applied for analysis.FindingsThe results indicate that robust CG practices and IT techniques significantly aid in detecting and reducing fraudulent activities by minimizing opportunities, rationalizations, pressures and capabilities of potential employees to commit fraud. Internal controls also play a significant role in reducing instances of fraud. Notably, ethical officers and ethical training were not perceived as significantly effective in preventing and detecting fraud, leading to a perception that fraudulent practices are prevalent and increasing the risk of future fraudulent activities.Research limitations/implicationsThis study recommends the adoption of strong CG practices to identify potential fraud within an organization. Moreover, IT techniques should be tailored to specific needs for effective utilization. Furthermore, the government should increase awareness regarding data provision by departments, organizations and other related personnel. Future research could use secondary data from various regions to expand the literature in this field.Originality/valueThis research uniquely combines three significant factors: CG, IT and forensic accounting in fraud detection and prevention. It contributes to the enhancement of literature about fraud and its preventive and detective measures. The results of this study set the seed for future research, government policymaking and enhanced organizational practices.

Disclosing Frauds in Herbal Food Supplements Labeling: A Simple LC-MS/MS Approach to Detect Alkaloids and Biogenic Amines

Food supplements are a category of products perceived safe and therefore commonly used by different categories of consumers without any particular attention or precaution. However, health risks associated with the consumption of supplements containing undeclared substances cannot be excluded. A variety of analytical methods are used to control supplement quality composition, but usually, these procedures are complex and time-consuming. Here, we report the results of a simple and fast liquid chromatography-tandem mass spectrometry (LC–MS/MS) method, to detect and quantify simultaneously different categories of active molecules, such as biogenic ammines and natural alkaloids that at high doses can produce negative health effect in consumers. Three categories of products intended for body weight loss, energy boosting, and erectile dysfunction treatment, purchased through e-commerce sites and from local supermarkets, were analyzed (n = 91). The caffeine, synephrine, agmatine sulfate, yohimbine, phenethylamine, and icariin were correctly separated and identified with good precision (RSD < 20%) and recovery (89–109%). The identification and quantification of the analytes in real samples highlighted that the 26% of the samples were not compliant with labeling, confirming that frauds are very common also in the natural supplements market. This LC-MS/MS method could be easily used to test natural supplements in order to check the correct labeling and to protect consumers from potential health risks and food frauds.

Fake Product Identification System Using Blockchain Technology

The Fake Product Identification System (FPIS) is a cutting-edge solution that tackles the global issue of counterfeit products by leveraging blockchain technology. In this system, each genuine product receives a unique digital identifier and is recorded on the blockchain during its production phase, ensuring a tamper-proof and traceable record. When consumers purchase a product, they can easily verify its authenticity through a mobile application that interfaces with the blockchain to retrieve relevant information. By eliminating the need for centralized databases and intermediaries, the FPIS provides a transparent and secure environment, protecting sensitive data and preventing potential fraud. With its decentralized consensus mechanism, the system maintains the integrity and immutability of product information, ensuring consumers can make informed choices and trust the authenticity of their purchases. Ultimately, the Fake Product Identification System aims to enhance consumer trust and revolutionize supply chain management, promoting a safer marketplace for all stakeholders. Keywords-Consumer Protection, Product Authenticity, Decentralization, Immutability.

ANALISIS DETEKSI FRAUD LAPORAN KEUANGAN DENGAN METODE BENEISH M-SCORE (STUDI KASUS PT BANK TABUNGAN NEGARA PERSERO TBK)

The financial statements present important disclosures in maintaining the quality of the capital market also explain the current state of the company and its going concern in the future. However, many financial reports are disclosed as misstatements due to manipulation, falsification, and fraud by changing the value and financial information. Self-detection of financial statement fraud is an important matter that must be prioritized. This study uses the Beneish M-Score model to detect potential manipulation of annual financial reports with case study in PT Bank Tabungan Negara (BTn) Persero Tbk for the 2017-2019 book year.&#x0D; Based on the results of data processing, the M-Score value of the company’s financial statement in 2017, 2018, and 2019 respectively was -2,16; -2,20; and -1,87. Those value were greater than the reference value was -2,22. So it can be concluded that based on this model there was potential for manipulation in PT BTN Persero Tbk’s financial statements. The Beneish M-Score model is used to detect potential financial statement fraud or income manipulation in the company’s financial statements. Moreover this model is very useful for shareholders, investors, and creditors to examine the fairness of company’s financial statement. &#x0D; Keywords : Beneish M-Score, Fraud, Financial Statement, Bank BTN Persero Tbk

PEMANFAATAN TEKNOLOGI INFORMASI DAN PENERAPAN PENGENDALIAN INTERN KAS

Internal cash control is a control process to achieve specific goals. Internal control is carried out by people from each organization, including management and all employees. Internal control is expected to provide reasonable assurance to managers and employees in an organization. The data analysis method in this study uses simple linear regression to predict the effect of independent variables. The population in this study amounted to 100 employees in the Aceh SKPD. The results showed that the use of information technology affected the implementation of internal cash control. However, it indicates that information technology can play an essential role in the performance of internal cash control in the Regional Work Unit (SKPD). By utilizing information technology, SKPD can improve efficiency and effectiveness in controlling and managing cash internally, such as identifying potential fraud or weaknesses in the cash control system, monitoring financial transactions in real-time, and making more accurate and transparent financial reports.

The Use of Blockchain Technology and OCR in E-Government for Document Management: Inbound Invoice Management as an Example

The field of electronic government (e-government) is gaining prominence in contemporary society, as it has a significant influence on the wider populace within the context of a technologically advanced world. E-government makes use of information and communication technologies (ICTs) at various levels and domains within government agencies and the public sector. ICT reduces manual labour, potential fraud points, errors, and process lapses. The Internet’s quick accessibility and the widespread adoption of modern technologies and disciplines, such as big data, the Internet of Things, machine learning, and artificial intelligence, have accelerated the need for e-government. However, these developments raise a number of data reliability and precision concerns. The adoption of blockchain technology by researchers demonstrates its efficacy in addressing such issues. The present study proposes the SECHash system model, which integrates blockchain and Optical Character Recognition (OCR) technologies for the purpose of regulating the processing of incoming documents by governmental agencies. As a case study to assess the proposed system paradigm, the study uses a document containing incoming invoices. The proposal seeks to maintain the integrity of document data by prohibiting its modification after acceptance. Additionally, SECHash guarantees that accepted documents will not be destroyed or lost. The analysis demonstrates that using the SECHash model system will decrease fraudulent transactions by eradicating manual labour and storing documents on a blockchain network.

FASTER: The European Commission Publishes a Proposed Council Directive Aimed at Improving the Withholding Tax Process within the European Union

In this article, the authors discuss the European Commission’s legislative proposal for a Directive that aims to improve withholding tax processes, reduce barriers to an effective capital markets union and reduce potential fraud and abuse connected with such withholding tax claims.

Post-publication Peer Review with an Intention to Uncover Data/Result Irregularities and Potential Research Misconduct in Scientific Research: Vigilantism or Volunteerism?

Irregularities in data/results of scientific research might be spotted pre-publication by co-workers and reviewers, or post-publication by readers typically with vested interest. The latter might consist of fellow researchers in the same subject area who would naturally pay closer attention to a published paper. However, it is increasingly apparent that there are readers who interrogate papers in detail with a primary intention to identify potential problems with the work. Here, we consider post-publication peer review (PPPR) by individuals, or groups of individuals, who perform PPPRs with a perceptible intention to actively identify irregularities in published data/results and to expose potential research fraud or misconduct, or intentional misconduct exposing (IME)-PPPR. On one hand, such activities, when done anonymously or pseudonymously with no formal discourse, have been deemed as lacking in accountability, or perceived to incur some degree of maleficence, and have been labelled as vigilantism. On the other, these voluntary works have unravelled many instances of research misconduct and have helped to correct the literature. We explore the tangible benefits of IME-PPPR in detecting errors in published papers and from the perspectives of moral permissibility, research ethics, and the sociological perspective of science. We posit that the benefits of IME-PPPR activities that uncover clear evidence of misconduct, even when performed anonymously or pseudonymously, outweigh their perceived deficiencies. These activities contribute to a vigilant research culture that manifests the self-correcting nature of science, and are in line with the Mertonian norms of scientific ethos.

Fraud Hexagon: Detection of Fraud of Financial Report in State-owned Enterprises in Indonesia

This study aims to identify the potential for fraudulent financial reporting using the Fraud Hexagon approach with pressure, capability, collusion, opportunity, rationalization, and ego indicators. The population in this study is state-owned companies with observations for 12 years, from 2010 to 2021, and uses regression analysis with SPSS tools to test the Hypothesis. The results showed that pressure and ego could detect the potential for fraudulent financial reporting. There is a tendency for management to report conditions that are different from the actual conditions when under pressure in the form of performance targets that are not supported by the resources they have. CEO duality also provides an excellent opportunity for fraudulent financial reporting practices because when there is a position war, there is no cross-check between departments, so other parts cannot detect the potential fraud committed by the CEO. This condition also indicates weak control processes that provide wider opportunities for fraudulent financial reporting. At the same time, variables of ability, collusion, opportunity, and rationalization cannot detect financial statement fraud. This condition is because state-owned companies are required to implement the Minister of Finance Regulation number PER-11/MBU/07/2021 and the Financial Services Authority number 13/POJK.03/2017 as the basis for the implementation of Good Corporate Governance so that it is possible to commit financial reporting fraud very small.

ANALISIS SISTEM INFORMASI AKUNTANSI SIKLUS PENGGAJIAN PADA USAHA MEBEL AYU

The purpose of this study was to analyze the accounting information system of the payroll cycle process in the Ayu Furniture industry. Data collected by interview, observation and documentation techniques. Observation and documentation techniques are directly carried out at the research location in the Ayu Furniture industry. Analysis carried out by the method (PIECES) Performance, Information, Economy, Control, and Efficiency. The research approach uses descriptive qualitative, namely by describing the payroll accounting information system that exists in the company which includes activities in the payroll system, related functions, documents used, accounting records used, network procedures that make up the system and internal control system. The results showed that the payroll process in the Ayu Furniture industry uses a manual system, this can lead to potential fraud or negligence at the stage of attendance and submission of employee salaries, although the system has been running smoothly and effectively in terms of service, performance, information and economy.

Lack of evidence for telehealth fraud

Despite evidence to the contrary, U.S. policy makers, payers, and the public continue to express apprehension that the use of telehealth is associated with increased risk of fraud and abuse. The fraudulent use of telehealth is multifaceted and complex, ranging from potentially false claims to miscoding, inaccurate billing, and kickbacks. For the past 6 years, the U.S. Federal Government has undertaken research studies to examine potential fraud issues related to the use of telehealth, including up-coding time spent with the patient, misrepresentation of the service provided, and billing for services that were not rendered. This article reviews previous efforts to assess the risk of fraud associated with the delivery of virtual care in America, concluding that there is little evidence for higher levels of fraud and abuse related to the use of telehealth.

Of degens and defrauders: Using open-source investigative tools to investigate decentralized finance frauds and money laundering

Fraud across the decentralized finance (DeFi) ecosystem is growing, with victims losing billions to DeFi scams every year. However, there is a disconnect between the reported value of these scams and associated legal prosecutions. We use open-source investigative tools to (1) investigate potential frauds involving Ethereum tokens using on-chain data and token smart contract analysis, and (2) investigate the ways proceeds from these scams were subsequently laundered. The analysis enabled us to (1) uncover transaction-based evidence of several rug pull and pump-and-dump schemes, and (2) identify their perpetrators’ money laundering tactics and cash-out methods. The rug pulls were less sophisticated than anticipated, money laundering techniques were also rudimentary and many funds ended up at centralized exchanges. This study demonstrates how open-source investigative tools can extract transaction-based evidence that could be used in a court of law to prosecute DeFi frauds. Additionally, we investigate how these funds are subsequently laundered.