Instances Of Fraud Research Articles

Provisional Attachment on the Documentary Letters of Credit: A Comparative Study Saudi and Emirati Law

The study aims to study the provisional attachment of documentary letters of credit to prevent the beneficiary from liquidating the value of the credit as the beneficiary used fraudulent methods without which he would not have been able to obtain the value of the credit. The study follows the descriptive analytical comparative approach. It will discuss the legislative and judicial efforts related to the provisional attachment of documentary letters of credit. This will lead us to the comparative approach to comparing what is stated in Emirati and Saudi law and the judgments. The study concludes several findings, including that documentary letters of credit are an unquestionable means of success in international trade operations, especially since they discuss the problems of geographical distance between the seller and the buyer. However, fraudulent behaviors by sellers (beneficiaries of the documentary letters of credit) disrupt the dominance of documentary letters of credit as the most important means of payment in international trade transactions. The study concludes with several recommendations, namely: the necessity of adopting the Uniform Customs and Practice (UCP) for Documentary Letters of Credit as a rule to eliminate instances of fraud in its transactions.

The role of blockchain technology and smart contracts in enhancing the transparency of fragmented investments in overseas real estate

As the mobility of global capital is getting higher, more and more investors tend to invest in overseas real estate. However, the conventional model of investing in real estate possesses several disadvantages including high entry hurdles, low levels of market liquidity, and low transparency, particularly for retail investors. This article will seek to establish how blockchain technology as applied in smart contracts can help in the clarification of unclear fragmented investments in overseas property. First of all, this paper begins with the definition and examples of applying conventional and advanced blockchain technology and then describes the work of smart contracts and the comparison between regular contracts and smart contracts. The paper also evaluates the nature of fragmented investment in overseas property and its associated risks before suggesting the use of blockchain and smart contracts for a new institutional structure of the investment platform. This study demonstrates how the technologies can advance data sec, promote trust in transactions, and how investment can be automated. It can be concluded that blockchain technology, and smart contracts, can enhance Overseas Real Estate Investment by increasing transparency and decreasing the investment threshold as well as the instances of fraud. This study also presents an analysis of the potential difficulties that may be encountered in real-world applications and a recommendation of what may be done in subsequent studies. On one hand, this research offers a new perspective to the theoretical world, on the other hand, it offers practical problems and solutions to the practitioners who are a part of overseas real estate markets and businesses by steadily helping the overseas real estate markets to move in the fairer and more transparent direction.

Examine the Relationship Between the Professional Competency of IA and the Effectiveness of RM in Improving Compliance in Iraq's Public Organization

The importance of internal audits has escalated in the current dynamic and intricate economic and financial landscape. It plays a crucial role in assisting management in attaining its goals by offering unbiased and independent assurance regarding the efficiency of risk management, the sufficiency of internal control, and the accuracy of financial reporting. It is widely known that Iraq has seen instances of fraud and mismanagement of public funds in recent years, highlighting a systemic flaw in the regulatory structure and the auditing profession inside public sector organizations. However, by enhancing compliance through the fundamental pillars of optimizing the efficiency of internal audits and the efficacy of risk management, we can envision a future where the misuse of public funds and various forms of corruption are minimized. This, in turn, will lead to improved adherence to relevant laws and the production of reliable financial statements that can be used for decision-making by appropriate stakeholders. It is the role of government agencies to prioritize these improvements, as they have the power and responsibility to do so. We created a questionnaire of three dimensions and 45 paragraphs to fulfil the research objectives. A sample of internal auditors in sectoral organizations was randomly selected, comprising 283 respondents. We employed both primary and multiple regression analyses to examine the hypotheses. The study concluded that there needs to be a strategy for continuing education for internal audit employees, which indicates a lack of sufficient interest in this topic and the lack of clear strategies and policies for risk management.

TINDER SWINDLERS: SUBSTANTIVE AND PROCEDURAL MATTERS PERTAINING TO ONLINE DATING FRAUD UNDER THE COMMON LAW AND THE CYBERCRIMES ACT

The proliferation of online dating scams or fraud has presented a significant threat to users of online dating platforms globally. Scammers misrepresent themselves as would-be lovers, usually employing false identities, in order to defraud unsuspecting victims of large sums of money. Not only do romance scammers cause great financial harm to their victims, but also often leave victims with long-term psychological scars. As both the common law and the Cybercrimes Act 19 of 2020 address instances of fraud, both may potentially be employed to address the issue of online dating fraud. The State may charge an accused with both offences, but a conviction based on both would probably constitute a duplication of convictions. The offence of cyber fraud under the Cybercrimes Act may however be more onerous to prove, owing to the particular way that cyber fraud must be committed under section 8. A conviction based on the Cybercrimes Act may, however, be more attractive to the State as certain minimum sentences apply, depending on the amount defrauded, whether the accused was a law enforcement officer, whether someone was in charge of or had access to the data belonging to others, and whether the offence was committed in concert with others.

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.

Identification of fraudulent financial statements through a multi‐label classification approach

SummaryAlthough the financial audit controls in companies have advanced over the years, the number of corporate fraud instances is growing, thus raising the need for investigating the factors that can be used as early warning signals and developing effective systems for identifying financial fraud. In this paper, financial statements from 133 Greek companies listed in the Athens Stock Exchange over the period 2014 to 2019 are investigated, based on the fraud diamond theory. Financial data and corporate governance variables are used as inputs to data mining techniques to develop models that can identify patterns of irregularities in a company's financial reports. To this end, popular machine learning classification algorithms are employed in a novel multi‐label classification setting that not only identifies fraudulent cases but also considers the nature of the auditors' comments. The results indicate that the proposed multi‐label approach provides enhanced results compared to binary classification algorithms, avoiding inconsistent outputs with respect to the existence of different forms of manipulation of financial statements.

Machine Learning Approaches for Credit Card Fraud Detection

Credit card fraud detection is a critical challenge in the financial sector, necessitating the adoption of advanced machine learning algorithms for timely and accurate identification of fraudulent transactions. In this project, we investigate the efficacy of four prominent machine learning algorithms - Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees (DT), and Random Forest (RF) in detecting credit card fraud. Through a comprehensive analysis, we evaluate the performance of these algorithms in terms of accuracy, precision, recall, and F1 score using real-world credit card transaction datasets. SVM, known for its ability to construct complex decision boundaries, excels in separating fraudulent and legitimate transactions. KNN leverages the proximity-based approach to identify similarities with known instances of fraud, while Decision Trees offer interpretable insights into fraudulent patterns. Random Forest combines the predictive power of multiple decision trees to produce robust and accurate predictions. Our findings shed light on the strengths and weaknesses of each algorithm, providing valuable insights for developing effective fraud detection systems in the financial industry

UNMASKING DECEPTION: FORENSIC ACCOUNTING'S INFLUENCE ON FRAUD REDUCTION IN INDIA'S BANKING INDUSTRY

The significance of forensic accounting in fraud reduction cannot be overstated, particularly in a complex financial landscape like India's banking industry. Forensic accounting serves as a crucial instrument in detecting, investigating, and preventing fraudulent activities that pose substantial threats to the integrity and stability of financial institutions. Through specialized techniques like data analysis, financial examination, and investigative methodologies, forensic accountants play a pivotal role in uncovering fraudulent schemes, tracing illicit financial transactions, and providing evidence for legal proceedings. This study delves into the critical role of forensic accounting in combating fraudulent activities within India's banking sector. With a focus on enhancing understanding and awareness, the research sets out three primary objectives. Firstly, it aims to investigate the current landscape of forensic accounting in India, exploring its methodologies, practices, and regulatory framework. Secondly, the study seeks to assess the level of awareness among bank managers regarding the efficacy of forensic accounting in reducing instances of fraud. Finally, it endeavours to analyse the relationship between the demographic profiles of bank managers—such as age, gender, and years of experience—and their awareness of forensic accounting's role in fraud reduction. Moreover, the findings of this study carry practical implications for various stakeholders, including policymakers, regulators, banking professionals, and industry participants. By gaining insights into the awareness levels of bank managers regarding forensic accounting and its efficacy in fraud reduction, policymakers and regulators can develop targeted interventions to enhance training, education, and awareness initiatives within the banking industry. The study's methodology involved interviews with 200 bank managers, allowing for a comprehensive assessment of the impact of forensic accounting on fraud reduction within India's banking industry.

Enhancing Electoral Integrity: A Fingerprint-Verified Voting System for Fair and Secure Elections

Voting is a significant method through which citizens in democratic nations like India can express their opinions. Voters often use polling booths to cast their ballots. Voting is now done via an electronic voting machine thanks to advancements in technology. This essay discusses a fingerprint-verified voting machine that is Internet of Things based. The primary goals of this project are to decrease voting malpractices and make voting safer with fingerprint verification. Voter information is kept in a database along with their fingerprint. The system verifies the user's Aadhaar number and, if verified, determines whether more than one vote has been cast if the fingerprint matches the stored fingerprint. A "Matching failed" message will appear if the fingerprint matching is incorrect, and an "Aadhar not match" message will appear if the Aadhaar number is incorrect. The controller used in this project is an Arduino Uno. The user's fingerprint is used for authentication. Everybody's fingerprints differ from one another, if not slightly. A notification stating "Already voted" will appear in the event of malpractice. The ballot card is displayed and the result is stored on the cloud using the Arduino IDE for board programming. Only authorized voters may cast ballots, and the system alerts users to instances of fraud. This project ensures fair elections and protects citizens' right to vote.

Credit Card Fraud Detection and Identification using Machine Learning Techniques

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods.

The Evaluation of Strategies Based on the Analysis of Three Causes of Accounting Frauds

Accounting fraud has a long history and a wide range of harm, which has attracted wide attention in the world. Although numerous nations have put in place pertinent laws and policies to reduce the likelihood of accounting fraud, the reality shows that the desired effect has not been reached, and fraud instances are still prevalent. Moreover, the way of accounting fraud also constantly changes. This paper analyses the consequences and causes of accounting fraud through a review of the literature, focusing on the audit departments lack of adequate external oversight and the companys irrational corporate governance, which involves having one person fill two key roles. The accountants lack of business ethics is also discussed. In order to provide a reference for the prevention of accounting fraud in the future, the author evaluates some detection methods for accounting fraud and discovers that while adequate audit supervision and sound corporate governance are important, it appears that improving the accountants own business ethics matter more in the future.

Investment tokens as financial market tools

The rapid evolution of the digital financial asset market and its growing integration with traditional financial instruments, coupled with its global reach and unrestricted cross-border capital mobility, necessitate a thorough research of these market instruments. Specifically, it calls for an assessment of their potential as alternatives to conventional financial instruments. At the same time, there are many factors contributing to an increased risk of regulatory gaps in the financial market: structural vulnerability of the market of digital financial assets, high volatility of the value of crypto-assets, low transparency, the possibility of a spillover of risks from the market of digital financial assets to other financial markets, as well as the lack of regulatory consensus among financial regulators in the countries of the world. The research objective is to determine the role of investment tokens in today’s financial market, their utility in attracting investment resources on blockchain-based trading platforms, and the nuances of their classification as securities. The article delves into the core concepts and intricacies of regulating securities tokens and analyses quantitative metrics depicting the development of the investment token market and its relative share in the global financial market compared to other financial assets. Notably, the author outlines key criteria for securities regulators to consider when determining whether tokens should be classified as securities. The article reveals the main models of the initial offering of investment tokens, highlights their features, and shows their adaptability to changing conditions within the digital financial asset market and the evolving needs of market participants. It is evident that all current models for raising funds through investment tokens, including IEO, IDO, and STO sub-models, trace their roots back to the Initial Coin Offering (ICO) model and, fundamentally, do not differ significantly from traditional capital-raising models such as Initial Public Offering (IPO) and Secondary Public Offering (SPO) in traditional financial markets. This substantiates the need for global financial regulators to take actions aimed at officially classifying investment tokens as securities. Expanding securities legislation requirements to encompass issuers of investment tokens, particularly concerning information disclosure, promises to mitigate instances of fraud within the digital financial asset market. Such measures would fortify investor protection and reduce the vulnerability of financial markets as a whole.

Ensemble Learning for fraud detection in Online Payment System

The imbalanced problem in fraud detection systems refers to the unequal distribution of fraud cases and non-fraud cases in the information that is used to train machine learning models. This can make it difficult to accurately detect fraudulent activity. As a general rule, instances of fraud occur much less frequently than instances of other types of occurrences, which results in a dataset which is very unbalanced. This imbalance can present challenges for machine learning algorithms, as they may become biased towards the majority class (that is, non-fraud cases) and fail to accurately detect fraud. In situations like these, machine learning models may have a high accuracy overall, but a low recall for the minority class (i.e., fraud cases), which means that many instances of fraud will be misclassified as instances of something else and will not be found. In this study, Synthetic Minority Sampling Technique (SMOTE) is used for balancing the data set and the following machine learning algorithms such as decision trees, Enhanced logistic regression, Naive Bayes are used to classify the dataset.Majority Voting mechanism is used to ensemble the DT,NB, ELR methods and analyze the performance of the model. The performance of the Ensemble of various Machine Learning algorithms was superior to that of the other algorithms in terms of accuracy (98.62%), F1 score (95.21%), precision (98.02%), and recall (96.75%).

CRIMINAL ACCOUNTABILITY FOR CIVIL SERVANT (CPNS) ADMISSION FRAUD

Several instances of misconduct have occurred, wherein certain individuals have taken advantage of public interest surrounding the CPNS registration process. In 2018, Defendants AD and SH profited IDR 200 million. In 2019, Defendant HB gained a profit of up to 5.7 billion Rupiah through the CPNS Recruitment mode, and in 2021, (ON) amassed a profit of 9.7 billion Rupiah. This research aims to analyze the application of the law to cases of fraud perpetrated against Candidates for Civil Servants (CPNS), and propose preventive measures to curb instances of fraud under the guise of a special pathway. The study is conducted through both normative and sociological legal research, utilizing a sociological interpretive approach. The Statute Approach and Case Approach are employed in this study. Legal material is gathered using library techniques, and the analysis of legal materials is performed using qualitative methods. The study's findings reveal that: (1) CPNS registration is strictly free of charge, and any claims suggesting the availability of internal routes for assistance are baseless. Perpetrators of such acts are subject to charges under Article 378 of the Criminal Code, which involves deceiving and causing harm to others. (2) Candidates applying for CPNS positions need not seek any special means to pass the selection process, as the principle of Bureaucratic Reform within the state civil apparatus prohibits any form of illicit practice.

Fraud Detection and Identification in Credit Card Based on Machine Learning Techniques

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods

Fathoming fraud: unveiling theories, investigating pathways and combating fraud

PurposeAlthough corporations exert considerable efforts to uphold ethical standards in their business operations, fraud instances persist as an enduring and formidable challenge within organisations, defying their utmost efforts. The presence of fraud poses a substantial and recurring threat to corporations, leading to significant financial losses on an annual basis. This emphasises the crucial need for a comprehensive understanding of the factors contributing to fraudulent activities and the intricate nature of fraud risk factors inherent in business operations. Therefore, this paper aims to enhance the efficacy of fraud detection and prevention measures through critical analysis and refinement of established fraud theories, drawing upon the existing literature on this subject matter.Design/methodology/approachThis paper offers a comprehensive qualitative analysis of the existing literature, thoroughly reviewing prominent models that aim to elucidate the underlying motivations behind fraudulent behaviour. Moreover, drawing upon the existing theoretical foundation, this study conceptualises a model that enhances the understanding of the crucial factors contributing to fraudulent behaviour.FindingsThe study presents new theoretical insights concerning the role of personal integrity in fraudulent decision-making, presenting refined interventions that enhance comprehension of the underlying drivers of fraud occurrences and strategies for prevention. Furthermore, the study reveals a comprehensive three-part approach to improving organisational health through strengthening compliance mechanisms and cultivating an ethical-values-based culture.Originality/valueThe study introduces a novel conceptual framework, the personal ethic-based fraud motivation model, which offers a deeper understanding of the factors and conditions influencing individuals’ propensity to engage in fraudulent activities. Furthermore, this study presents a three Cs strategy that effectively delineates the influential forces that drive individuals to surmount fraud risks.

The Effectiveness of Internal Controls in Preventing Fraud and Financial Misconduct

Objective: The objective of this study is to examine the role of internal control in mitigating fraud and professional misconduct among small and medium-sized businesses (SMEs) within the Enugu metropolis.
 
 Method: To achieve this objective, a mixed-methods approach was employed. Qualitative interviews were conducted with SME owners and managers, and quantitative data were collected for analysis. A sample of SMEs was selected from the Enugu metropolis, and their internal control mechanisms, maintenance practices, and effectiveness in reducing fraud and professional misconduct were assessed.
 
 Result: The study's findings reveal several key insights. Firstly, SMEs in the Enugu metropolis actively create and implement internal control measures tailored to their operational needs. Secondly, SMEs that consistently maintain and update their internal controls experience a significant decrease in instances of fraud and professional misconduct. Finally, there is a clear correlation between well-implemented internal controls and reduced levels of fraud and professional misconduct within SMEs.
 
 Conclusion: In conclusion, the study highlights the crucial role of internal control in addressing the challenge of fraud and professional misconduct, especially within SMEs. SME owners and managers should assess their existing internal control mechanisms to ensure they are well-suited to their business operations. Regular review and adaptation of internal controls are necessary to sustain their effectiveness in preventing and detecting fraudulent activities. Proper maintenance of financial records and regular statement preparation contribute significantly to the success of internal control systems.

Preventing financial statement fraud in the corporate sector: insights from auditors

Purpose This study aims to examine how auditors perceive the influence of crucial fraud prevention factors in deterring financial statement fraud within the corporate sector. Additionally, this research explores the mediating effect of fraud awareness in elucidating the impact of ethical leadership and internal control systems on preventing financial statement fraud. Design/methodology/approach The study used an online survey, targeting a sample of 141 professionally qualified auditors with at least one year of practical experience in the field. The researchers used “Structural Equation Modeling (SEM)” to examine relationships between latent variables using partial least squares structural equation modeling. The study investigated the impact of whistleblowing systems, fraud awareness, ethical leadership, internal control systems and corporate governance on fraud prevention. Findings This research finding provides evidence to the corporate sector by establishing the significance of fraud awareness as the most influencing factor in preventing financial statement fraud. Furthermore, the combined explanatory variables account for 77.4% of the overall variance in financial statement fraud prevention. The study reveals a partial mediation effect of fraud awareness on the relationship between the internal control system and financial statement fraud prevention. Practical implications This research finding may assist in developing an effective fraud prevention programme to mitigate fraud instances and improve financial reporting quality. In the corporate sector, each organisation should clearly specify the policies on whistleblowing systems, fraud awareness training, internal control systems and corporate governance. To foster a comprehensive fraud prevention programme, the leaders should enforce these policies with employee support. Originality/value This research integrated crucial elements to develop a new theoretical framework for investigating financial statement fraud prevention within the corporate context. Accordingly, this research framework provides a more in-depth explanation of preventing financial statement fraud from an auditor’s perspective. Additionally, this research is the first to explore the mediating role of fraud awareness in influencing the effectiveness of the internal control system in preventing financial statement fraud.

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

Purpose This 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/approach Primary 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. Findings The 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/implications This 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/value This 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.

NUMBERS DON’T LIE: DECODING FINANCIAL ERROR AND FRAUD THROUGH BENFORD’S LAW

This research investigates Benford’s Law as a statistical instrument to detect financial fraud and errors. Benford's Law, also known as the First-Digit Law, states that lesser digits, specifically '1', frequently appear as the leading digit in numerous numerical datasets, and deviations from this distribution may indicate potential financial irregularities. This research examines literature demonstrating the application and efficacy of Benford’s Law in identifying numerical inconsistencies indicative of financial misconduct. This research investigates the use of Benford’s Law as a tool of detecting error and fraudulent activities within the sales data of two branches during 2022. A comprehensive dataset of 3098 records from Bandung and 539 records from Surabaya was collected after excluding certain data points that exhibited abnormalities. The application of Benford's statistical test discovered a discrepancy between the Benford probability and the observed probability, suggesting the presence of possible errors and frauds. The audit findings unveiled anomalies in pricing and instances of fraudulent activities in both locations, primarily due to pricing discrepancies and incorrect price inputs from sales orders. Furthermore, instances of fraud involved the manipulation of set prices for personal gain by salesmen. The results affirmed the hypothesis that a larger deviation between Benford’s probability and the observed probability corresponded with a higher incidence of error and fraud. However, it observes that Benford’s Law is not a stand-alone solution for detecting fraud, as not all financial datasets conform to it and deviations from the law only indicate the possibility of fraud, not confirm it. Therefore, the research suggests using Benford’s Law in conjunction with other data analysis and auditing techniques to conduct a comprehensive investigation. The conclusion of research emphasizes the significance of Benford’s Law in the field of forensic accounting and the need for multidimensional strategies for effective error and fraud detection.