Future Fraud Research Articles

The Parmalat Scandal: An Analysis of Financial Deception and Its Implications for Global Business

The Parmalat scandal, often compared to Europe's Enron case, highlights one of the biggest financial frauds in global business history. Revealed in 2003, the case exposed an astonishing €14 billion deficit in the company's accounts, resulting from fraud, mismanagement, and oversight failures. This case study delves into the complexities of the Parmalat scandal, exploring its causes, impacts, and lessons for global businesses. It examines inadequate corporate governance, strategic missteps, auditing failures, and ethical violations, highlighting the importance of maintaining an ethical and transparent corporate culture. The role of auditing is emphasized, pointing out deficiencies in both internal and external audits, and the need for stricter controls and a whistleblowing culture. Parmalat's strategic decisions are scrutinized to understand how they contributed to the company's decline. The problematic relationship between majority and minority shareholders is discussed, revealing internal conflicts. The response to the scandal included a push for better corporate governance standards, regulatory reforms, and the adoption of more robust governance models. The importance of an ethical corporate culture, financial transparency, and the empowerment of whistleblowers are underscored as essential for preventing future frauds. The Parmalat case serves as a warning of the need for transparent and ethical practices in business, reinforcing that profitability should not override ethical considerations. This study provides a blueprint for companies to avoid similar pitfalls, highlighting corporate governance, business ethics, and oversight as pillars for trust and economic stability.

Improving credit card fraud detection using machine learning and GAN technology

The motivation behind this study stems from identifying contemporary challenges associated with prosecuting electronic financial crimes. Highlights ongoing efforts to identify and address credit card fraud and fraud as there are many credit card fraud issues in the financial industry. Traditional methods are no longer able to keep up with modern methods of tracking the behavior of credit card users and detecting suspicious cases. Artificial intelligence technology offers promising solutions to quickly detect and prevent future fraud by credit card users. Datasets used to detect financial anomalies are affected by imbalances in financial transactions, and this study aims to address the imbalance of financial fraud datasets using adversarial algorithm techniques and compare them with the most commonly used methods in the scientific literature.The results showed that the function of the adversarial algorithm is consistent in several ways, including allowing researchers and interested parties to determine data growth rates, which helps bring the dataset closer to real-time data from financial markets and banks. This study proposes a hybrid machine learning model consisting of three machine learning algorithms: decision trees, logistic regression, and Naive Bayes algorithm, and calculates performance metrics such as accuracy, specificity, precision, and F1 score. Experimental results reveal varying degrees of accuracy in fraud detection. Model testing using the SMOTE method recorded an accuracy of 98.1% and an F-score of 98.3%. On the other hand, the oversampling and under sampling test methods showed similar performance, with the two methods recording an accuracy of 94.3 and 95.3 and an F-score of 94.7 and 95.1, respectively. Finally, the GAN method excelled, receiving a test score and accuracy of 99.9%, as well as exceptional precision, recall, and F1 score. As a result, we conclude that the GAN method is able to balance the data set, which in turn is reflected in the performance of the model in training and the accuracy of predictions when tested. Historical transaction analysis identifies behavioral patterns and adapts to evolving fraud techniques. This approach enhances transaction security and protects against potential financial losses due to fraud. This contribution allows financial institutions and companies to proactively combat fraudulent activities.

ANALISIS KECURANGAN DALAM MENGHADAPI PENIPUAN DI SITUS E-COMMERCE MENGGUNAKAN RANDOM FOREST ; PENDEKATAN MACHINE LEARNING BERBASIS AI

In this rapidly growing digital era, the phenomenon of e-commerce has become a major highlight, the rapid growth of e-commerce has attracted more and more users. However, cases of sophisticated and dynamic fraud are increasing as the volume of transactions increases. This phenomenon not only poses a risk of financial loss for buyers and sellers but also threatens the trust that is so important in the e-commerce industry. To solve this problem, the author uses a random Forest AI-based Machine Learning approach in analyzing and finding fraud patterns to deal with fraud on e-commerce sites. The Random Forest model was chosen because of its excellent ability to handle complex e-commerce transaction data, including the ability to find non-linear patterns, its resistance to overfitting, and its scalability on large datasets. This model is expected to identify suspicious fraud patterns in e-commerce transactions. The method will involve data processing, feature selection, and model training using a dataset that includes ecommerce transactions. The results of this research are expected to contribute to a better understanding of fraud on e-commerce sites in the face of future fraud. Effective fraud detection is also expected to reduce the losses caused by fraud on e-commerce sites and protect users from the risk of fraud.

Corporate fraud, political connections, and media bias: Evidence from China

AbstractThis article empirically examines how political connections (PCs) affect a firm's media reaction after corporate fraud. Using data for Chinese listed companies from 2008 to 2021, we find that the media reports more positively for firms with PCs than for others that do not possess such advantages after the enforcement against fraud. The results are robust to a series of robustness checks and endogeneity corrections. When decomposing media reports, we find that PCs only facilitate positive media coverage but do not impede negative media coverage, which is more pronounced in state‐controlled media. This suggests that PCs protect firms’ branding by facilitating positive media reports rather than withholding bad news. Moreover, we find this protective effect is more pronounced in firms with stronger PCs, weaker anti‐corruption regulation, lighter punishment for fraud, private ownership, and more donations. Further, the consequences analysis shows that this kind of protective effect significantly increases the probability of future fraud and stock price crashes. Our findings present a new perspective on the role of PCs and provide evidence for political bias in media coverage.

Quality Control of Personalized Drug Products – Identity and Quantity of Monoclonal Antibodies as Active Pharmaceutical Ingredient

Deliberate underdosing occurred in personalized preparations of drugs such as monoclonal antibodies as the active pharmaceutical ingredient in the past. To ensure the required quality standard and to prevent future fraud attempts at an early stage, a HPLC-DAD-HRMS method was established. Thereby, identity and quantity of the active ingredients bevacizumab, rituximab and trastuzumab were determined. The analysis of ten samples from seven pharmacies fulfilled the quality criteria and were therefore not objectionable.

Safeguarding FinTech innovations with machine learning: Comparative assessment of various approaches

Protecting data is paramount to the development of FinTech. Fraudulent activities can exploit weaknesses in FinTech systems, wreaking havoc on both customers and service providers. However, machine-learning approaches have the potential to spot irregularities in FinTech systems, looking for red flags in economic data sets and using such red flags to inform predictive models for the detection of future fraud. We assess anomaly detection techniques, thereby adding to this crucial topic. We apply a variety of techniques to multiple synthetic and real-world databases. Findings corroborate that machine-learning approaches help with fraud detection, although with varying degrees of effectiveness. Our findings demonstrate that competitive advantage is the most crucial component amongst some Fintech-based predictors, while sales volume is diagnosed as having the least effective importance. To ensure the consistency and accuracy of our findings, we choose case studies for evaluating ML-based fraudulent activities based on the availability of properly allowed appropriate.

Anti-Fraud Analysis during the COVID-19 Pandemic: A Global Perspective

The ongoing coronavirus disease 2019 (COVID-19) pandemic has brought unexpected economic downturns and accelerated digital transformation, leading to stronger financial fraud motives and more complicated fraud schemes. Although scholars, practitioners, and regulators have begun to focus on the new characteristics of financial fraud, a systematic and effective anti-fraud strategy during the pandemic still needs to be explored. This paper comprehensively analyzes the lessons of anti-fraud that we should learn from the COVID-19 pandemic. By exploring the complex motives and schemes of fraud, we summarize the characteristics of financial fraud activities and further analyze the regulatory challenges posed by financial fraud during the outbreak. To better cope with the fraudulent activities during the pandemic, policy proposals on how to improve the supervision of financial fraud activities are put forward. In particular, the panoramic data and graph-based techniques are powerful tools for future fraud detection.

Evaluating the effectiveness of a social grant regulating framework in South Africa

Orientation: Fraud and corruption have become a major concern for the South African government. However, the government has made attempts (often futile) to fight the scourge of corruption. The South African Social Security Agency (SASSA) developed a framework aimed at minimising corruption; nevertheless, loopholes in its anticorruption efforts continue to be an encumbrance.Research purpose: This research sought to evaluate the effectiveness of the Fraud Management and Compliance Department (FMCD) in terms of its role in the prevention, detection, investigation, reporting and resolution of fraud, theft, corruption and maladministration within the SASSA.Motivation for the study: The study was based on public knowledge of the plethora of activities both by civilians and by the SASSA officials that are negatively affecting the SASSA’s achievement of its goals and objectives.Research design, approach and method: The study adopted a qualitative research approach, including 10 participants with whom one-on-one interviews were conducted. Thematic analysis was used.Main findings: The study mainly uncovered that the Special Investigation Unit, National Prosecuting Authority, South African Police Service and Public Service Commission each play a critical role in investigating corruption, fraud and maladministration within the SASSA, thus contributing to how future fraud situations are handled.Practical/managerial implications: The article recommends the training and development of relevant authorities to enhance the effectiveness of the FMCD within the SASSA.Contribution/value-add: This article contributes to the literature on proposed solutions in effectively diminishing corruption in a South African government entity.

ACCOUNTING FRAUD IN CORPORATE: CASE STUDY ON SATYAM COMPUTERS LIMITED

In the modern globalized and liberalized era, the firms are facing stiff competition and are under pressure to show the good financial results through financial reporting. In such an environment, they start using the creative accounting practices, especially in unsuitable situation to boost up the profit or manipulate the assets and liabilities to report to the stakeholders the image that is better than the actual image. Corporate accounting fraud is a major problem that is increasing both in its frequency and severity. Research evidence has shown that growing number of frauds have undermined the integrity of financial reports, contributed to substantial economic losses, and eroded investor’s confidence regarding the usefulness and reliability of financial statements. An attempt is made in this paper to examine in-depth and analyse India's Enron, Satyam Computer's " creative-accounting " scandal. Satyam was brought to its knee due to 'tunnelling' effect. The Satyam scandal highlights the importance of securities laws and Corporate Governance in emerging markets. Their scandal/fraud has put a big question mark on the entire corporate governance system in India. Indeed, Satyam fraud "spurred the government of India to tighten the CG norms to prevent recurrence of similar frauds in future. Thus, major financial reporting frauds need to be studied for “lessons-learned” and “strategies-tofollow” to reduce the incidents of such frauds in the future. Thus this paper is devoted to the case study of Satyam, its creative accounting scandal and the effect of it on the global market.

Detection of Credit Card Fraud using a Hybrid Ensemble Model

The rising number of credit card frauds presents a significant challenge for the banking industry. Many businesses and financial institutions suffer huge losses because card users are reluctant to use their cards. A primary goal of fraud detection is to identify prior transaction patterns to detect future fraud. In this paper, a hybrid ensemble model is proposed to combine bagging and boosting techniques to distinguish between fraudulent and legitimate transactions. During the experimentation two datasets are used; the European credit card dataset and the credit card stimulation dataset which are highly imbalanced. The oversampling method is used to balance both datasets. To overcome the problem of unbalanced data oversampling method is used. The model is trained to predict output results by combining random forest with Adaboost. The proposed model provides 98.27 % area under curve score on the European credit cards dataset and the stimulation credit card dataset gives 99.3 % area under curve score.

The Escalation of Disregard on Normative Economy in the Era of Pandemic

Covid-19 as a global pandemic spreading around the sphere is not only inducing issues on health but also in economic crisis, particularly on business. The purpose of this research is to reveal fraud scandals and presents the importance of implementing normative economy by following business ethics, seeing it from Islamic economic viewpoint which has been done by Prophet Muhammad and how to prevent future fraud scandal which one of it is from religion. This research used a qualitative method with a descriptive approach. The result of this research reveals that it is important to implement normative economy as it possesses an influence on economic development and stability to society as well as exhibits that fraud cases can be prevented earlier by building the awareness of normative economics through education from the micro to macro scale. The micro-scale is from the family circumstance and from the religion. Islam as a religion that gives guidance to overall human activities, emphasizes a lot about following norms. For instance, Prophet Muhammad taught a lot how to run the business according to the right norms and following the value of ethics. On macro-scale business ethics also can be taught at school. We concluded that the awareness of normative economics can be taught since the child phase by parents, by religion, and at education institutions to prevent future fraud scandals, particularly in the age of economic crisis.

Professional competence and business ethics

PurposeThe purpose of this paper is to determine if the failure of the Securities and Exchange Commission (SEC) to persecute Madoff is, in fact, an ethical failure. The authors turn to the extension of Aristotelian theory of moral values, virtue epistemology, to identify specific failures. The authors generalize this study’s conclusions to an overall responsibility of regulatory agencies to exercise epistemic virtues in their decision-making process. The authors explore how behavioral biases confound the execution of epistemic duty, and how awareness of behavioral biases can alleviate epistemic failures. The authors conclude this study with recommendations to prevent future frauds of Madoff proportions.Design/methodology/approachThe authors rely on recent advances in virtue epistemology and behavioral finance. The authors combine these two theoretical approaches to better understand the duty of competence inherent in being a finance professional, and even more so in being a regulator entrusted with overseeing financial industry, and psychological biases that may prevent finance professionals and regulators from performing this duty.FindingsThe paper concludes that the SEC employees failed to exercise epistemic virtues in their handling of the complaints implicating Madoff’s firm of fraud. This failure reveals a consistent pattern of behavioral biases in decision-making. The authors posit that knowledge of ethical theory, specifically virtue epistemology, as well as awareness of behavioral biases, which inhibit epistemically virtuous cognitive process, can improve the functioning of both finance industry and its overseers. The authors suggest that future finance professionals and regulators need to acquire this knowledge while pursuing their undergraduate education: it is the duty of business schools to facilitate this progress.Originality/valueThis paper combines the theory of virtue epistemology with the current knowledge of behavioral biases, which distort rational decision-making, to explain the failures of regulators to analyze fraud reports. The authors extend this finding to recommend the inclusion of the theory of virtue epistemology in business schools’ ethics curriculum.

Telecom traffic pumping analytics via explainable data science

Traffic pumping is a type of fraud committed in several countries, in which small telephone operators inflate the number of incoming calls to their networks, profiting from a higher access charge in relation to the network operator associated with the origin of the call. The identification of traffic pumping is complex due to the lack of labels for performing supervised learning, and the scarce literature on the topic. We propose a decision support system for fraud detection via clustering and decision trees. After data collection and feature engineering, we group the potential fraud cases into various clusters via an unsupervised learning approach. Then, we constructed a decision tree by using the cluster memberships as labels, evolving into the rules of a given variable and a certain label required for filing lawsuits against the suspicious cases. Telecommunication experts validate these rules to seek a legal resource against alleged perpetrators. We present the results of a case study from a Chilean telecommunication provider. All the lawsuits taken by the legal department were granted, confirming our success in dramatically reducing current and future fraud losses for the company.

Follow the Trail: Machine Learning for Fraud Detection in Fintech Applications.

Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.

Regulatory Enforcement in OTC Markets

Regulators of over-the-counter (OTC) markets face a unique enforcement challenge. OTC markets are particularly susceptible to investor fraud, but prior research indicates regulators prefer to focus their enforcement efforts on larger, exchange-listed firms. We shed light on the SEC’s tailored approach to enforcement in OTC markets by examining the SEC’s most common regulatory action against OTC firms—trading suspensions. We find that approximately half of all SEC suspensions are due to the targeted firms’ delinquency in filing required documents with the SEC. A relatively small percentage of suspensions are explicitly reactive to suspicious disclosure or trading behavior. The remaining suspensions are shell company eliminations that the SEC alleges help to prevent future fraud. We find, however, that these allegedly preventative suspensions are more likely to occur in stocks that already exhibit recent indicators of fraud, and that the SEC’s practice of suspending shell companies does little to prevent future fraud.

Hiring Auditors with a Part II Quality Control Report: An Opportunity for Increased Earnings Management or Lower Audit Fees?

ABSTRACT The Public Company Accounting Oversight Board's (PCAOB) Part II inspection reports, which disclose systemic quality control issues that auditors fail to remediate, signal poor audit quality for triennially inspected audit firms. Auditors that receive a Part II inspection report typically experience a decrease in clients, which demonstrates a general demand for audit quality. However, some companies hire auditors that receive Part II inspection reports. We examine potential reasons for hiring these audit firms. We find that relative to companies that switch to auditors without Part II reports, companies that switch to auditors with Part II reports have higher discretionary accruals in the first fiscal year after the switch, which indicates lower audit quality and a heightened risk for future fraud. We find no difference in audit fees. Our results suggest that PCAOB Part II inspection reports may signal low-quality auditors to companies that desire low-quality audits. Data Availability: Data are available from the public sources cited in the text.

THE VALUE OF RESTATEMENT TO FRAUD PREDICTION

A financial report restatement reflects errors in the previous financial statement, and thus it increases investors’ doubt about the credibility of the financial statement. The primary objective of this paper is to examine whether restatement announcements imply increased fraud risks in Chinese firms in the context that up to one quarter of listed companies have restated their financial reports in China, and explore the implications of the content, severity and reasons for restatements with respect to fraud. In this paper, firms with financial restatements prove to be more likely to be labeled as fraudulent by regulators in China. Second, the following results also are revealed: (1) financial statements, except balance sheet restatements, provide insights into the revelation of fraudulent behaviors, (2) the severity of restatements is positively correlated with future fraud disclosures, and (3) restatements due to negligence are positively correlated with future fraud occurrences. These results imply that restatement announcements and their different characteristics provide important information for detecting financial statement fraud.

Horseshoes, hand grenades, and regulatory enforcement: Close experience with potential sanctions and fraud deterrence

We investigate the deterrence effect of experience with regulatory enforcement on fraud in a unique natural setting. Using ride-level data on New York City taxicab drivers, we identify drivers who fraudulently overcharge customers and pair them with the outcomes of drivers’ experience with regulatory enforcement (taxi court). We examine whether drivers’ experience with the taxi court, specifically whether the taxi court found them guilty or not guilty of fraud, affects their subsequent fraud. Interestingly, we find that the effect of experience with the regulatory enforcement on the likelihood of future fraud depends on the verdict received. Consistent with an impact bias in drivers’ affective forecasting, drivers who are found guilty (not guilty) are more (less) likely to commit fraud than similar drivers without recent taxi court experience. Our results have implications for academics and policymakers by showing that sometimes the threat of enforcement can be more effective at deterring future fraud than the actual enforcement itself.

A Review on Satyam Computer Failure Lessons for Corporate Governance and World

Satyam scam has been the greatest scam in the history of corporate world of the India. Satyam is the fourth largest IT Company in India. The CEO of the company has made a scam of around $2 billion; it appears that corporate accounting fraud is a major problem that is increasing both in its frequency and severity. The Satyam scam shockwaves also impacted the entire auditing structure of Indian Corporate world. The scam has added to a notorious list of companies involved in fraudulent financial activities, one that includes such names as Enron, WorldCom, Societe General, Parmalat, The increasing rate of white-collar crimes demands stiff penalties, exemplary punishments, and effective enforcement of law with the right spirit. The fraud committed by the founders of Satyam in 2009, is a testament to the fact ― the science of conduct is swayed in large by human greed, ambition, and hunger for power, money, fame and glory. In the aftermath of the Satyam scandal, that has tested the efficacy of corporate governance norms in India; the existing framework fails to satisfactorily address the shortcomings in the prevailing regime. The Satyam scandal highlights the importance of securities laws and corporate legislation in emerging markets. Indeed, Satyam fraud spurred the government of India to tighten the corporate legislation norms to prevent recurrence of similar frauds in future. Satyam scandal has had a deep impact on how we construe “Corporate Governance”. Though it is well recognized that corporate governance is about “transparency”, “efficacy” and “accountability” but now the concerns are about the changing paradigm of corporate governance. Thus, major financial reporting frauds need to be studied for “lessons-learned” and “strategies-to-follow” to reduce the incidents of such frauds in the future.

The Comprehensive Model of Whistle-Blowing, Forensic Audit, Audit Investigation, and Fraud Detection

Fraud is a problem that has never been resolved from year to year. One type of fraud that often occurs in the government sector is corruption. Various attempts have been made by the government to detect fraud, but the efforts that have been made have not been effective in reducing fraud. This study aims to propose a conceptual model for detecting fraud by linking whistle-blowing, forensic audits, and investigation audits. The conceptual model of this research is expected to provide input to managers and decision makers regarding the factors that need to be considered in improving fraud detection so that in the future fraud prevention methods will be better and better.