Conceptual Structure of Fraud Research and Its Dynamics

The concept of fraud, its antecedents and outcomes as well as its detection and prevention, have been discussed by both academics and practitioners for decades. The scope and complexity of the concept of fraud attracts scholars from diverse disciplines. The purpose of our study is to gain a broader understanding of how fraud is viewed in the compendium of academic literature. Using semantic network analysis method, we explore the structure of fraud-related research and analyze the internal connections among the current areas of interest for fraud researchers. We are particularly interested in the relationship between the mainstream financial accounting and audit research and the field of information systems and technology. Our work makes a big stride toward the understanding of current state of fraud-related research. The interdisciplinary semantic map of keywords and subject terms helps understand the trends in fraud scholarship, identify gaps and propose directions for future research

Literature Review of Credit Card Fraud Detection with Machine Learning

This thesis presents a comprehensive examination of the field of credit card fraud detection, aiming to offer a thorough understanding of its evolution and nuances. Through a synthesis of various studies, methodologies, and technologies, this research strives to provide a holistic perspective on the subject, shedding light on both its strengths and limitations. In the realm of credit card fraud detection, a range of methods and combinations have been explored to enhance effectiveness. This research reviews several noteworthy approaches, including Genetic Algorithms (GA) coupled with Random Forest (GA-RF), Decision Trees (GA-DT), and Artificial Neural Networks (GA-ANN). Additionally, the study delves into outlier score definitions, considering different levels of granularity, and their integration into a supervised framework. Moreover, it discusses the utilization of Artificial Neural Networks (ANNs) in federated learning and the incorporation of Generative Adversarial Networks (GANs) with Modified Focal Loss and Random Forest as the base machine learning algorithm. These methods, either independently or in combination, represent some of the most recent developments in credit card fraud detection, showcasing their potential to address the evolving landscape of digital financial threats. The scope of this literature review encompasses a wide range of sources, including research articles, academic papers, and industry reports, spanning multiple disciplines such as computer science, data science, artificial intelligence, and cybersecurity. The review is organized to guide readers through the progression of credit card fraud detection, commencing with foundational concepts and advancing toward the most recent developments. In today's digital financial landscape, the need for robust defense mechanisms against credit card fraud is undeniable. By critically assessing the existing literature, recognizing emerging trends, and evaluating the effectiveness of various detection methods, this thesis aims to contribute to the knowledge pool within the credit card fraud detection domain. The insights gleaned from this comprehensive review will not only benefit researchers and practitioners but also serve as a roadmap for the enhancement of more adaptive and resilient fraud detection systems. As the ongoing battle between fraudsters and defenders in the financial realm continues to evolve, a deep understanding of the current landscape becomes an asset. This literature review aspires to equip readers with the insights needed to address the dynamic challenges associated with credit card fraud detection, fostering innovation and resilience in the pursuit of secure and trustworthy financial transactions

Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries

It is estimated that between $600 and$850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to$175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program

Intelligent Financial Fraud Detection Practices: An Investigation

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014

Immigration of Foreign Workers: Labor Tests and Protections

[Excerpt] Economic indicators confirm that the U.S. economy sunk into a recession in December 2007. Although some economic indicators suggest that growth has resumed, unemployment remains high and is projected to remain so for some time. Historically, international migration ebbs during economic crises; for example, immigration to the United States was at its lowest levels during the Great Depression. While preliminary statistical trends hint at a slowing of migration pressures, it remains unclear how the current economic recession will affect immigration. Addressing these contentious policy reforms against the backdrop of economic crisis sharpens the social and business cleavages and narrows the range of options. Some employers maintain that they continue to need the “best and the brightest” workers, regardless of their country of birth, to remain competitive in a worldwide market and to keep their firms in the United States. While support for increasing employment-based immigration may be dampened by the high levels of unemployment, proponents argue that the ability to hire foreign workers is an essential ingredient for economic growth. Those opposing increases in foreign workers assert that such expansions—particularly during a period of high unemployment—would have a deleterious effect on salaries, compensation, and working conditions of U.S. workers. Others question whether the United States should continue to issue foreign worker visas (particularly temporary visas) during a recession and suggest that a moratorium on such visas might be prudent. The number of foreign workers entering the United States legally has notably increased over the past decade. The number of employment-based legal permanent residents (LPRs) grew from under 100,000 in FY1994 to over 250,000 in FY2005, and stood at 166,511 in 2008. The number of visas issued to employment-based temporary non-immigrants rose from just under 600,000 in FY1994 to approximately 1.3 million in FY2007. In FY2009, the number of visas issued to employment-based temporary nonimmigrants dropped slightly to 1.1 million. The Immigration and Nationality Act (INA) bars the admission of any alien who seeks to enter the U.S. to perform skilled or unskilled labor, unless it is determined that (1) there are not sufficient U.S. workers who are able, willing, qualified, and available; and (2) the employment of the alien will not adversely affect the wages and working conditions of similarly employed workers in the United States. The foreign labor certification program in the U.S. Department of Labor (DOL) is responsible for ensuring that foreign workers do not displace or adversely affect working conditions of U.S. workers. The 111th Congress has addressed one element of the labor market test for foreign workers issue in §1611 of P.L. 111-5, the American Recovery and Reinvestment Act of 2009, which requires companies receiving Troubled Asset Relief Program (TARP) funding to comply with the more rigorous labor market rules of H-1B dependent companies if they hire foreign workers on H-1B visas. Also, §524 of division D of the Consolidated Appropriations Act, 2010 (P.L. 111-117) authorized the Department of Labor to use its share of the H-1B, H-2B, and L Fraud Prevention and Detection fees to conduct wage and hour enforcement of industries more likely to employ any type of nonimmigrants (not just H-1B, H-2B or L visaholders). This report does not track legislation and will be updated if policies are revised

Data Mining in Health-Care: Issues and a Research Agenda

While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare

Audit and AI: Can Artificial Intelligence Restore Public Trust?

Due to the fallout from a series of corporate fraud scandals in the late 2000s, the auditing world has lost much of the public trust that is very important to the profession. Much of the value of an audit opinion is determined by the trust the public places in the auditors behind the opinion. Without trust in the auditors, the audit opinion has very little value. The recent increase in the usage of artificial intelligence (AI) in many industries presents a solution to the problem of auditors. Increased usage of AI in the audit process has the potential to better meet public demand for an audit as well as restore public trust