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The prediction of the valuation of the “quality” of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of machine learning methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the “actual” financial performance of the business activity. Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses machine learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies machine learning models to identify “quality” characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students.
It is with immense pleasure that we extend a warm welcome to all of you to the recently concluded conference, international conference on Advances in Science, Technology and Management (ICOSTEM 2023) which took place from November 24 – 27, 2023, in the picturesque Maldives, Male. This significant event focused on the “Recent Technological Advances in Engineering and Management” with special sessions on Applied Sciences, Management and Engineering.
This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
The ever-evolving field of management in today's corporate world is marked by constant disruptions and turbulence. The emergence of Artificial Intelligence (AI) and Emotional Intelligence (EI) presents opportunities for automation, optimization, and effective leadership, but it also raises concerns about job displacement and the need to bridge the gap between these two domains. AI and Emotional Intelligence for Modern Business Management: Bridging the Gap and Nurturing Success offers solutions to closing the knowledge gap. This book provides comprehensive insights and practical strategies to academic scholars, researchers, practitioners, educators, and students. Targeting a diverse audience, this book serves as a solution-oriented resource for navigating the complexities of AI and EI in business management. By addressing both AI and EI, the book equips readers with the necessary tools to integrate these domains seamlessly into modern business management practices, stimulating informed discussions, inspiring innovative approaches, and fostering a deeper understanding of the opportunities and challenges posed by these emerging fields.
Despite the evolution of corporate governance in the last 30 years, corporate scandals have not stopped appearing in the media and academic documents. Therefore, this book presents a multidisciplinary study of corporate governance, as its mechanisms to reduce conflicts of interest and risk management must act as preventers of ethical and financial problems. The number of corporate scandals began to grow in the 1960s and peaked in the 1990s. From the first decade of the 2000s onwards, a remarkable evolution has taken place in the regulation market. However, new scandals continued to take place including the Subprime Crisis of 2008. New concepts such as corporate social responsibility (CRS), independence, gender diversity, and shell companies were incorporated. Until 2008 the scandals were mainly financial. Now, cases of corruption, environmental accidents, unsafe working conditions, child labor, and the political influence of power are increasing, which this book intends to address. It is critical to explore methodologies that allow collaboration among companies, regulatory entities, and those that guide their behavior and to ensure that they are consistent with the values of ethics, legality, disclosure, social responsibility, and accountability. Addressing Corporate Scandals and Transgressions Through Governance and Social Responsibility examines the tools of management and control that can be used as enforcement mechanisms of corporate governance and social responsibility and provides critical research on how to improve, discuss, and develop theories around fraud, corruption, ethics, corporate governance, and corporate social responsibility. Covering topics such as corporate scandal, human rights, and business fraud, this publication is ideal for corporate governance and social responsibility professionals such as accountants, auditors, tax officers, counsellors, directors, and managers as well as researchers, investors, and regulatory bodies and authorities.
Corruption is a phenomenon as old as civilization itself within the history of humanity, and it has presented itself in society with different intensities and various nuances. Many authors have described corruption as the action and effect of corrupting or becoming corrupted, but it also includes the use of the functions and means of organizations (public or private) for economic benefit or some other form of benefit. Corruption has thus become one of the main threats to democracy and governance because the principles of good governance are violated and the ethical precepts within society are defied. Management Strategies and Tools for Addressing Corruption in Public and Private Organizations explores the phenomenon of corruption in its entire context, analyzes it as dysfunctionality in the managerial practice of public and private organizations, and provides methods for monitoring, treating, and prevention. Covering topics such as anti-corruption organizational structure, rehabilitation systems, and shadow economy, this book is ideal for academicians, students, government officials, public and private organizations, and more.
When the COVID-19 pandemic caused a halt in global society, many business leaders found themselves unprepared for the unprecedented change that swept across industry. Whether the need to shift to remote work or the inability to safely conduct business during a global pandemic, many businesses struggled in the transition to the “new normal.” In the wake of the pandemic, these struggles have created opportunities to study how businesses navigate these times of crisis. The Research Anthology on Business Continuity and Navigating Times of Crisis discusses the strategies, cases, and research surrounding business continuity throughout crises such as pandemics. This book analyzes business operations and the state of the economy during times of crisis and the leadership involved in recovery. Covering topics such as crisis management, entrepreneurship, and business sustainability, this four-volume comprehensive major reference work is a valuable resource for managers, CEOs, business leaders, entrepreneurs, professors and students of higher education, researchers, and academicians.
The purpose of this dissertation was to study why corporate fraud detection models are often met with skepticism by industry practitioners despite a vast literature supporting their use. This dissertation examined the parsimonious standards in the academic literature for corporate fraud detection and included the latest studies that introduced ideas from Benford's Law and machine learning algorithms. The study of corporate fraud detection models is important because academic literature is relied upon by industry practitioners and government regulators including the Securities and Exchange Commission. This paper starts with a critique that was recently published in Econ Journal Watch. This critique examined the results of a paper recently published in the Journal of Accounting Research applying machine learning to the detection of accounting fraud. Afterwards, I applied the most popular ensemble boosting algorithm in machine learning known as XGBoost to a comprehensive sample of financial ratios and variables. In addition to this model, I ran a horserace with the other models from the extant literature. Results showed that the F-Score (Dechow, et al. 2011) stood up quite well against the machine learning models. Interestingly, a univariate screen on sales growth performed about as well as more complicated methodologies at the top of the probability distribution. Finally, I provided a discussion based on a Bayesian analysis that illustrated why practitioners find fraud detection difficult.
Globalization is a multi-dimensional concept reflecting the increased economic, social, cultural, and political integration of countries. There has been no pinpointed consensus on the history of globalization; however, the globalization process has gained significant speed as of the 1980s in combination with liberalization. Many countries have removed or loosened barriers over the international flows of goods, services, and production factors. In this context, both liberalization and globalization have led to considerable institutional, economic, social, cultural, and political changes in the world. The liberalization and globalization processes have affected economic units, institutions, cultures, social lives, and national and international politics. The Handbook of Research on Institutional, Economic, and Social Impacts of Globalization and Liberalization provides a comprehensive evaluation of the institutional, economic, and social impacts of globalization and liberalization processes across the world. While highlighting topics like economics, finance, business, and public administration, this book is ideally intended for government officials, policymakers, practitioners, stakeholders, researchers, and academicians interested in the international impacts of globalization and liberalization across a variety of different domains.