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These hearing transcripts present testimony on the high risks and emerging fraud in several areas of the federal government, including the Student Loan Program of the Education Department (ED), the Multifamily Housing Program of the Department of Housing and Urban Development (HUD), and Internal Revenue Service (IRS) tax return filing. Testimony was heard from concerned senators and government officials responsible for risk management and fraud in these departments and agencies. Current and possible solutions to risk management and fraud were discussed. Opening and/or prepared statements were given by: Senators John Glenn, Byron L. Dorgan, Jim Sasser, William S. Cohen, and William V. Roth, Jr. Testimony was heard from: (1) the special assistant to the comptroller general and the director of tax systems issues, General Accounting Office (GAO); (2) the commissioner, deputy commissioner, and other officials of the IRS; (3) the deputy secretary, inspector general, and other officials of ED; and (4) the inspector general, assistant inspector general for audit, and other officials of HUD. An appendix contains the prepared statements of several witnesses, along with written questions and answers from officials of GAO, ED, HUD, and the Department of Treasury. (MDM)
Policymakers and program managers are continually seeking ways to improve accountability in achieving an entity's mission. A key factor in improving accountability in achieving an entity's mission is to implement an effective internal control system. An effective internal control system helps an entity adapt to shifting environments, evolving demands, changing risks, and new priorities. As programs change and entities strive to improve operational processes and implement new technology, management continually evaluates its internal control system so that it is effective and updated when necessary. Section 3512 (c) and (d) of Title 31 of the United States Code (commonly known as the Federal Managers' Financial Integrity Act (FMFIA)) requires the Comptroller General to issue standards for internal control in the federal government.
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
An essential resource on artificial intelligence ethics for business leaders In Trustworthy AI, award-winning executive Beena Ammanath offers a practical approach for enterprise leaders to manage business risk in a world where AI is everywhere by understanding the qualities of trustworthy AI and the essential considerations for its ethical use within the organization and in the marketplace. The author draws from her extensive experience across different industries and sectors in data, analytics and AI, the latest research and case studies, and the pressing questions and concerns business leaders have about the ethics of AI. Filled with deep insights and actionable steps for enabling trust across the entire AI lifecycle, the book presents: In-depth investigations of the key characteristics of trustworthy AI, including transparency, fairness, reliability, privacy, safety, robustness, and more A close look at the potential pitfalls, challenges, and stakeholder concerns that impact trust in AI application Best practices, mechanisms, and governance considerations for embedding AI ethics in business processes and decision making Written to inform executives, managers, and other business leaders, Trustworthy AI breaks new ground as an essential resource for all organizations using AI.
An in-depth scrutiny into the American savings and loan financial crisis in the 1980s. The authors come to conclusions about the deliberate nature of this financial fraud and the leniency of the criminal justice system on these 'Gucci-clad white-collar criminals'.
Food Fraud: A Global Threat With Public Health and Economic Consequences serves as a practical resource on the topic of food fraud prevention and compliance with regulatory and industry standards. It includes a brief overview of the history of food fraud, current challenges, and vulnerabilities faced by the food industry, and requirements for compliance with regulatory and industry standards on mitigating vulnerability to food fraud, with a focus on the Global Food Safety Initiative (GFSI) Benchmarking Requirements. The book also provides individual chapters dedicated to specific commodities or sectors of the food industry known to be affected by fraud, with a focus on specific vulnerabilities to fraud, the main types of fraud committed, analytical methods for detection, and strategies for mitigation. The book provides an overview of food fraud mitigation strategies applicable to the food industry and guidance on how to start the process of mitigating the vulnerability to food fraud. The intended audience for this book includes food industry members, food safety and quality assurance practitioners, food science researchers and professors, students, and members of regulatory agencies. - Presents industry and regulatory standards for mitigating vulnerability to food fraud including Global Food Safety Initiative (GFSI) Benchmarking Requirements - Provides tools and resources to comply with industry and regulatory standards, including steps for developing a food fraud vulnerability assessment and mitigation plan - Contains detailed, commodity-specific information on the major targets of food fraud, including specific vulnerabilities to fraud, analytical methods, and strategies for mitigation
Fraud has become a challenging phenomena affecting economies worldwide. Anti-fraud measures are an integral part of today’s management practices and have found their way into business education. Yet in developing countries these topics have long been neglected and only limited research has been conducted in this area. This book fills an essential gap by analyzing the impact of fraud on developing economies, describing successful anti-fraud methods and featuring cases that exemplify the measures described. The book features contributions by outstanding experts in the field and is intended for academic readers with a special interest in fraud research.
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.
2018 International Conference on Artificial Intelligence and Big Data (ICAIBD 2018) will take place on May 26 28, 2018 in Chengdu, China It is sponsored by Sichuan Province Computer Federation and College of Computer Science at Sichuan University, China This conference provides you opportunity to meet with academicians as well as practitioners in the fields of Artificial Intelligence and Big Data from all over the world, and get the latest insights from every area of Artificial Intelligence and Big Data theory and practice