A savvy examination of where people and value meet, creating the opportunity for fraud An essential reference for all business professionals, Detecting Fraud in Organizations: Techniques, Tools, and Resources explains the process of how people commit fraud, as well as how to prevent and stop fraud from occurring in your organization. Organized by business processes which succinctly describe how fraud manifests itself on a daily basis, the authors explain ways in which everyone can help guard against fraud by familiarizing themselves with its building blocks and methods used to perpetrate and conceal it. Filled with situational examples the book is accompanied by a website featuring fraud simulations, business process maps, and other useful tools for combating fraud. Focuses on the people who perpetrate fraud and those who are tasked with preventing and detecting it Uniquely organized by business processes for more relevance and easier understanding by those people working within organizations Shows how subtle factors play a large role in identifying and ferreting out fraud in addition to the traditional knowledge of fraud schemes giving people and organizations the edge they need to be successful in prevention and deterrence Companion website includes additional fraud simulations, business process maps, and useful tools The price of fraud can be devastating to your business. Detecting Fraud in Organizations: Techniques, Tools, and Resources equips you and others in your organization with essential information and tools necessary to proactively catch fraud, reduce losses, improve efficiencies and develop actionable controls.

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.

Lessons can be learned from major fraud cases. Whether the victim is a company, public agency, nonprofit, foundation, or charity, there is a high likelihood that many of these frauds could have been prevented or detected sooner if early Red Flag warning signs had been identified and acted upon. Fraud Prevention and Detection: Warning Signs and the

Practical examples, sample reports, best practices and recommendations to help you deter, detect, and prevent financial statement fraud Financial statement fraud (FSF) continues to be a major challenge for organizations worldwide. Financial Statement Fraud: Prevention and Detection, Second Edition is a superior reference providing you with an up-to-date understanding of financial statement fraud, including its deterrence, prevention, and early detection. You will find A clear description of roles and responsibilities of all those involved in corporate governance and the financial reporting process to improve the quality, reliability and transparency of financial information. Sample reports, examples, and documents that promote a real-world understanding of incentives, opportunities, and rationalizations Emerging corporate governance reforms in the post-SOX era, including provisions of the SOX Act, global regulations and best practices, ethical considerations, and corporate governance principles Practical examples and real-world "how did this happen" discussions that provide valuable insight for corporate directors and executives, auditors, managers, supervisory personnel and other professionals saddled with anti-fraud responsibilities Expert advice from the author of Corporate Governance and Ethics and coauthor of the forthcoming Wiley textbook, White Collar Crime, Fraud Examination and Financial Forensics Financial Statement Fraud, Second Edition contains recommendations from the SEC Advisory Committee to reduce the complexity of the financial reporting process and improving the quality of financial reports.

Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.

Step-by-step guidance for board members and executives on preventing and detecting accounting fraud In the wake of highly publicized allegations of accounting irregularities and fraudulent financial reporting that are shaking up today's corporate community, Financial Fraud Prevention and Detection provides a step-by-step guide to how these crises can envelop a company and how to prevent them from happening in the first place. It is written for almost everyone involved: outside directors, audit committee members, senior executives, CFOs, CPAs, in-house lawyers, and outside law firms. Provides a blueprint for Fraud Prevention and Detection for corporate executives Presents step-by-step guidance to corporate boards and C-suite executives on managing the threat of accounting fraud Prepares directors and executives for the possibility of accounting irregularities Answers the question of how accounting fraud starts—and grows With solid strategies for prevention of accounting fraud as well as a process to follow when fraud has been discovered, Financial Fraud Prevention and Detection vividly explores the corporate environment that causes fraud, how it spreads, the kind of crises it can create for a company, and the best ways to deal with it.

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Business Theft and Fraud: Detection and Prevention offers a broad perspective on business-related theft, providing a detailed discussion of numerous avenues of theft, including internal and external fraud, organized retail crime, mortgage fraud, cyber fraud, and extortion. Combining current research and the author’s extensive experience with loss prevention and security, this professional text identifies industry trouble areas and offers techniques to combat business theft, such as how to identify sales underreporting, track sales by shifts, and educate employees on computer-related fraud. This publication is critical for those involved with loss prevention, security, or criminal justice. Business Theft and Fraud’s accessible, franchise-oriented scope will help many professionals identify and thwart threats in the evolving business world.

Detect fraud faster—no matter how well hidden—with IDEA automation Fraud and Fraud Detection takes an advanced approach to fraud management, providing step-by-step guidance on automating detection and forensics using CaseWare's IDEA software. The book begins by reviewing the major types of fraud, then details the specific computerized tests that can detect them. Readers will learn to use complex data analysis techniques, including automation scripts, allowing easier and more sensitive detection of anomalies that require further review. The companion website provides access to a demo version of IDEA, along with sample scripts that allow readers to immediately test the procedures from the book. Business systems' electronic databases have grown tremendously with the rise of big data, and will continue to increase at significant rates. Fraudulent transactions are easily hidden in these enormous datasets, but Fraud and Fraud Detection helps readers gain the data analytics skills that can bring these anomalies to light. Step-by-step instruction and practical advice provide the specific abilities that will enhance the audit and investigation process. Readers will learn to: Understand the different areas of fraud and their specific detection methods Identify anomalies and risk areas using computerized techniques Develop a step-by-step plan for detecting fraud through data analytics Utilize IDEA software to automate detection and identification procedures The delineation of detection techniques for each type of fraud makes this book a must-have for students and new fraud prevention professionals, and the step-by-step guidance to automation and complex analytics will prove useful for even experienced examiners. With datasets growing exponentially, increasing both the speed and sensitivity of detection helps fraud professionals stay ahead of the game. Fraud and Fraud Detection is a guide to more efficient, more effective fraud identification.