Fraud Detection

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

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.

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.

Praise for Computer-Aided Fraud Prevention and Detection: A Step-by-Step Guide "A wonderful desktop reference for anyone trying to move from traditional auditing to integrated auditing. The numerous case studies make it easy to understand and provide a how-to for those?seeking to implement automated tools including continuous assurance. Whether you are just starting down the path or well on your way, it is a valuable resource." -Kate M. Head, CPA, CFE, CISA Associate Director, Audit and Compliance University of South Florida "I have been fortunate enough to learn from Dave's work over the last fifteen years, and this publication is no exception. Using his twenty-plus years of experience, Dave walks through every aspect of detecting fraud with a computer from the genesis of the act to the mining of data for its traces and its ultimate detection. A complete text that first explains how one prevents and detects fraud regardless of technology and then shows how by automating such procedures, the examiners' powers become superhuman." -Richard B. Lanza, President, Cash Recovery Partners, LLC "Computer-Aided Fraud Prevention and Detection: A Step-by-Step Guide helps management and auditors answer T. S. Eliot's timeless question, 'Where is the knowledge lost in information?' Data analysis provides a means to mine the knowledge hidden in our information. Dave Coderre has long been a leader in educating auditors and others about Computer Assisted Audit Techniques. The book combines practical approaches with unique data analysis case examples that compel the readers to try the techniques themselves." -Courtenay Thompson Jr. Consultant, Courtenay Thompson & Associates

There has been an increase in awareness (and perhaps occurrence) of individual and organized cheating on tests. Recent reports of widespread problems with state student accountability tests and teacher certification testing have raised questions about the very validity of assessment programs. While there are several books that specifically detail the issues of test security cheating on assessments, few outline the statistical procedures used for detecting various types of potential test fraud and the associated research findings. Without a significant research literature base, the new generation of researchers will have little opportunity or incentive to improve on existing methods. Enlisting a variety of experts and scholars in different fields of testing, this edited volume expands on the current literature base by including examples of detailed research findings arrived at by statistical methodology. It also provides a synthesis of the current state of the art with regard to the statistical detection of testing infidelity, particularly for large-scale assessments. By presenting methods currently used by testing organizations and research on new methods, the volume offers an important forum for expanding the literature in this area.

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.

A powerful new tool for all forensic accountants, or anyone whoanalyzes data that may have been altered Benford's Law gives the expected patterns of the digits in thenumbers in tabulated data such as town and city populations orMadoff's fictitious portfolio returns. Those digits, in unaltereddata, will not occur in equal proportions; there is a large biastowards the lower digits, so much so that nearly one-half of allnumbers are expected to start with the digits 1 or 2. Thesepatterns were originally discovered by physicist Frank Benford inthe early 1930s, and have since been found to apply to alltabulated data. Mark J. Nigrini has been a pioneer in applyingBenford's Law to auditing and forensic accounting, even before hisgroundbreaking 1999 Journal of Accountancy article introducing thisuseful tool to the accounting world. In Benford's Law, Nigrinishows the widespread applicability of Benford's Law and itspractical uses to detect fraud, errors, and other anomalies. Explores primary, associated, and advanced tests, all describedwith data sets that include corporate payments data and electiondata Includes ten fraud detection studies, including vendor fraud,payroll fraud, due diligence when purchasing a business, and taxevasion Covers financial statement fraud, with data from Enron, AIG,and companies that were the target of hedge fund short sales Looks at how to detect Ponzi schemes, including data on Madoff,Waxenberg, and more Examines many other applications, from the Clinton tax returnsand the charitable gifts of Lehman Brothers to tax evasion andnumber invention Benford's Law has 250 figures and uses 50 interestingauthentic and fraudulent real-world data sets to explain boththeory and practice, and concludes with an agenda and directionsfor future research. The companion website adds additionalinformation and resources.

More and more data-driven companies are looking to adopt stream processing and streaming analytics. With this concise ebook, you’ll learn best practices for designing a reliable architecture that supports this emerging big-data paradigm. Authors Ted Dunning and Ellen Friedman (Real World Hadoop) help you explore some of the best technologies to handle stream processing and analytics, with a focus on the upstream queuing or message-passing layer. To illustrate the effectiveness of these technologies, this book also includes specific use cases. Ideal for developers and non-technical people alike, this book describes: Key elements in good design for streaming analytics, focusing on the essential characteristics of the messaging layer New messaging technologies, including Apache Kafka and MapR Streams, with links to sample code Technology choices for streaming analytics: Apache Spark Streaming, Apache Flink, Apache Storm, and Apache Apex How stream-based architectures are helpful to support microservices Specific use cases such as fraud detection and geo-distributed data streams Ted Dunning is Chief Applications Architect at MapR Technologies, and active in the open source community. He currently serves as VP for Incubator at the Apache Foundation, as a champion and mentor for a large number of projects, and as committer and PMC member of the Apache ZooKeeper and Drill projects. Ted is on Twitter as @ted_dunning. Ellen Friedman, a committer for the Apache Drill and Apache Mahout projects, is a solutions consultant and well-known speaker and author, currently writing mainly about big data topics. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics. Ellen is on Twitter as @Ellen_Friedman.