Fraud Analytics Using Descriptive Predictive And Social Network Techniques

Author: Bart Baesens
Publisher: John Wiley & Sons
ISBN: 1119133122
Size: 40.74 MB
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Fraud Analytics Using Descriptive Predictive And Social Network Techniques from the Author: Bart Baesens. 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.

Fraud Analytics

Author: Delena D. Spann
Publisher: John Wiley & Sons
ISBN: 1118282736
Size: 57.15 MB
Format: PDF, ePub, Mobi
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Fraud Analytics from the Author: Delena D. Spann. Proven guidance for expertly using analytics in fraud examinations, financial analysis, auditing and fraud prevention Fraud Analytics thoroughly reveals the elements of analysis that are used in today's fraud examinations, fraud investigations, and financial crime investigations. This valuable resource reviews the types of analysis that should be considered prior to beginning an investigation and explains how to optimally use data mining techniques to detect fraud. Packed with examples and sample cases illustrating pertinent concepts in practice, this book also explores the two major data analytics providers: ACL and IDEA. Looks at elements of analysis used in today's fraud examinations Reveals how to use data mining (fraud analytic) techniques to detect fraud Examines ACL and IDEA as indispensable tools for fraud detection Includes an abundance of sample cases and examples Written by Delena D Spann, Board of Regent (Emeritus) for the Association of Certified Fraud Examiners (ACFE), who currently serves as Advisory Board Member of the Association of Certified Fraud Examiners, Board Member of the Education Task Force of the Association of Certified Anti-Money Laundering Specialists ASIS International (Economic Crime Council) and Advisory Board Member of the Robert Morris University (School of Business), Fraud Analytics equips you with authoritative fraud analysis techniques you can put to use right away.

Fraud And Fraud Detection

Author: Sunder Gee
Publisher: John Wiley & Sons
ISBN: 1118779665
Size: 20.89 MB
Format: PDF, Mobi
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Fraud And Fraud Detection from the Author: Sunder Gee. 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.

Real Time Fraud Detection Analytics On Ibm System Z

Author: Mike Ebbers
Publisher: IBM Redbooks
ISBN: 0738437638
Size: 80.24 MB
Format: PDF, Mobi
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Real Time Fraud Detection Analytics On Ibm System Z from the Author: Mike Ebbers. Payment fraud can be defined as an intentional deception or misrepresentation that is designed to result in an unauthorized benefit. Fraud schemes are becoming more complex and difficult to identify. It is estimated that industries lose nearly $1 trillion USD annually because of fraud. The ideal solution is where you avoid making fraudulent payments without slowing down legitimate payments. This solution requires that you adopt a comprehensive fraud business architecture that applies predictive analytics. This IBM® Redbooks® publication begins with the business process flows of several industries, such as banking, property/casualty insurance, and tax revenue, where payment fraud is a significant problem. This book then shows how to incorporate technological advancements that help you move from a post-payment to pre-payment fraud detection architecture. Subsequent chapters describe a solution that is specific to the banking industry that can be easily extrapolated to other industries. This book describes the benefits of doing fraud detection on IBM System z®. This book is intended for financial decisionmakers, consultants, and architects, in addition to IT administrators.

Benford S Law

Author: Mark Nigrini
Publisher: John Wiley & Sons
ISBN: 1118286863
Size: 54.24 MB
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Benford S Law from the Author: Mark Nigrini. A powerful new tool for all forensic accountants, or anyone who analyzes data that may have been altered Benford's Law gives the expected patterns of the digits in the numbers in tabulated data such as town and city populations or Madoff's fictitious portfolio returns. Those digits, in unaltered data, will not occur in equal proportions; there is a large bias towards the lower digits, so much so that nearly one-half of all numbers are expected to start with the digits 1 or 2. These patterns were originally discovered by physicist Frank Benford in the early 1930s, and have since been found to apply to all tabulated data. Mark J. Nigrini has been a pioneer in applying Benford's Law to auditing and forensic accounting, even before his groundbreaking 1999 Journal of Accountancy article introducing this useful tool to the accounting world. In Benford's Law, Nigrini shows the widespread applicability of Benford's Law and its practical uses to detect fraud, errors, and other anomalies. Explores primary, associated, and advanced tests, all described with data sets that include corporate payments data and election data Includes ten fraud detection studies, including vendor fraud, payroll fraud, due diligence when purchasing a business, and tax evasion 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 returns and the charitable gifts of Lehman Brothers to tax evasion and number invention Benford's Law has 250 figures and uses 50 interesting authentic and fraudulent real-world data sets to explain both theory and practice, and concludes with an agenda and directions for future research. The companion website adds additional information and resources.

Using Analytics To Detect Possible Fraud

Author: Pamela S. Mantone
Publisher: John Wiley & Sons
ISBN: 1118715985
Size: 47.68 MB
Format: PDF, ePub, Mobi
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Using Analytics To Detect Possible Fraud from the Author: Pamela S. Mantone. Detailed tools and techniques for developing efficiency and effectiveness in forensic accounting Using Analytics to Detect Possible Fraud: Tools and Techniques is a practical overview of the first stage of forensic accounting, providing a common source of analytical techniques used for both efficiency and effectiveness in forensic accounting investigations. The book is written clearly so that those who do not have advanced mathematical skills will be able to understand the analytical tests and use the tests in a forensic accounting setting. It also includes case studies and visual techniques providing practical application of the analytical tests discussed. Shows how to develop both efficiency and effectiveness in forensic accounting Provides information in such a way that non-practitioners can easily understand Written in plain language: advanced mathematical skills are not required Features actual case studies using analytical tests Essential reading for every investor who wants to prevent financial fraud, Using Analytics to Detect Possible Fraud allows practitioners to focus on areas that require further investigative techniques and to unearth deceptive financial reporting before it's too late.

Analytics In A Big Data World

Author: Bart Baesens
Publisher: John Wiley & Sons
ISBN: 1118892747
Size: 37.42 MB
Format: PDF, ePub, Mobi
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Analytics In A Big Data World from the Author: Bart Baesens. The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

Data Mining For Intelligence Fraud Criminal Detection

Author: Christopher Westphal
Publisher: CRC Press
ISBN: 9781420067248
Size: 70.24 MB
Format: PDF, Docs
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Data Mining For Intelligence Fraud Criminal Detection from the Author: Christopher Westphal. In 2004, the Government Accountability Office provided a report detailing approximately 200 government-based data-mining projects. While there is comfort in knowing that there are many effective systems, that comfort isn’t worth much unless we can determine that these systems are being effectively and responsibly employed. Written by one of the most respected consultants in the area of data mining and security, Data Mining for Intelligence, Fraud & Criminal Detection: Advanced Analytics & Information Sharing Technologies reviews the tangible results produced by these systems and evaluates their effectiveness. While CSI-type shows may depict information sharing and analysis that are accomplished with the push of a button, this sort of proficiency is more fiction than reality. Going beyond a discussion of the various technologies, the author outlines the issues of information sharing and the effective interpretation of results, which are critical to any integrated homeland security effort. Organized into three main sections, the book fully examines and outlines the future of this field with an insider’s perspective and a visionary’s insight. Section 1 provides a fundamental understanding of the types of data that can be used in current systems. It covers approaches to analyzing data and clearly delineates how to connect the dots among different data elements Section 2 provides real-world examples derived from actual operational systems to show how data is used, manipulated, and interpreted in domains involving human smuggling, money laundering, narcotics trafficking, and corporate fraud Section 3 provides an overview of the many information-sharing systems, organizations, and task forces as well as data interchange formats. It also discusses optimal information-sharing and analytical architectures Currently, there is very little published literature that truly defines real-world systems. Although politics and other factors all play into how much one agency is willing to support the sharing of its resources, many now embrace the wisdom of that path. This book will provide those individuals with an understanding of what approaches are currently available and how they can be most effectively employed.

Analytics For Insurance

Author: Tony Boobier
Publisher: John Wiley & Sons
ISBN: 1119141079
Size: 64.58 MB
Format: PDF
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Analytics For Insurance from the Author: Tony Boobier. The business guide to Big Data in insurance, with practical application insight Big Data and Analytics for Insurers is the industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. Written from a non-IT perspective, this book focusses less on the architecture and technical details, instead providing practical guidance on translating analytics into target delivery. The discussion examines implementation, interpretation, and application to show you what Big Data can do for your business, with insights and examples targeted specifically to the insurance industry. From fraud analytics in claims management, to customer analytics, to risk analytics in Solvency 2, comprehensive coverage presented in accessible language makes this guide an invaluable resource for any insurance professional. The insurance industry is heavily dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential – yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. Understand what Big Data is and what it can do Delve into Big Data's specific impact on the insurance industry Learn how advanced analytics can revolutionise the industry Bring Big Data out of IT and into strategy, management, marketing, and more Big Data and analytics is changing business – but how? The majority of Big Data guides discuss data collection, database administration, advanced analytics, and the power of Big Data – but what do you actually do with it? Big Data and Analytics for Insurers answers your questions in real, everyday business terms, tailored specifically to the insurance industry's unique needs, challenges, and targets.

Forensic Analytics

Author: Mark Nigrini
Publisher: John Wiley & Sons
ISBN: 9781118087633
Size: 79.73 MB
Format: PDF, ePub
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Forensic Analytics from the Author: Mark Nigrini. Discover how to detect fraud, biases, or errors in your data using Access or Excel With over 300 images, Forensic Analytics reviews and shows how twenty substantive and rigorous tests can be used to detect fraud, errors, estimates, or biases in your data. For each test, the original data is shown with the steps needed to get to the final result. The tests range from high-level data overviews to assess the reasonableness of data, to highly focused tests that give small samples of highly suspicious transactions. These tests are relevant to your organization, whether small or large, for profit, nonprofit, or government-related. Demonstrates how to use Access, Excel, and PowerPoint in a forensic setting Explores use of statistical techniques such as Benford's Law, descriptive statistics, correlation, and time-series analysis to detect fraud and errors Discusses the detection of financial statement fraud using various statistical approaches Explains how to score locations, agents, customers, or employees for fraud risk Shows you how to become the data analytics expert in your organization Forensic Analytics shows how you can use Microsoft Access and Excel as your primary data interrogation tools to find exceptional, irregular, and anomalous records.