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Author: Leonard W. Vona Publisher: John Wiley & Sons ISBN: 111918679X Category : Business & Economics Languages : en Pages : 400
Book Description
Uncover hidden fraud and red flags using efficient data analytics Fraud Data Analytics Methodology addresses the need for clear, reliable fraud detection with a solid framework for a robust data analytic plan. By combining fraud risk assessment and fraud data analytics, you'll be able to better identify and respond to the risk of fraud in your audits. Proven techniques help you identify signs of fraud hidden deep within company databases, and strategic guidance demonstrates how to build data interrogation search routines into your fraud risk assessment to locate red flags and fraudulent transactions. These methodologies require no advanced software skills, and are easily implemented and integrated into any existing audit program. Professional standards now require all audits to include data analytics, and this informative guide shows you how to leverage this critical tool for recognizing fraud in today's core business systems. Fraud cannot be detected through audit unless the sample contains a fraudulent transaction. This book explores methodologies that allow you to locate transactions that should undergo audit testing. Locate hidden signs of fraud Build a holistic fraud data analytic plan Identify red flags that lead to fraudulent transactions Build efficient data interrogation into your audit plan Incorporating data analytics into your audit program is not about reinventing the wheel. A good auditor must make use of every tool available, and recent advances in analytics have made it accessible to everyone, at any level of IT proficiency. When the old methods are no longer sufficient, new tools are often the boost that brings exceptional results. Fraud Data Analytics Methodology gets you up to speed, with a brand new tool box for fraud detection.
Author: Leonard W. Vona Publisher: John Wiley & Sons ISBN: 111918679X Category : Business & Economics Languages : en Pages : 400
Book Description
Uncover hidden fraud and red flags using efficient data analytics Fraud Data Analytics Methodology addresses the need for clear, reliable fraud detection with a solid framework for a robust data analytic plan. By combining fraud risk assessment and fraud data analytics, you'll be able to better identify and respond to the risk of fraud in your audits. Proven techniques help you identify signs of fraud hidden deep within company databases, and strategic guidance demonstrates how to build data interrogation search routines into your fraud risk assessment to locate red flags and fraudulent transactions. These methodologies require no advanced software skills, and are easily implemented and integrated into any existing audit program. Professional standards now require all audits to include data analytics, and this informative guide shows you how to leverage this critical tool for recognizing fraud in today's core business systems. Fraud cannot be detected through audit unless the sample contains a fraudulent transaction. This book explores methodologies that allow you to locate transactions that should undergo audit testing. Locate hidden signs of fraud Build a holistic fraud data analytic plan Identify red flags that lead to fraudulent transactions Build efficient data interrogation into your audit plan Incorporating data analytics into your audit program is not about reinventing the wheel. A good auditor must make use of every tool available, and recent advances in analytics have made it accessible to everyone, at any level of IT proficiency. When the old methods are no longer sufficient, new tools are often the boost that brings exceptional results. Fraud Data Analytics Methodology gets you up to speed, with a brand new tool box for fraud detection.
Author: Bart Baesens Publisher: John Wiley & Sons ISBN: 1119133122 Category : Computers Languages : en Pages : 406
Book Description
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.
Author: Sunder Gee Publisher: John Wiley & Sons ISBN: 1118779657 Category : Business & Economics Languages : en Pages : 358
Book Description
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.
Author: Delena D. Spann Publisher: John Wiley & Sons ISBN: 1118282736 Category : Business & Economics Languages : en Pages : 176
Book Description
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.
Author: Bart Baesens Publisher: John Wiley & Sons ISBN: 1119146836 Category : Computers Languages : en Pages : 406
Book Description
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.
Author: Gerard Blokdyk Publisher: Createspace Independent Publishing Platform ISBN: 9781979909075 Category : Languages : en Pages : 134
Book Description
What potential environmental factors impact the Data analysis techniques for fraud detection effort? What key business process output measure(s) does Data analysis techniques for fraud detection leverage and how? Who sets the Data analysis techniques for fraud detection standards? Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data analysis techniques for fraud detection? Among the Data analysis techniques for fraud detection product and service cost to be estimated, which is considered hardest to estimate? This easy Data analysis techniques for fraud detection self-assessment will make you the dependable Data analysis techniques for fraud detection domain specialist by revealing just what you need to know to be fluent and ready for any Data analysis techniques for fraud detection challenge. How do I reduce the effort in the Data analysis techniques for fraud detection work to be done to get problems solved? How can I ensure that plans of action include every Data analysis techniques for fraud detection task and that every Data analysis techniques for fraud detection outcome is in place? How will I save time investigating strategic and tactical options and ensuring Data analysis techniques for fraud detection opportunity costs are low? How can I deliver tailored Data analysis techniques for fraud detection advise instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Data analysis techniques for fraud detection essentials are covered, from every angle: the Data analysis techniques for fraud detection self-assessment shows succinctly and clearly that what needs to be clarified to organize the business/project activities and processes so that Data analysis techniques for fraud detection outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Data analysis techniques for fraud detection practitioners. Their mastery, combined with the uncommon elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Data analysis techniques for fraud detection are maximized with professional results. Your purchase includes access details to the Data analysis techniques for fraud detection self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.
Author: David Coderre Publisher: John Wiley & Sons ISBN: 0470508485 Category : Business & Economics Languages : en Pages : 327
Book Description
"When people ask me what they can do to better utilize ACL, I tell them, 'Take an instructor lead course, participate in the ACL Forum, and study (not read, study) David Coderre's Fraud Analysis Techniques Using ACL.' I studied this book, and would not be where I am today without it. Even without the anti-fraud material, the book is worth the investment as a tool to learning ACL!" —Porter Broyles, President and founder of the Texas ACL User Group, Keynote Speaker at ACL's 2009 San Francisco Conference, Official ACL Super User "For individuals interested in learning about fraud analysis techniques or the art of ACL scripting, this book is a must-read. For those individuals interested in learning both, this book is a treasure." —Jim Hess, Principal, Hess Group, LLC Your very own ACL Fraud Toolkit—at your fingertips Fraud Analysis Techniques Using ACL offers auditors and investigators: Authoritative guidance from David Coderre, renowned expert on the use of computer-assisted audit tools and techniques in fraud detection A website containing an educational version of ACL from the world leader in fraud detection software An accompanying website containing a thorough Fraud Toolkit with two sets of customizable scripts to serve your specific audit needs Case studies and sample data files that you can use to try out the tests Step-by-step instructions on how to run the tests A self-study course on ACL script development with exercises, data files, and suggested answers The toolkit also contains 12 'utility scripts' and a self-study course on ACL scripting which includes exercises, data files, and proposed answers. Filled with screen shots, flow charts, example data files, and descriptive commentary highlighting and explaining each step, as well as case studies offering real-world examples of how the scripts can be used to search for fraud, Fraud Analysis Techniques Using ACL is the only toolkit you will need to harness the power of ACL to spot fraud.
Author: Gerardus Blokdyk Publisher: Createspace Independent Publishing Platform ISBN: 9781983813047 Category : Languages : en Pages : 134
Book Description
What potential environmental factors impact the Data analysis techniques for fraud detection effort? What key business process output measure(s) does Data analysis techniques for fraud detection leverage and how? Who sets the Data analysis techniques for fraud detection standards? Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data analysis techniques for fraud detection? Among the Data analysis techniques for fraud detection product and service cost to be estimated, which is considered hardest to estimate? This easy Data analysis techniques for fraud detection self-assessment will make you the dependable Data analysis techniques for fraud detection domain specialist by revealing just what you need to know to be fluent and ready for any Data analysis techniques for fraud detection challenge. How do I reduce the effort in the Data analysis techniques for fraud detection work to be done to get problems solved? How can I ensure that plans of action include every Data analysis techniques for fraud detection task and that every Data analysis techniques for fraud detection outcome is in place? How will I save time investigating strategic and tactical options and ensuring Data analysis techniques for fraud detection opportunity costs are low? How can I deliver tailored Data analysis techniques for fraud detection advise instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Data analysis techniques for fraud detection essentials are covered, from every angle: the Data analysis techniques for fraud detection self-assessment shows succinctly and clearly that what needs to be clarified to organize the business/project activities and processes so that Data analysis techniques for fraud detection outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Data analysis techniques for fraud detection practitioners. Their mastery, combined with the uncommon elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Data analysis techniques for fraud detection are maximized with professional results. Your purchase includes access details to the Data analysis techniques for fraud detection self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.
Author: Mark J. Nigrini Publisher: John Wiley & Sons ISBN: 1119585902 Category : Business & Economics Languages : en Pages : 549
Book Description
Become the forensic analytics expert in your organization using effective and efficient data analysis tests to find anomalies, biases, and potential fraud—the updated new edition Forensic Analytics reviews the methods and techniques that forensic accountants can use to detect intentional and unintentional errors, fraud, and biases. This updated second edition shows accountants and auditors how analyzing their corporate or public sector data can highlight transactions, balances, or subsets of transactions or balances in need of attention. These tests are made up of a set of initial high-level overview tests followed by a series of more focused tests. These focused tests use a variety of quantitative methods including Benford’s Law, outlier detection, the detection of duplicates, a comparison to benchmarks, time-series methods, risk-scoring, and sometimes simply statistical logic. The tests in the new edition include the newly developed vector variation score that quantifies the change in an array of data from one period to the next. The goals of the tests are to either produce a small sample of suspicious transactions, a small set of transaction groups, or a risk score related to individual transactions or a group of items. The new edition includes over two hundred figures. Each chapter, where applicable, includes one or more cases showing how the tests under discussion could have detected the fraud or anomalies. The new edition also includes two chapters each describing multi-million-dollar fraud schemes and the insights that can be learned from those examples. These interesting real-world examples help to make the text accessible and understandable for accounting professionals and accounting students without rigorous backgrounds in mathematics and statistics. Emphasizing practical applications, the new edition shows how to use either Excel or Access to run these analytics tests. The book also has some coverage on using Minitab, IDEA, R, and Tableau to run forensic-focused tests. The use of SAS and Power BI rounds out the software coverage. The software screenshots use the latest versions of the software available at the time of writing. This authoritative book: Describes the use of statistically-based techniques including Benford’s Law, descriptive statistics, and the vector variation score to detect errors and anomalies Shows how to run most of the tests in Access and Excel, and other data analysis software packages for a small sample of the tests Applies the tests under review in each chapter to the same purchasing card data from a government entity Includes interesting cases studies throughout that are linked to the tests being reviewed. Includes two comprehensive case studies where data analytics could have detected the frauds before they reached multi-million-dollar levels Includes a continually-updated companion website with the data sets used in the chapters, the queries used in the chapters, extra coverage of some topics or cases, end of chapter questions, and end of chapter cases. Written by a prominent educator and researcher in forensic accounting and auditing, the new edition of Forensic Analytics: Methods and Techniques for Forensic Accounting Investigations is an essential resource for forensic accountants, auditors, comptrollers, fraud investigators, and graduate students.
Author: Leonard W. Vona
Author: Leonard W. Vona
Author: Bart Baesens
Author: Sunder Gee
Author: Delena D. Spann
Author: Bart Baesens
Author: Gerard Blokdyk
Author: David Coderre
Author: Gerardus Blokdyk
Author: Mark J. Nigrini
Author: David G. Coderre