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| data analysis for fraud detection: Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke, 2015-08-17 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. |
| data analysis for fraud detection: Fraud and Fraud Detection, + Website Sunder Gee, 2014-12-03 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. |
| data analysis for fraud detection: Fraud Analytics Delena D. Spann, 2014-07-22 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. |
| data analysis for fraud detection: Fraud Data Analytics Methodology Leonard W. Vona, 2017-01-04 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. |
| data analysis for fraud detection: Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke, 2015-07-27 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. |
| data analysis for fraud detection: Fraud Detection David G. Coderre, 1999 |
| data analysis for fraud detection: Unstructured Data Analytics Jean Paul Isson, 2018-03-13 Turn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis. |
| data analysis for fraud detection: Forensic Analytics Mark J. Nigrini, 2020-04-20 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. |
| data analysis for fraud detection: Using Analytics to Detect Possible Fraud Pamela S. Mantone, 2013-07-16 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. |
| data analysis for fraud detection: Fraud Analysis Techniques Using ACL David Coderre, 2009-07-23 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. |
| data analysis for fraud detection: Data Mining for Intelligence, Fraud & Criminal Detection Christopher Westphal, 2008-12-22 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 |
| data analysis for fraud detection: Computer Aided Fraud Prevention and Detection David Coderre, 2009-03-17 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 |
| data analysis for fraud detection: Real-time Fraud Detection Analytics on IBM System z Mike Ebbers, Dheeraj Reddy Chintala, Priya Ranjan, Lakshminarayanan Sreenivasan, IBM Redbooks, 2013-04-11 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. |
| data analysis for fraud detection: Fraud Analytics with SAS , 2019-06-21 SAS software provides many different techniques to monitor in real time and investigate your data, and several groundbreaking papers have been written to demonstrate how to use these techniques. Topics covered illustrate the power of SAS solutions that are available as tools for fraud analytics, highlighting a variety of domains, including money laundering, financial crime, and terrorism. Also available free as a PDF from: sas.com/books. |
| data analysis for fraud detection: Fraud and Fraud Detection Sunder Gee, 2014-11-05 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. |
| data analysis for fraud detection: Benford's Law Mark J. Nigrini, 2012-03-09 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. |
| data analysis for fraud detection: Encyclopedia of Organizational Knowledge, Administration, and Technology Khosrow-Pour D.B.A., Mehdi, 2020-09-29 For any organization to be successful, it must operate in such a manner that knowledge and information, human resources, and technology are continually taken into consideration and managed effectively. Business concepts are always present regardless of the field or industry – in education, government, healthcare, not-for-profit, engineering, hospitality/tourism, among others. Maintaining organizational awareness and a strategic frame of mind is critical to meeting goals, gaining competitive advantage, and ultimately ensuring sustainability. The Encyclopedia of Organizational Knowledge, Administration, and Technology is an inaugural five-volume publication that offers 193 completely new and previously unpublished articles authored by leading experts on the latest concepts, issues, challenges, innovations, and opportunities covering all aspects of modern organizations. Moreover, it is comprised of content that highlights major breakthroughs, discoveries, and authoritative research results as they pertain to all aspects of organizational growth and development including methodologies that can help companies thrive and analytical tools that assess an organization’s internal health and performance. Insights are offered in key topics such as organizational structure, strategic leadership, information technology management, and business analytics, among others. The knowledge compiled in this publication is designed for entrepreneurs, managers, executives, investors, economic analysts, computer engineers, software programmers, human resource departments, and other industry professionals seeking to understand the latest tools to emerge from this field and who are looking to incorporate them in their practice. Additionally, academicians, researchers, and students in fields that include but are not limited to business, management science, organizational development, entrepreneurship, sociology, corporate psychology, computer science, and information technology will benefit from the research compiled within this publication. |
| data analysis for fraud detection: Streaming Architecture Ted Dunning, Ellen Friedman, 2016-05-10 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. |
| data analysis for fraud detection: Credit Card Fraud Detection and Analysis Through Machine Learning Yogita Goyal, Anand Sharma, 2020-07-28 |
| data analysis for fraud detection: Forensic Analytics Mark J. Nigrini, 2011-05-12 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. |
| data analysis for fraud detection: Bank Fraud Revathi Subramanian, 2014-04-14 Learn how advances in technology can help curb bank fraud Fraud prevention specialists are grappling with ever-mounting quantities of data, but in today's volatile commercial environment, paying attention to that data is more important than ever. Bank Fraud provides a frank discussion of the attitudes, strategies, and—most importantly—the technology that specialists will need to combat fraud. Fraudulent activity may have increased over the years, but so has the field of data science and the results that can be achieved by applying the right principles, a necessary tool today for financial institutions to protect themselves and their clientele. This resource helps professionals in the financial services industry make the most of data intelligence and uncovers the applicable methods to strengthening defenses against fraudulent behavior. This in-depth treatment of the topic begins with a brief history of fraud detection in banking and definitions of key terms, then discusses the benefits of technology, data sharing, and analysis, along with other in-depth information, including: The challenges of fraud detection in a financial services environment The use of statistics, including effective ways to measure losses per account and ROI by product/initiative The Ten Commandments for tackling fraud and ways to build an effective model for fraud management Bank Fraud offers a compelling narrative that ultimately urges security and fraud prevention professionals to make the most of the data they have so painstakingly gathered. Such professionals shouldn't let their most important intellectual asset—data—go to waste. This book shows you just how to leverage data and the most up-to-date tools, technologies, and methods to thwart fraud at every turn. |
| data analysis for fraud detection: Test Fraud Neal Kingston, Amy Clark, 2014-06-27 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. |
| data analysis for fraud detection: Investigative Data Mining for Security and Criminal Detection Jesus Mena, 2003 Publisher Description |
| data analysis for fraud detection: Practical Data Analysis Dhiraj Bhuyan, 2019-11-30 “Practical Data Analysis – Using Python & Open Source Technology” uses a case-study based approach to explore some of the real-world applications of open source data analysis tools and techniques. Specifically, the following topics are covered in this book: 1. Open Source Data Analysis Tools and Techniques. 2. A Beginner’s Guide to “Python” for Data Analysis. 3. Implementing Custom Search Engines On The Fly. 4. Visualising Missing Data. 5. Sentiment Analysis and Named Entity Recognition. 6. Automatic Document Classification, Clustering and Summarisation. 7. Fraud Detection Using Machine Learning Techniques. 8. Forecasting - Using Data to Map the Future. 9. Continuous Monitoring and Real-Time Analytics. 10. Creating a Robot for Interacting with Web Applications. Free samples of the book is available at - http://timesofdatascience.com |
| data analysis for fraud detection: Intelligent Data Analysis Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna, Kalpna Sagar, 2020-07-13 This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools. |
| data analysis for fraud detection: Machine Learning Applications for Accounting Disclosure and Fraud Detection Papadakis, Stylianos, Garefalakis, Alexandros, Lemonakis, Christos, Chimonaki, Christiana, Zopounidis, Constantin, 2020-10-02 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. |
| data analysis for fraud detection: Advances in Intelligent Data Analysis XVIII Michael R. Berthold, Ad Feelders, Georg Krempl, 2020-04-02 This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation. |
| data analysis for fraud detection: Statistics and Health Care Fraud Tahir Ekin, 2019-02-07 Statistics and Health Care Fraud: How to Save Billions helps the public to become more informed citizens through discussions of real world health care examples and fraud assessment applications. The author presents statistical and analytical methods used in health care fraud audits without requiring any mathematical background. The public suffers from health care overpayments either directly as patients or indirectly as taxpayers, and fraud analytics provides ways to handle the large size and complexity of these claims. The book starts with a brief overview of global healthcare systems such as U.S. Medicare. This is followed by a discussion of medical overpayments and assessment initiatives using a variety of real world examples. The book covers subjects as: • Description and visualization of medical claims data • Prediction of fraudulent transactions • Detection of excessive billings • Revealing new fraud patterns • Challenges and opportunities with health care fraud analytics Dr. Tahir Ekin is the Brandon Dee Roberts Associate Professor of Quantitative Methods in McCoy College of Business, Texas State University. His previous work experience includes a working as a statistician on health care fraud detection. His scholarly work on health care fraud has been published in a variety of academic journals including International Statistical Review, The American Statistician, and Applied Stochastic Models in Business and Industry. He is a recipient of the Texas State University 2018 Presidential Distinction Award in Scholar Activities and the ASA/NISS y-Bis 2016 Best Paper Awards. He has developed and taught courses in the areas of business statistics, optimization, data mining and analytics. Dr. Ekin also serves as Vice President of the International Society for Business and Industrial Statistics. |
| data analysis for fraud detection: Data Sleuth Leah Wietholter, 2022-04-19 Straightforward, practical guidance for working fraud examiners and forensic accountants In Data Sleuth: Using Data in Forensic Accounting Engagements and Fraud Investigations, certified fraud examiner, former FBI support employee, private investigator, and certified public accountant Leah Wietholter delivers a step-by-step guide to financial investigation that can be applied to almost any forensic accounting use-case. The book emphasizes the use of best evidence as you work through problem-solving data analysis techniques that address the common challenge of imperfect and incomplete information. The accomplished author bridges the gap between modern fraud investigation theory and practical applications and processes necessary for working practitioners. She also provides: Access to a complimentary website with supplementary resources, including a Fraud Detection Worksheet and case planning template Strategies for systematically applying the Data Sleuth® framework to streamline and grow your practice Methods and techniques to improve the quality of your work product Data Sleuth is an indispensable, hands-on resource for practicing and aspiring fraud examiners and investigators, accountants, and auditors. It’s a one-of-a-kind book that puts a practical blueprint to effective financial investigation in the palm of your hand. |
| data analysis for fraud detection: Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS Richard C. Zink, 2014-07-01 Improve efficiency while reducing costs in clinical trials with centralized monitoring techniques using JMP and SAS. International guidelines recommend that clinical trial data should be actively reviewed or monitored; the well-being of trial participants and the validity and integrity of the final analysis results are at stake. Traditional interpretation of this guidance for pharmaceutical trials has led to extensive on-site monitoring, including 100% source data verification. On-site review is time consuming, expensive (estimated at up to a third of the cost of a clinical trial), prone to error, and limited in its ability to provide insight for data trends across time, patients, and clinical sites. In contrast, risk-based monitoring (RBM) makes use of central computerized review of clinical trial data and site metrics to determine if and when clinical sites should receive more extensive quality review or intervention. Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS presents a practical implementation of methodologies within JMP Clinical for the centralized monitoring of clinical trials. Focused on intermediate users, this book describes analyses for RBM that incorporate and extend the recommendations of TransCelerate Biopharm Inc., methods to detect potential patient-or investigator misconduct, snapshot comparisons to more easily identify new or modified data, and other novel visual and analytical techniques to enhance safety and quality reviews. Further discussion highlights recent regulatory guidance documents on risk-based approaches, addresses the requirements for CDISC data, and describes methods to supplement analyses with data captured external to the study database. Given the interactive, dynamic, and graphical nature of JMP Clinical, any individual from the clinical trial team - including clinicians, statisticians, data managers, programmers, regulatory associates, and monitors - can make use of this book and the numerous examples contained within to streamline, accelerate, and enrich their reviews of clinical trial data. The analytical methods described in Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS enable the clinical trial team to take a proactive approach to data quality and safety to streamline clinical development activities and address shortcomings while the study is ongoing. This book is part of the SAS Press |
| data analysis for fraud detection: Stream Analytics with Microsoft Azure Anindita Basak, Krishna Venkataraman, Ryan Murphy, Manpreet Singh, 2017-12-01 Develop and manage effective real-time streaming solutions by leveraging the power of Microsoft Azure About This Book Analyze your data from various sources using Microsoft Azure Stream Analytics Develop, manage and automate your stream analytics solution with Microsoft Azure A practical guide to real-time event processing and performing analytics on the cloud Who This Book Is For If you are looking for a resource that teaches you how to process continuous streams of data in real-time, this book is what you need. A basic understanding of the concepts in analytics is all you need to get started with this book What You Will Learn Perform real-time event processing with Azure Stream Analysis Incorporate the features of Big Data Lambda architecture pattern in real-time data processing Design a streaming pipeline for storage and batch analysis Implement data transformation and computation activities over stream of events Automate your streaming pipeline using Powershell and the .NET SDK Integrate your streaming pipeline with popular Machine Learning and Predictive Analytics modelling algorithms Monitor and troubleshoot your Azure Streaming jobs effectively In Detail Microsoft Azure is a very popular cloud computing service used by many organizations around the world. Its latest analytics offering, Stream Analytics, allows you to process and get actionable insights from different kinds of data in real-time. This book is your guide to understanding the basics of how Azure Stream Analytics works, and building your own analytics solution using its capabilities. You will start with understanding what Stream Analytics is, and why it is a popular choice for getting real-time insights from data. Then, you will be introduced to Azure Stream Analytics, and see how you can use the tools and functions in Azure to develop your own Streaming Analytics. Over the course of the book, you will be given comparative analytic guidance on using Azure Streaming with other Microsoft Data Platform resources such as Big Data Lambda Architecture integration for real time data analysis and differences of scenarios for architecture designing with Azure HDInsight Hadoop clusters with Storm or Stream Analytics. The book also shows you how you can manage, monitor, and scale your solution for optimal performance. By the end of this book, you will be well-versed in using Azure Stream Analytics to develop an efficient analytics solution that can work with any type of data. Style and approach A comprehensive guidance on developing real-time event processing with Azure Stream Analysis |
| data analysis for fraud detection: Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making Cengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari, 2019-07-05 This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic. |
| data analysis for fraud detection: New Paradigm in Decision Science and Management Srikanta Patnaik, Andrew W. H. Ip, Madjid Tavana, Vipul Jain, 2019-09-20 This book discusses an emerging area in computer science, IT and management, i.e., decision sciences and management. It includes studies that employ various computing techniques like machine learning to generate insights from huge amounts of available data; and which explore decision-making for cross-platforms that contain heterogeneous data associated with complex assets; leadership; and team coordination. It also reveals the advantages of using decision sciences with management-oriented problems. The book includes a selection of the best papers presented at the International Conference on Decision Science and Management 2018 (ICDSM 2018), held at the Interscience Institute of Management and Technology (IIMT), Bhubaneswar, India. |
| data analysis for fraud detection: Network Science Albert-László Barabási, Márton PÃ3sfai, 2016-07-21 Illustrated throughout in full colour, this pioneering text is the only book you need for an introduction to network science. |
| data analysis for fraud detection: ICT Systems Security and Privacy Protection Nora Cuppens-Boulahia, Frederic Cuppens, Sushil Jajodia, Anas Abou El Kalam, Thierry Sans, 2016-09-21 This book constitutes the refereed proceedings of the 29th IFIP TC 11 International Information Security and Privacy Conference, SEC 2014, held in Marrakech, Morocco, in June 2014. The 27 revised full papers and 14 short papers presented were carefully reviewed and selected from 151 submissions. The papers are organized in topical sections on intrusion detection, data security, mobile security, privacy, metrics and risk assessment, information flow control, identity management, identifiability and decision making, malicious behavior and fraud and organizational security. |
| data analysis for fraud detection: Trustworthy AI Beena Ammanath, 2022-03-15 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. |
| data analysis for fraud detection: Handbook of Computer Networks and Cyber Security Brij B. Gupta, Gregorio Martinez Perez, Dharma P. Agrawal, Deepak Gupta, 2019-12-31 This handbook introduces the basic principles and fundamentals of cyber security towards establishing an understanding of how to protect computers from hackers and adversaries. The highly informative subject matter of this handbook, includes various concepts, models, and terminologies along with examples and illustrations to demonstrate substantial technical details of the field. It motivates the readers to exercise better protection and defense mechanisms to deal with attackers and mitigate the situation. This handbook also outlines some of the exciting areas of future research where the existing approaches can be implemented. Exponential increase in the use of computers as a means of storing and retrieving security-intensive information, requires placement of adequate security measures to safeguard the entire computing and communication scenario. With the advent of Internet and its underlying technologies, information security aspects are becoming a prime concern towards protecting the networks and the cyber ecosystem from variety of threats, which is illustrated in this handbook. This handbook primarily targets professionals in security, privacy and trust to use and improve the reliability of businesses in a distributed manner, as well as computer scientists and software developers, who are seeking to carry out research and develop software in information and cyber security. Researchers and advanced-level students in computer science will also benefit from this reference. |
| data analysis for fraud detection: Handbook of Research on Managing Information Systems in Developing Economies Boateng, Richard, 2020-04-17 Technology provides accessibility otherwise unavailable to the people who can benefit from it the most. As new digital tools become less expensive and more widely available, research and real-world cases that examine the union between emergent countries and information systems are essential in determining the next steps for these nations. The Handbook of Research on Managing Information Systems in Developing Economies is a pivotal reference source that explores the effects of technological data handling within developing economies. Covering a broad range of topics such as emerging digital technologies, socio-economic development, and technology startups, this book is ideally designed for software programmers, policymakers, practitioners, educators, academicians, students, and researchers. |
| data analysis for fraud detection: Computational and Statistical Methods for Analysing Big Data with Applications Shen Liu, James Mcgree, Zongyuan Ge, Yang Xie, 2015-11-20 Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. - Advanced computational and statistical methodologies for analysing big data are developed - Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable - Case studies are discussed to demonstrate the implementation of the developed methods - Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation - Computing code/programs are provided where appropriate |
| data analysis for fraud detection: Computing Science, Communication and Security Nirbhay Chaubey, Satyen Parikh, Kiran Amin, 2021-05-19 This book constitutes revised selected papers of the Second International Conference on Computing Science, Communication and Security, COMS2 2021, held in Gandhinagar, India, in February 2021. Due to the COVID-19 pandemic the conference was held virtually. The 19 full papers were thoroughly reveiwed and selected from 105 submissions. Papers are organised according to the topical sections on artificial intelligence and machine learning; networking and communications. |
Fraud Detection: Identifying Patterns with Analytics - IABAC
Aug 17, 2023 · Data analytics plays a crucial role in identifying fraud by harnessing the power of data to uncover patterns, anomalies, and trends that could indicate fraudulent activities.
Data analysis for fraud detection - Wikipedia
Statistical analysis of research data is the most comprehensive method for determining if data fraud exists. Data fraud as defined by the Office of Research Integrity (ORI) includes …
What Is Fraud Analytics? Techniques, Workflows, and Tools
Oct 12, 2024 · Fraud analytics refers to statistical and machine-learning techniques to identify and flag potentially fraudulent transactions, usually in real time. Discovering fraudulent activity is …
Using fraud data analytics to detect and prevent fraud
Fraud data analytics has emerged as a powerful tool in the fight against fraud, providing organisations with new insights about potential risks and empowering them to predict, detect, …
Data Analytics & Predictive Techniques for Fraud Detection - SEON
May 14, 2025 · Discover how predictive analytics and data analysis techniques detect suspicious activity and prevent fraud. Explore essential tools, models, and use cases.
DATA ANALYSIS:- BANK TRANSACTION FRAUD DETECTION
Explore and run machine learning code with Kaggle Notebooks | Using data from Bank Transaction Fraud Detection
Big Data For Fraud Detection and Prevention - HyperVerge
Mar 13, 2025 · Discover how Big Data analytics transforms fraud detection by improving accuracy, reducing false positives, and enabling real-time analysis for proactive prevention.
Q&A: Data analytics for fraud detection and prevention
Accurate, complete and timely data forms the backbone of effective fraud detection, enabling precise analysis and anomaly detection. When data is flawed, it can lead to false positives and …
Creating a data analytics approach to fraud detection and …
Fraud analytics is a process that leverages advanced analytics to identify and combat fraudulent activities. By analyzing large volumes of data, it can detect suspicious behaviors and potential …
Detecting and Preventing Fraud with Data Analytics
Jan 1, 2015 · Since the companies usually operate with large volumes of data, it is absolutely necessary to implement such processes of continuous monitoring, in order to identify …
Fraud Detection: Identifying Patterns with Analytics - IABAC
Aug 17, 2023 · Data analytics plays a crucial role in identifying fraud by harnessing the power of data to uncover patterns, anomalies, and trends that could indicate fraudulent activities.
Data analysis for fraud detection - Wikipedia
Statistical analysis of research data is the most comprehensive method for determining if data fraud exists. Data fraud as defined by the Office of Research Integrity (ORI) includes …
What Is Fraud Analytics? Techniques, Workflows, and Tools
Oct 12, 2024 · Fraud analytics refers to statistical and machine-learning techniques to identify and flag potentially fraudulent transactions, usually in real time. Discovering fraudulent activity is …
Using fraud data analytics to detect and prevent fraud
Fraud data analytics has emerged as a powerful tool in the fight against fraud, providing organisations with new insights about potential risks and empowering them to predict, detect, …
Data Analytics & Predictive Techniques for Fraud Detection - SEON
May 14, 2025 · Discover how predictive analytics and data analysis techniques detect suspicious activity and prevent fraud. Explore essential tools, models, and use cases.
DATA ANALYSIS:- BANK TRANSACTION FRAUD DETECTION
Explore and run machine learning code with Kaggle Notebooks | Using data from Bank Transaction Fraud Detection
Big Data For Fraud Detection and Prevention - HyperVerge
Mar 13, 2025 · Discover how Big Data analytics transforms fraud detection by improving accuracy, reducing false positives, and enabling real-time analysis for proactive prevention.
Q&A: Data analytics for fraud detection and prevention
Accurate, complete and timely data forms the backbone of effective fraud detection, enabling precise analysis and anomaly detection. When data is flawed, it can lead to false positives and …
Creating a data analytics approach to fraud detection and …
Fraud analytics is a process that leverages advanced analytics to identify and combat fraudulent activities. By analyzing large volumes of data, it can detect suspicious behaviors and potential …
Detecting and Preventing Fraud with Data Analytics
Jan 1, 2015 · Since the companies usually operate with large volumes of data, it is absolutely necessary to implement such processes of continuous monitoring, in order to identify …
