Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A

Description: Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques by Bart Baesens, Wouter Verbeke, Veronique Van Vlasselaer 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. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher 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 dataUtilize labeled, unlabeled, and networked dataDetect fraud before the damage cascadesReduce 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. Back Cover THE DEFINITIVE GUIDE TO THE DETECTION AND PREVENTION OF FRAUD THROUGH DATA ANALYTICS Catch fraud early! Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques shows you how with a thorough overview of how to prevent losses and recover quickly as well as the security issues you need to address now. Exploring how auditors, corporate security prevention managers, and fraud prevention professionals can stay one step ahead of cyber criminals, this book addresses the different types of analytics in detecting fraud, including descriptive analytics, predictive analytics, and social network analysis. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques offers a current, state-of-the-art detection and prevention methodology, describing the data necessary to detect fraud. Taking you from the basics of fraud detection data analytics, through advanced pattern recognition methodology, to cutting-edge social network analysis and fraud ring detection, this book presents essential coverage of: The fraud analytics process model Big data Break point/peer group analysis Anomaly detection Linear/logistic regression Neural networks Ensemble methods Social network metrics Bipartite graphs Community mining Visual analytics Model monitoring and backtesting Insightful and clearly written, this hands-on guide reveals what you need to know about fraud analytics and the secret to putting historical data to work in the fight against fraud. Flap THE DEFINITIVE GUIDE TO THE DETECTION AND PREVENTION OF FRAUD THROUGH DATA ANALYTICS Catch fraud early! Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques shows you how with a thorough overview of how to prevent losses and recover quickly as well as the security issues you need to address now. Exploring how auditors, corporate security prevention managers, and fraud prevention professionals can stay one step ahead of cyber criminals, this book addresses the different types of analytics in detecting fraud, including descriptive analytics, predictive analytics, and social network analysis. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques offers a current, state-of-the-art detection and prevention methodology, describing the data necessary to detect fraud. Taking you from the basics of fraud detection data analytics, through advanced pattern recognition methodology, to cutting-edge social network analysis and fraud ring detection, this book presents essential coverage of: The fraud analytics process model Big data Break point/peer group analysis Anomaly detection Linear/logistic regression Neural networks Ensemble methods Social network metrics Bipartite graphs Community mining Visual analytics Model monitoring and backtesting Insightful and clearly written, this hands-on guide reveals what you need to know about fraud analytics and the secret to putting historical data to work in the fight against fraud. Author Biography BART BAESENS is a full professor at KU Leuven, and a lecturer at the University of Southampton. He has done extensive research on analytics, customer relationship management, web analytics, fraud detection, and credit risk management. He regularly advises and provides consulting support to international firms with respect to their analytics and credit risk management strategy. VÉRONIQUE VAN VLASSELAER is a PhD researcher in the Department of Decision Sciences and Information Management at KU Leuven. Her research focuses on the development of new techniques for fraud detection by combining predictive and network analytics. WOUTER VERBEKE is an assistant professor at Vrije Universiteit Brussel (Brussels, Belgium). His research is situated in the field of predictive analytics and complex network analysis with applications in fraud, marketing, credit risk, human resources management, and mobility. Table of Contents List of Figures xv Foreword xxiii Preface xxv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for Fraud Detection 15 Data-Driven Fraud Detection 17 Fraud-Detection Techniques 19 Fraud Cycle 22 The Fraud Analytics Process Model 26 Fraud Data Scientists 30 A Fraud Data Scientist Should Have Solid Quantitative Skills 30 A Fraud Data Scientist Should Be a Good Programmer 31 A Fraud Data Scientist Should Excel in Communication and Visualization Skills 31 A Fraud Data Scientist Should Have a Solid Business Understanding 32 A Fraud Data Scientist Should Be Creative 32 A Scientific Perspective on Fraud 33 References 35 Chapter 2 Data Collection, Sampling, and Preprocessing 37 Introduction 38 Types of Data Sources 38 Merging Data Sources 43 Sampling 45 Types of Data Elements 46 Visual Data Exploration and Exploratory Statistical Analysis 47 Benfords Law 48 Descriptive Statistics 51 Missing Values 52 Outlier Detection and Treatment 53 Red Flags 57 Standardizing Data 59 Categorization 60 Weights of Evidence Coding 63 Variable Selection 65 Principal Components Analysis 68 RIDITs 72 PRIDIT Analysis 73 Segmentation 74 References 75 Chapter 3 Descriptive Analytics for Fraud Detection 77 Introduction 78 Graphical Outlier Detection Procedures 79 Statistical Outlier Detection Procedures 83 Break-Point Analysis 84 Peer-Group Analysis 85 Association Rule Analysis 87 Clustering 89 Introduction 89 Distance Metrics 90 Hierarchical Clustering 94 Example of Hierarchical Clustering Procedures 97 k-Means Clustering 104 Self-Organizing Maps 109 Clustering with Constraints 111 Evaluating and Interpreting Clustering Solutions 114 One-Class SVMs 117 References 118 Chapter 4 Predictive Analytics for Fraud Detection 121 Introduction 122 Target Definition 123 Linear Regression 125 Logistic Regression 127 Basic Concepts 127 Logistic Regression Properties 129 Building a Logistic Regression Scorecard 131 Variable Selection for Linear and Logistic Regression 133 Decision Trees 136 Basic Concepts 136 Splitting Decision 137 Stopping Decision 140 Decision Tree Properties 141 Regression Trees 142 Using Decision Trees in Fraud Analytics 143 Neural Networks 144 Basic Concepts 144 Weight Learning 147 Opening the Neural Network Black Box 150 Support Vector Machines 155 Linear Programming 155 The Linear Separable Case 156 The Linear Nonseparable Case 159 The Nonlinear SVM Classifier 160 SVMs for Regression 161 Opening the SVM Black Box 163 Ensemble Methods 164 Bagging 164 Boosting 165 Random Forests 166 Evaluating Ensemble Methods 167 Multiclass Classification Techniques 168 Multiclass Logistic Regression 168 Multiclass Decision Trees 170 Multiclass Neural Networks 170 Multiclass Support Vector Machines 171 Evaluating Predictive Models 172 Splitting Up the Data Set 172 Performance Measures for Classification Models 176 Performance Measures for Regression Models 185 Other Performance Measures for Predictive Analytical Models 188 Developing Predictive Models for Skewed Data Sets 189 Varying the Sample Window 190 Undersampling and Oversampling 190 Synthetic Minority Oversampling Technique (SMOTE) 192 Likelihood Approach 194 Adjusting Posterior Probabilities 197 Cost-sensitive Learning 198 Fraud Performance Benchmarks 200 References 201 Chapter 5 Social Network Analysis for Fraud Detection 207 Networks: Form, Components, Characteristics, and Their Applications 209 Social Networks 211 Network Components 214 Network Representation 219 Is Fraud a Social Phenomenon? An Introduction to Homophily 222 Impact of the Neighborhood: Metrics 227 Neighborhood Metrics 228 Centrality Metrics 238 Collective Inference Algorithms 246 Featurization: Summary Overview 254 Community Mining: Finding Groups of Fraudsters 254 Extending the Graph: Toward a Bipartite Representation 266 Multipartite Graphs 269 Case Study: Gotcha! 270 References 277 Chapter 6 Fraud Analytics: Post-Processing 279 Introduction 280 The Analytical Fraud Model Life Cycle 280 Model Representation 281 Traffic Light Indicator Approach 282 Decision Tables 283 Selecting the Sample to Investigate 286 Fraud Alert and Case Management 290 Visual Analytics 296 Backtesting Analytical Fraud Models 302 Introduction 302 Backtesting Data Stability 302 Backtesting Model Stability 305 Backtesting Model Calibration 308 Model Design and Documentation 311 References 312 Chapter 7 Fraud Analytics: A Broader Perspective 313 Introduction 314 Data Quality 314 Data-Quality Issues 314 Data-Quality Programs and Management 315 Privacy 317 The RACI Matrix 318 Accessing Internal Data 319 Label-Based Access Control (LBAC) 324 Accessing External Data 325 Capital Calculation for Fraud Loss 326 Expected and Unexpected Losses 327 Aggregate Loss Distribution 329 Capital Calculation for Fraud Loss Using Monte Carlo Simulation 331 An Economic Perspective on Fraud Analytics 334 Total Cost of Ownership 334 Return on Investment 335 In Versus Outsourcing 337 Modeling Extensions 338 Forecasting 338 Text Analytics 340 The Internet of Things 342 Corporate Fraud Governance 344 References 346 About the Authors 347 Index 349 Long Description THE DEFINITIVE GUIDE TO THE DETECTION AND PREVENTION OF FRAUD THROUGH DATA ANALYTICS Catch fraud early! Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques shows you how with a thorough overview of how to prevent losses and recover quickly as well as the security issues you need to address now. Exploring how auditors, corporate security prevention managers, and fraud prevention professionals can stay one step ahead of cyber criminals, this book addresses the different types of analytics in detecting fraud, including descriptive analytics, predictive analytics, and social network analysis. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques offers a current, state-of-the-art detection and prevention methodology, describing the data necessary to detect fraud. Taking you from the basics of fraud detection data analytics, through advanced pattern recognition methodology, to cutting-edge social network analysis and fraud ring detection, this book presents essential coverage of: The fraud analytics process model Big data Break point/peer group analysis Anomaly detection Linear/logistic regression Neural networks Ensemble methods Social network metrics Bipartite graphs Community mining Visual analytics Model monitoring and backtesting Insightful and clearly written, this hands-on guide reveals what you need to know about fraud analytics and the secret to putting historical data to work in the fight against fraud. Details ISBN1119133122 Series Wiley and SAS Business Series Year 2015 ISBN-10 1119133122 ISBN-13 9781119133124 Format Hardcover Language English Media Book Short Title FRAUD ANALYTICS USING DESCRIPT Subtitle A Guide to Data Science for Fraud Detection Place of Publication New York Country of Publication United States DEWEY 658.473 Pages 400 Author Veronique Van Vlasselaer UK Release Date 2015-10-09 AU Release Date 2015-08-07 NZ Release Date 2015-08-07 Publisher John Wiley & Sons Inc Publication Date 2015-10-09 Imprint John Wiley & Sons Inc Audience Professional & Vocational US Release Date 2015-10-09 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:138994750;

Price: 81.37 AUD

Location: Melbourne

End Time: 2024-12-16T17:10:23.000Z

Shipping Cost: 12.28 AUD