LEADER 05868nam 2200769 450 001 9910824829403321 005 20230912145345.0 010 $a1-119-14683-6 010 $a1-119-14684-4 010 $a1-119-14682-8 035 $a(CKB)3710000000454783 035 $a(EBL)2009871 035 $a(SSID)ssj0001530643 035 $a(PQKBManifestationID)12562642 035 $a(PQKBTitleCode)TC0001530643 035 $a(PQKBWorkID)11532438 035 $a(PQKB)10822268 035 $a(PQKBManifestationID)16114674 035 $a(PQKB)24273660 035 $a(DLC) 2015019076 035 $a(Au-PeEL)EBL4041085 035 $a(CaPaEBR)ebr11114058 035 $a(CaONFJC)MIL818884 035 $a(OCoLC)908935560 035 $a(CaSebORM)9781119133124 035 $a(MiAaPQ)EBC4041085 035 $a(MiAaPQ)EBC2009871 035 $a(PPN)272710415 035 $a(EXLCZ)993710000000454783 100 $a20151105h20152015 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFraud analytics using descriptive, predictive, and social network techniques $ea guide to data science for fraud detection /$fBart Baesens, Veronique Van Vlasselaer, Wouter Verbeke 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d2015. 210 4$dİ2015 215 $a1 online resource (402 p.) 225 1 $aWiley and SAS Business Series 300 $aIncludes index. 311 $a1-119-13312-2 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCover; Title Page; Copyright; Contents; List of Figures; Foreword; Preface; Acknowledgments; Chapter 1 Fraud: Detection, Prevention, and Analytics!; Introduction; Fraud!; Fraud Detection and Prevention; Big Data for Fraud Detection; Data-Driven Fraud Detection; Fraud-Detection Techniques; Fraud Cycle; The Fraud Analytics Process Model; Fraud Data Scientists; A Fraud Data Scientist Should Have Solid Quantitative Skills; A Fraud Data Scientist Should Be a Good Programmer; A Fraud Data Scientist Should Excel in Communication and Visualization Skills 327 $aA Fraud Data Scientist Should Have a Solid Business Understanding A Fraud Data Scientist Should Be Creative; A Scientific Perspective on Fraud; References; Chapter 2 Data Collection, Sampling, and Preprocessing; Introduction; Types of Data Sources; Merging Data Sources; Sampling; Types of Data Elements; Visual Data Exploration and Exploratory Statistical Analysis; Benford's Law; Descriptive Statistics; Missing Values; Outlier Detection and Treatment; Red Flags; Standardizing Data; Categorization; Weights of Evidence Coding; Variable Selection; Principal Components Analysis; RIDITs 327 $aPRIDIT Analysis Segmentation; References; Chapter 3 Descriptive Analytics for Fraud Detection; Introduction; Graphical Outlier Detection Procedures; Statistical Outlier Detection Procedures; Break-Point Analysis; Peer-Group Analysis; Association Rule Analysis; Clustering; Introduction; Distance Metrics; Hierarchical Clustering; Example of Hierarchical Clustering Procedures; k-Means Clustering; Self-Organizing Maps; Clustering with Constraints; Evaluating and Interpreting Clustering Solutions; One-Class SVMs; References; Chapter 4 Predictive Analytics for Fraud Detection; Introduction 327 $aTarget Definition Linear Regression; Logistic Regression; Basic Concepts; Logistic Regression Properties; Building a Logistic Regression Scorecard; Variable Selection for Linear and Logistic Regression; Decision Trees; Basic Concepts; Splitting Decision; Stopping Decision; Decision Tree Properties; Regression Trees; Using Decision Trees in Fraud Analytics; Neural Networks; Basic Concepts; Weight Learning; Opening the Neural Network Black Box; Support Vector Machines; Linear Programming; The Linear Separable Case; The Linear Nonseparable Case; The Nonlinear SVM Classifier; SVMs for Regression 327 $aOpening the SVM Black Box Ensemble Methods; Bagging; Boosting; Random Forests; Evaluating Ensemble Methods; Multiclass Classification Techniques; Multiclass Logistic Regression; Multiclass Decision Trees; Multiclass Neural Networks; Multiclass Support Vector Machines; Evaluating Predictive Models; Splitting Up the Data Set; Performance Measures for Classification Models; Performance Measures for Regression Models; Other Performance Measures for Predictive Analytical Models; Developing Predictive Models for Skewed Data Sets; Varying the Sample Window; Undersampling and Oversampling 327 $aSynthetic Minority Oversampling Technique (SMOTE) 330 $aDetect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, mode 410 0$aWiley and SAS business series. 606 $aFraud$xStatistical methods 606 $aFraud$xPrevention 606 $aCommercial crimes$xPrevention 615 0$aFraud$xStatistical methods. 615 0$aFraud$xPrevention. 615 0$aCommercial crimes$xPrevention. 676 $a364.16/3015195 700 $aBaesens$b Bart$0903326 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910824829403321 996 $aFraud analytics using descriptive, predictive, and social network techniques$93933817 997 $aUNINA