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Benford's law : theory, the general law of relative quantities, and forensic fraud detection applications / Alex Ely Kossovsky
| Benford's law : theory, the general law of relative quantities, and forensic fraud detection applications / Alex Ely Kossovsky |
| Autore | Kossovsky, Alex Ely |
| Descrizione fisica | xxi, 649 p. : ill. ; 24 cm |
| Disciplina | 363.25 |
| Soggetto topico |
Fraud investigation - Statistical methods
Fraud - Statistical methods Distribution (Probability theory) Forensic statistics Forensic sciences - Statistical methods |
| ISBN | 9789814651202 (paperback) |
| Classificazione |
AMS 60-01
AMS 60E05 AMS 11K06 AMS 60F15 LC HV8079.F7K67 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991003364879707536 |
Kossovsky, Alex Ely
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| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection / / Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke
| Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection / / Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke |
| Autore | Baesens Bart |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (402 p.) |
| Disciplina | 364.16/3015195 |
| Collana | Wiley and SAS Business Series |
| Soggetto topico |
Fraud - Statistical methods
Fraud - Prevention Commercial crimes - Prevention |
| ISBN |
1-119-14683-6
1-119-14684-4 1-119-14682-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; 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
A 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 PRIDIT 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 Target 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 Opening 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 Synthetic Minority Oversampling Technique (SMOTE) |
| Record Nr. | UNINA-9910131489503321 |
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Baesens Bart
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| Hoboken, New Jersey : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection / / Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke
| Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection / / Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke |
| Autore | Baesens Bart |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (402 p.) |
| Disciplina | 364.16/3015195 |
| Collana | Wiley and SAS Business Series |
| Soggetto topico |
Fraud - Statistical methods
Fraud - Prevention Commercial crimes - Prevention |
| ISBN |
1-119-14683-6
1-119-14684-4 1-119-14682-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; 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
A 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 PRIDIT 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 Target 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 Opening 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 Synthetic Minority Oversampling Technique (SMOTE) |
| Record Nr. | UNINA-9910824829403321 |
|
Baesens Bart
|
||
| Hoboken, New Jersey : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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