Fraud analytics using descriptive, predictive, and social network techniques a guide to data science for fraud detection
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...
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| Format: | eBook |
| Language: | Inglés |
| Published: |
Hoboken, New Jersey :
Wiley
2015.
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| Edition: | 1st edition |
| Series: | Wiley and SAS business series.
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| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629575606719 |
Table of Contents:
- 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)
