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Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection

Book 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 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.

    Table of Contents

    1. Title Page
    2. Copyright
    3. Dedication
    4. List of Figures
    5. Foreword
    6. Preface
    7. Acknowledgments
    8. Chapter 1: Fraud: Detection, Prevention, and Analytics!
      1. Introduction
      2. Fraud!
      3. Fraud Detection and Prevention
      4. Big Data for Fraud Detection
      5. Data-Driven Fraud Detection
      6. Fraud-Detection Techniques
      7. Fraud Cycle
      8. The Fraud Analytics Process Model
      9. Fraud Data Scientists
      10. A Scientific Perspective on Fraud
      11. References
    9. Chapter 2: Data Collection, Sampling, and Preprocessing
      1. Introduction
      2. Types of Data Sources
      3. Merging Data Sources
      4. Sampling
      5. Types of Data Elements
      6. Visual Data Exploration and Exploratory Statistical Analysis
      7. Benford's Law
      8. Descriptive Statistics
      9. Missing Values
      10. Outlier Detection and Treatment
      11. Red Flags
      12. Standardizing Data
      13. Categorization
      14. Weights of Evidence Coding
      15. Variable Selection
      16. Principal Components Analysis
      17. RIDITs
      18. PRIDIT Analysis
      19. Segmentation
      20. References
    10. Chapter 3: Descriptive Analytics for Fraud Detection
      1. Introduction
      2. Graphical Outlier Detection Procedures
      3. Statistical Outlier Detection Procedures
      4. Clustering
      5. One-Class SVMs
      6. References
    11. Chapter 4: Predictive Analytics for Fraud Detection
      1. Introduction
      2. Target Definition
      3. Linear Regression
      4. Logistic Regression
      5. Variable Selection for Linear and Logistic Regression
      6. Decision Trees
      7. Neural Networks
      8. Support Vector Machines
      9. Ensemble Methods
      10. Multiclass Classification Techniques
      11. Evaluating Predictive Models
      12. Other Performance Measures for Predictive Analytical Models
      13. Developing Predictive Models for Skewed Data Sets
      14. Fraud Performance Benchmarks
      15. References
    12. Chapter 5: Social Network Analysis for Fraud Detection
      1. Networks: Form, Components, Characteristics, and Their Applications
      2. Is Fraud a Social Phenomenon? An Introduction to Homophily
      3. Impact of the Neighborhood: Metrics
      4. Community Mining: Finding Groups of Fraudsters
      5. Extending the Graph: Toward a Bipartite Representation
      6. References
    13. Chapter 6: Fraud Analytics: Post-Processing
      1. Introduction
      2. The Analytical Fraud Model Life Cycle
      3. Model Representation
      4. Selecting the Sample to Investigate
      5. Fraud Alert and Case Management
      6. Visual Analytics
      7. Backtesting Analytical Fraud Models
      8. Model Design and Documentation
      9. References
    14. Chapter 7: Fraud Analytics: A Broader Perspective
      1. Introduction
      2. Data Quality
      3. Privacy
      4. Capital Calculation for Fraud Loss
      5. An Economic Perspective on Fraud Analytics
      6. In Versus Outsourcing
      7. Modeling Extensions
      8. The Internet of Things
      9. Corporate Fraud Governance
      10. References
    15. About the Authors
    16. Index
    17. End User License Agreement