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

List of Figures

1. Figure 1.1 Fraud Triangle
2. Figure 1.2 Fire Incident Claim-Handling Process
3. Figure 1.3 The Fraud Cycle
4. Figure 1.4 Outlier Detection at the Data Item Level
5. Figure 1.5 Outlier Detection at the Data Set Level
6. Figure 1.6 The Fraud Analytics Process Model
7. Figure 1.7 Profile of a Fraud Data Scientist
8. Figure 1.8 Screenshot of Web of Science Statistics for Scientific Publications on Fraud between 1996 and 2014
9. Figure 2.1 Aggregating Normalized Data Tables into a Non-Normalized Data Table
10. Figure 2.2 Pie Charts for Exploratory Data Analysis
11. Figure 2.3 Benford's Law Describing the Frequency Distribution of the First Digit
12. Figure 2.4 Multivariate Outliers
13. Figure 2.5 Histogram for Outlier Detection
14. Figure 2.6 Box Plots for Outlier Detection
15. Figure 2.7 Using the z-Scores for Truncation
16. Figure 2.8 Default Risk Versus Age
17. Figure 2.9 Illustration of Principal Component Analysis in a Two-Dimensional Data Set
18. Figure 3.1 3D Scatter Plot for Detecting Outliers
19. Figure 3.2 OLAP Cube for Fraud Detection
20. Figure 3.3 Example Pivot Table for Credit Card Fraud Detection
21. Figure 3.4 Break-Point Analysis
22. Figure 3.5 Peer-Group Analysis
23. Figure 3.6 Cluster Analysis for Fraud Detection
24. Figure 3.7 Hierarchical Versus Nonhierarchical Clustering Techniques
25. Figure 3.8 Euclidean Versus Manhattan Distance
26. Figure 3.9 Divisive Versus Agglomerative Hierarchical Clustering
27. Figure 3.10 Calculating Distances between Clusters
28. Figure 3.11 Example for Clustering Birds. The Numbers Indicate the Clustering Steps ...