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Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques A Guide to Data Science for Fraud Detection von Baesens, Bart (eBook)

  • Erscheinungsdatum: 27.07.2015
  • Verlag: Wiley
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Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques

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.

Produktinformationen

    Format: ePUB
    Kopierschutz: AdobeDRM
    Seitenzahl: 400
    Erscheinungsdatum: 27.07.2015
    Sprache: Englisch
    ISBN: 9781119146834
    Verlag: Wiley
    Größe: 16110 kBytes
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Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques

List of Figures

Figure 1.1 Fraud Triangle
Figure 1.2 Fire Incident Claim-Handling Process
Figure 1.3 The Fraud Cycle
Figure 1.4 Outlier Detection at the Data Item Level
Figure 1.5 Outlier Detection at the Data Set Level
Figure 1.6 The Fraud Analytics Process Model
Figure 1.7 Profile of a Fraud Data Scientist
Figure 1.8 Screenshot of Web of Science Statistics for Scientific Publications on Fraud between 1996 and 2014
Figure 2.1 Aggregating Normalized Data Tables into a Non-Normalized Data Table
Figure 2.2 Pie Charts for Exploratory Data Analysis
Figure 2.3 Benford's Law Describing the Frequency Distribution of the First Digit
Figure 2.4 Multivariate Outliers
Figure 2.5 Histogram for Outlier Detection
Figure 2.6 Box Plots for Outlier Detection
Figure 2.7 Using the z -Scores for Truncation
Figure 2.8 Default Risk Versus Age
Figure 2.9 Illustration of Principal Component Analysis in a Two-Dimensional Data Set
Figure 3.1 3D Scatter Plot for Detecting Outliers
Figure 3.2 OLAP Cube for Fraud Detection
Figure 3.3 Example Pivot Table for Credit Card Fraud Detection
Figure 3.4 Break-Point Analysis
Figure 3.5 Peer-Group Analysis
Figure 3.6 Cluster Analysis for Fraud Detection
Figure 3.7 Hierarchical Versus Nonhierarchical Clustering Techniques
Figure 3.8 Euclidean Versus Manhattan Distance
Figure 3.9 Divisive Versus Agglomerative Hierarchical Clustering
Figure 3.10 Calculating Distances between Clusters
Figure 3.11 Example for Clustering Birds. The Numbers Indicate the Clustering Steps
Figure 3.12 Dendrogram for Birds Example. The Thick Black Line Indicates the Optimal Clustering
Figure 3.13 Screen Plot for Clustering
Figure 3.14 Scatter Plot of Hierarchical Clustering Data
Figure 3.15 Output of Hierarchical Clustering Procedures
Figure 3.16 k -Means Clustering: Start from Original Data
Figure 3.17 k -Means Clustering Iteration 1: Randomly Select Initial Cluster Centroids
Figure 3.18 k -Means Clustering Iteration 1: Assign Remaining Observations
Figure 3.19 k -Means Iteration Step 2: Recalculate Cluster Centroids
Figure 3.20 k -Means Clustering Iteration 2: Reassign Observations
Figure 3.21 k -Means Clustering Iteration 3: Recalculate Cluster Centroids
Figure 3.22 k -Means Clustering Iteration 3: Reassign Observations
Figure 3.23 Rectangular Versus Hexagonal SOM Grid
Figure 3.24 Clustering Countries Using SOMs
Figure 3.25 Component Plane for Literacy
Figure 3.26 Component Plane for Political Rights
Figure 3.27 Must-Link and Cannot-Link Constraints in Semi-Supervised Clustering
Figure 3.28 d -Constraints in Semi-Supervised Clustering
Figure 3.29 -Constraints in Semi-Supervised Clustering
Figure 3.30 Cluster Profiling Using Histograms
Figure 3.31 Using Decision Trees for Clustering Interpretation
Figure 3.32 One-Class Support Vector Machines

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