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

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

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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.

    Wouter Verbeke: author's other books


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

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    Table of Contents for Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection
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    Index
    A
    1. Absolute deviation
    2. Account data
    3. Account management database, information storage
    4. Accuracy ratio (AR)
      1. AUC, linear relation
      2. calculation, example
    5. Acquisition costs
    6. Activation functions
    7. Actual fraud, predicted fraud (contrast)
    8. Adaptive boosting (Adaboost) procedure
    9. Adjacency list
    10. Adjacency matrix
      1. mathematical representation
    11. Administrative activities (fire incident claims)
    12. Administrators, experts (collusion)
    13. Affiliation networks
    14. Age
      1. default risk, contrast
      2. regression model
      3. split, entropy (calculation)
    15. Agglomerative hierarchical clustering
      1. divisive hierarchical clustering, contrast
      2. methods, usage
    16. Aggregate loss distribution
      1. description
      2. indicators
      3. Monte Carlo simulation
    17. Alert Type
    18. Analysis of variance (ANOVA) test
    19. Analytical fraud models
      1. backtesting
      2. calibration, backtesting
      3. design/documentation
      4. life cycle
      5. performance metric, monitoring
      6. stability, backtesting
    20. Analytical model life cycle
    21. Analytics, strategic contribution
    22. Anomaly detection
    23. Anticipating effect
    24. Anti-fraud steering group
    25. Anti-money laundering setting, cash transfers (clustering)
    26. Approval activities (fire incident claims)
    27. Approval cycle, absence
    28. AR. See Accuracy ratio
    29. Area under the ROC curve (AUC). See also Multiclass area under the ROC curve
      1. calculation (performance metric)
    30. Assignment decision. See Decision trees
    31. Association rule analysis
    32. Association rules
      1. consideration
      2. examples
    33. Attrition, problem
    34. AUC. See Area under the ROC curve
    35. Autoregressive integrated moving average (ARIMA)
    36. Average claim value, distribution
    37. Average path length
      1. network
    B
    1. Backward looking time horizon
    2. Backward variable selection
      1. procedure, usage
    3. Bagging
    4. Bankruptcy
      1. contrast
      2. filing
    5. Base class
    6. Bayesian methods
    7. Behavioral characteristics, example
    8. Behavioral information
    9. Benford's law
      1. deviation
      2. example
    10. Best matching unit (BMU)
      1. location
      2. weight vector
    11. Between-community edges
      1. quantification
      2. weight, sum
    12. Betweenness
      1. centrality
      2. illustration
      3. recalculation
    13. Binary classification
    14. Binary fraud target (modeling), linear regression (usage)
    15. Binary link
      1. statistics
      2. usage
    16. Binary logistic regression
    17. Binary red-flag indicators
    18. Binary weight
    19. Binomial distribution, usage
    20. Binomial test, usage
    21. Bipartite graph
      1. connectivity matrix
      2. example
      3. node types
      4. usage
    22. Bipartite networks
    23. Bipartite representation
    24. Birds, clustering (example)
      1. dendrogram
    25. BMU. See Best matching unit
    26. Boosting. See also Adaptive boosting
    27. Bootstrapping
      1. procedure
        1. adoption
        2. usage
    28. Bootstraps
    29. Bottom-up approaches
    30. Bottom-up clustering
    31. Bounding function. See Logistic regression
    32. Break-point analysis
      1. intra-account fraud detection method
        1. example
    33. Brier score (BS), measurement
    34. Browser-based digital dashboard, usage
    35. Business policy
      1. customer relationship management, example
    36. Business rules, set (usage)
    C
    1. C4.5 (decision tree)
    2. CAIRO matrix
    3. Call detail records, example
    4. Cannot-link constraints. See Semi-supervised clustering
    5. CAP. See Cumulative accuracy profile
    6. Capital, sufficient level
    7. CART (decision tree)
    8. Cascade correlation
    9. Case management
      1. environment
    10. Categorical data
    11. Categorization
      1. Chi-squared analysis, usage
    12. Centrality. See Closeness
      1. metrics
        1. components
    13. CHAID (decision tree)
    14. Chief analytics officer (CAO), addition
    15. Chi-squared analysis, usage
    16. Chi-squared distance, calculation
    17. Chi-squared distribution
      1. test statistic, relationship
    18. Chi-squared test statistic
    19. Claim
      1. amounts, distribution
      2. geographical distribution
        1. enlargement
      3. score
      4. value, distribution. See Average claim value.
    20. ClaimID
    21. Classification. See also Binary classification
      1. accuracy
      2. data set, example
      3. error
      4. measures, dependence
      5. model
        1. calibration, monitoring
      6. SVMs, procedure
      7. techniques. See Multiclass classification techniques.
    22. Classifier. See Probabilistic relational neighbor
      1. cost sensitivity
      2. relational neighbor classifier
    23. Click fraud
    24. Closed-form solutions
    25. Closed-loop fraud-management strategy, adoption
    26. Closeness
      1. centrality
      2. summary
    27. Cluster centroids
      1. random selection
      2. recalculation
      3. stability
    28. ClusterID
    29. Clustering. See also Spectral clustering
      1. claims
      2. constraints, usage
      3. countries, SOMs (usage)
      4. dendrogram
      5. distance metrics
      6. example. See Birds.
      7. interpretation, decision trees (usage)
      8. screen plot
      9. semi-supervised clustering, must-link/cannot-link constraints
      10. solutions, evaluation/interpretation
      11. steps, indication
      12. techniques, contrast
      13. transactions
    30. Cluster profiling, histograms (usage)
    31. Clusters
      1. analysis, usage. See Fraud detection.
      2. distances, calculation
    32. CNA. See Complex network analysis
    33. Coefficient of determination (R2), performance metric
    34. Collective inference algorithms
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