1. Data Mining For Credit Card Fraud: A Comparative Study
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DSCI 5240 | Dr. Nick Evangelopoulos
Graduate Presentation

2. Overview Credit Card Fraud Data Mining Techniques Data
Experimental Setup
Results
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3. Credit Card Fraud Two Types: Application Fraud Behavioral Fraud
Obtain new cards using false information
Behavioral Fraud
Mail theft
Stolen/lost card
Counterfeit card
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4. Credit Card Fraud Online Revenue loss due to Fraud (cybersource.com)
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5. Data Mining Techniques
Logistic Regression
Used to predict outcome of categorical dependent variable
Fraud variable is binary
Support Vector Machines
Random Forest
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6. Support Vector Machines (SVM)
Supervised learning models with associated learning algorithms that analyze and recognize patterns
Linear classifiers that work in high dimensional feature space that is non-linear mapping of input space
Two properties of SVM
Kernel representation
Margin optimization
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7. Random Forest (RF) Ensemble of classification trees
Performs well when individual members are dissimilar
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8. Data: Datasets 13 Months of data (Jan 2006 – Jan 2007)
50 Million credit card transactions on 1 Million credit cards
2420 known fraudulent transactions with 506 credit cards
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9. Percentage of Transaction by transaction type
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10. Data Selection
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11. Primary attributes in Dataset
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12. Derived Attributes
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13. Experimental Setup
For SVM, Gaussian radial basis function was used as the kernel function
For Random Forest, number of attributes considered at the node and number of trees was set.
Data were sampled at different rates using random under sampling of majority class
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14. Training and testing data
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15. Results
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16. Proportion of fraud captured at different depths
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17. Fraud Capture Rate w/ Different Fraud Rates in Training Data
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18. Conclusion Examine the performance of two data mining techniques
SVM and RF together with logistic regression
Used real life data set from Jan 2006 – Jan 2007
Used data undersampling approach to sample data
Random forest showed much higher performance at upper file depths
SVM performance at the upper file depths tended to increase with lower proportion of fraud in the training data
Random forest demonstrated overall better performance
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19. Questions
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