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

Data Mining For Credit Card Fraud: A Comparative Study

XxxxxxxxDSCI 5240 | Dr. Nick Evangelopoulos

Graduate Presentation

Graduate Presentation | DSCI 5240 | Xxxxxxx 2

OverviewO Credit Card FraudO Data Mining TechniquesO DataO Experimental SetupO Results


Credit Card FraudO Two Types:

O Application FraudO Obtain new cards using false information

O Behavioral FraudO Mail theftO Stolen/lost cardO Counterfeit card


Credit Card FraudO Online Revenue loss due to Fraud

(cybersource.com)


Data Mining TechniquesO Logistic Regression

O Used to predict outcome of categorical dependent variable

O Fraud variable is binaryO Support Vector MachinesO Random Forest


Support Vector Machines (SVM)

O Supervised learning models with associated learning algorithms that analyze and recognize patterns

O Linear classifiers that work in high dimensional feature space that is non-linear mapping of input space

O Two properties of SVMO Kernel representationO Margin optimization


Random Forest (RF)O Ensemble of classification treesO Performs well when individual members are

dissimilar


Data: DatasetsO 13 Months of data (Jan 2006 – Jan 2007)O 50 Million credit card transactions on 1 Million

credit cardsO 2420 known fraudulent transactions with 506

credit cards


Percentage of Transaction by transaction type


Data Selection


Primary attributes in Dataset


Derived Attributes


Experimental SetupO For SVM, Gaussian radial basis function was used

as the kernel functionO For Random Forest, number of attributes

considered at the node and number of trees was set.

O Data were sampled at different rates using random under sampling of majority class


Training and testing data


Results


Proportion of fraud captured at different depths


Fraud Capture Rate w/ Different Fraud Rates in Training Data


ConclusionO Examine the performance of two data mining techniques

O SVM and RF together with logistic regressionO Used real life data set from Jan 2006 – Jan 2007O Used data undersampling approach to sample dataO Random forest showed much higher performance at

upper file depthsO SVM performance at the upper file depths tended to

increase with lower proportion of fraud in the training data

O Random forest demonstrated overall better performance


Questions

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

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