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ImprovingCreditCardFraudDetecConusingCNN/GAN

RajeshSabari,rajeshsa@stanford.edu

RajeshSabarirajeshsa@stanford.edu

Contact1. S.Maes,K.Tuyls,B.Vanschoenwinkel,andB.Manderick,“CreditCardFraudDetecAonUsingBayesianandNeuralNetworks,”

2. K.Fu,D.Cheng,Y.Tu,andL.Zhang,“CreditCardFraudDetecAonUsingConvoluAonalNeuralNetworks,”

References

UsingGANfor5000roundspiqngthegeneratornetworkagainstthediscriminatornetwork,makinguseofthecross-entropylossfromthediscriminatortotrainthenetworksforimprovingclassificaAoneffecAvenessinCreditCardFraudDetecAon.Theaugmentedimageispassedthrough1x29convoluAonallayerfollowedbyafullyconnecteddenselayerstofinallyhaveaSoGmaxpredictor

MoCvaCon/Summary

Dataset

ThebaselinemodelswereLogisAcRegression,RandomForestandGaussianNB.3DLmodelsusedare2layerMLP,GAN,two1DConvlayer,maxpooling,afullyconnected(300neurons)andsoGmaxclassifier(2classes). prediction).

Models

CNN(bestperformance):0.860230099502RandomForestClassifier:0.846437(baseline)

MLPClassifier(2Layerwithdropout):0.8085106382978723MLPClassifier(1Layer):0.707243346007604

Discussion

CNNwithenhancedfrauddatasetthroughGANyieldedhightrainingandtestaccuracyonourtrainingandtestsets.AddingdropoutcertainlyyieldedlowervarianceandaddiAonallayersinMLPenhancedperformance.ThepoorgeneralizaAonisaresultofourdatasetnotbeingdiverse.Bytheendof5000trainingiteraAonsthegeneratedfraudimagedparernstartedtomimicactualfraud

Conclusions

ModelResults

DatasetsusedfortraininginthisprojectarefromKagglewith31featuresincludingtheAmeandamountoftransacAonaswellasalabelwhetherthattransacAonwasfraudulentornot.ThevariablesarePCAtransformed.99.83%oftransacAonsinthisdatasetwerenotfradulentwhileonly0.17%werefradulent