Fraud Detection System using Deep Neural Networks
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Transcript of Fraud Detection System using Deep Neural Networks
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FraudulentTransac+onPayment Fraud (phishing, account take-over, carding)
System abuse (promo, content, account, logistic and payment methods especially COD)
Fraud not only result in financial losses but also produce some reputational risk.
Some security measures has been taken by bank or another multinational finance service.
[E. Duman et al, 2013]
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Stateoftheart
• SomemethodsusedinFraudDetec+onresearcharea:– GASS82.78%-91%andMBOalgorithm91.3%-94.35%– ANN91.74%– SVM83.06%
[E.Dumanetal,2013]
– Copula-basedmethod,extremeoutliereliminaUon,PCA,naïvebayes,regressionlogisUc,k-nearestneighbors,etc.
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AnnualReportsCybersource
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AnnualReportsCybersource
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Stateoftheart
BS (bivariate statistics)
Feature Extractions
PCA (principal component analysis)
Information Gain
PCA + IG = GPCA
Etc.
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WhyDeepLearning
• HighnonlinearityDataset• Amountofdata• Alotoffeatures• Mostlyunlabeleddata
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Deepneuralnetworks
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Standardneuralnetworks
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Standardneuralnetworks
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Back-propaga+on
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Deepneuralnetworks
[H. Karisma et al,2016]
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Pre-training
• Denoisingauto-encoder• RestrictedBoltzmannmachine
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Auto-encoder
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Pre-training
1 2 3
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Deepneuralnetworkforrepricinggapforcas+ng(bank)
• Equalsnetworktopology• Highnonlinearity• Almostalla_ributeshaveconUnuousvalues• Usingauto-encoder• Minimummeansquarederror:10-9
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1 31 61 91 121 151 181 211 241 271 301
MSE (10^-4)
Iteration (10^2) SB DNN [H. Karisma et al,2016]
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Networktopologies
[H. Karisma et al, 2016]
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AlgorithmParameters• Minimummeansquarederror:10^-8• Learningrate:0.75• Momentum:0.5• Topologynetwork:equalsforeachhiddenlayer• HiddenLayer:3HiddenLayer• Neuron/HiddenLayer:(26,26,26)• AcUvaUonfuncUon:sigmoidfuncUon• Auto-encoder(pre-training)parameters:
– Minimumsquarederror:10-5– Maxepoch:2000– Learningrate:0.5– Momentum:0.75– AcUvaUonfuncUon:sigmoidfuncUon
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Dataset
• Dataset:4000• Fraud:32(confirmfraud)• GoodtransacUon:2000• Falseaddresscases:2157• SuspecttransacUon:500• A_ributes:+/-102• Non-linearity:High
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Featureengineering
• OrderinformaUon(customerinfo,billinginfo,shippinginfo,items,itemcategory,amount,discount,etc)
• CardVerificaUonnumber(forBINnumber)• Postaladdress• Googlemapslookup(distancebetweenshippingandbilling)• Telephonearealookup• Credithistory• Customerorderhistory• Ordervelocitymonitoring• IPGeolocaUon• ValueSimilarity(shippingandbillingaddress,customeremailand
customername)
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Featureengineering(Velocity)
• Maskcardnumbergiven:billzip,cus+p,email,name,shipzip.(justcount)
• Maskcardnumbergiven:billzip,cus+p,email,name,shipzip.(changing)
• Emailgiven:billzip,cus+p,name,maskednumber,shipzip(justcount)
• Emailgiven:billzip,cus+p,name,maskednumber,shipzip(changing)
• Soonforbillzip,cus+p,name,andshipzip.Thencomputethegradient.
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Featureengineering
Itwilladdmorethan60featurestodataset.• Look-upfeatures• Velocityfeatures• Otherpreprocessing
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emailgivencardchanging
changecard
Linear(changecard)
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temailgivenchardchanging
changecard
Linear(changecard)
0123456
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emailcountoftransac+on
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Result
• Accuracy:89.475• ConfusionMatrix• MSE:8.31 x 10^-6
Fraud Good predict/actual
1636 364 Fraud57 1943 Good
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1 26 51 76 101 126 151 176 201 226 251 276
MSE(1
0^-3)
ITERATION(10^2)
DeepNeuralNetworkforFDS
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Challenges
• Unbalancingdataset• FraudistransacUonperspecUvetofraudisperson
perspecUve(datastructureschanging)• Eventdata(fromcheckingpage,orderunUltransacUon/
checkout)• GPUopUmizaUon• Networkmodelarchitecture• Socialnetworkfeatures(textandnetwork)• RestrictedBoltzmannmachineoranotherpre-training• Graphtheory
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THANK YOU
Any question?