fraud detection system using deep neural networks
TRANSCRIPT
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]
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.
AnnualReportsCybersource
AnnualReportsCybersource
Stateoftheart
BS (bivariate statistics)
Feature Extractions
PCA (principal component analysis)
Information Gain
PCA + IG = GPCA
Etc.
WhyDeepLearning
• HighnonlinearityDataset• Amountofdata• Alotoffeatures• Mostlyunlabeleddata
Deepneuralnetworks
Standardneuralnetworks
Standardneuralnetworks
Back-propaga+on
Deepneuralnetworks
[H. Karisma et al,2016]
Pre-training
• Denoisingauto-encoder• RestrictedBoltzmannmachine
Auto-encoder
Pre-training
1 2 3
Deepneuralnetworkforrepricinggapforcas+ng(bank)
• Equalsnetworktopology• Highnonlinearity• Almostalla_ributeshaveconUnuousvalues• Usingauto-encoder• Minimummeansquarederror:10-9
0.00
0.20
0.40
0.60
0.80
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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]
Networktopologies
[H. Karisma et al, 2016]
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
Dataset
• Dataset:4000• Fraud:32(confirmfraud)• GoodtransacUon:2000• Falseaddresscases:2157• SuspecttransacUon:500• A_ributes:+/-102• Non-linearity:High
Featureengineering
• OrderinformaUon(customerinfo,billinginfo,shippinginfo,items,itemcategory,amount,discount,etc)
• CardVerificaUonnumber(forBINnumber)• Postaladdress• Googlemapslookup(distancebetweenshippingandbilling)• Telephonearealookup• Credithistory• Customerorderhistory• Ordervelocitymonitoring• IPGeolocaUon• ValueSimilarity(shippingandbillingaddress,customeremailand
customername)
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.
Featureengineering
Itwilladdmorethan60featurestodataset.• Look-upfeatures• Velocityfeatures• Otherpreprocessing
02468
10
0 1 2 3 4 5 6 7
coun
t
emailgivencardchanging
changecard
Linear(changecard)
0
1
2
3
0 2 4 6 8
coun
temailgivenchardchanging
changecard
Linear(changecard)
0123456
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coun
t
emailcountoftransac+on
Result
• Accuracy:89.475• ConfusionMatrix• MSE:8.31 x 10^-6
Fraud Good predict/actual
1636 364 Fraud57 1943 Good
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0.60
0.80
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1 26 51 76 101 126 151 176 201 226 251 276
MSE(1
0^-3)
ITERATION(10^2)
DeepNeuralNetworkforFDS
Challenges
• Unbalancingdataset• FraudistransacUonperspecUvetofraudisperson
perspecUve(datastructureschanging)• Eventdata(fromcheckingpage,orderunUltransacUon/
checkout)• GPUopUmizaUon• Networkmodelarchitecture• Socialnetworkfeatures(textandnetwork)• RestrictedBoltzmannmachineoranotherpre-training• Graphtheory
THANK YOU
Any question?