The document discusses the use of deep learning techniques for fraud prevention at PayPal, outlining the company's competitive advantage and extensive dataset. It emphasizes the efficacy of machine learning models in identifying fraudulent behavior and the challenges faced during experiments with deep learning infrastructure. Ultimately, it concludes that deep learning can significantly enhance fraud detection performance while noting the necessity for optimization and addressing data import issues.

1. Fraud Prevention Using Deep Learning
Venkatesh Ramanathan
H2O World 2014
November 19, 2014

2. Outline(
About(PayPal(
Fraud(Preven3on(@(PayPal(
Fraud(Preven3on(Dilemma(&(Solu3on((Deep(Learning)(
Experimental(Setup(
Results(
Conclusions(

3. About PayPal
Unmatched CompetitiveAdvantage
+150M Active Digital
Wallets
Deep Relationships Core Competency
In Risk
Global Platform with
Huge Momentum
4321
143M
2013
2012
123M

4. PAYMENT CODE WEARABLE TECH
QR scanning that generates
a payment code for easy
check out
Fully able to integrate with
existing POS systems; no
rip & replace
Available in select markets
today
Payments on any type
of mobile device
Available in select
markets today
About PayPal
Innovative leader in payment…

5. Fraud(Preven3on(@(PayPal(
StateDofDthe(art(feature(engineering,(
machine(learning(and(sta3s3cal(
models(
Highly(scalable(and(mul3Dlayered(
infrastructure(soIware((
Superior(team(of(data(scien3sts,(
researchers,(financial(and(
intelligence(analysts(

6. Fraud(Preven3on(@(PayPal(
• Employs(stateDofDthe(art(machine(learning(and(
sta3s3cal(models(to(flag(fraudulent(behavior(upDfront(
• More(sophis3cated(algorithms(aIer(transac3on(is(
complete(
Transac3on(
Level(
• Monitor(account(level(ac3vity(to(iden3fy(abusive(
behavior(
• Abusive(paPern(include(frequent(payments,(suspicious(
profile(changes(
Account(Level(
• Monitor(accountDtoDaccount(interac3on(
• Frequent(transfer(of(money(from(several(accounts(to(
one(central(account((
Network(Level(

7. Fraud(Preven3on(Dilemma(
Fraudsters(are(becoming(increasingly(smarter(and(
adap3ve(
Need(costDeffec3ve(solu3ons(that(can(model(
complex(aPack(paPerns(not(previously(observed(((
Need(scalable(and(computa3onally(efficient(
predic3on(models(

8. Fraud(Preven3on(Dilemma(
Solu3on:(Deep(Learning(
• Helps(to(unearth(lowDlevel(complex(abstrac3ons(
• Helps(to(learn(complex(highly(varying(func3ons(not(
present(in(the(training(examples(
• Widely(employed(for(image,(video(processing(and(object(
recogni3on(
Why(Deep(
Learning?(
• Highly(scalable(
• Superior(performance(
• Flexible(deployment(
• Work(seamlessly(with(other(big(data(frameworks(
• Simple(interface(
Why(H2O?(

9. Experiment(
• Dataset(
– 160(million(records(
– 1500(features((150(categorical)(
– 0.6TB(compressed(in(HDFS(
• Infrastructure(
– 800(node(Hadoop((CDH3)(cluster(
• Decision(
– fraud/notDfraud(

10. Experiment(
R(
H2O(
Mapper(
HDFS( HDFS(
• Setup(
– 800(node(Hadoop(
(CDH3)(cluster(
– R(as(a(client(
H2O(
Mapper(
• H2O(cloud(forma3on(
failed(
– H2O(mapper(needs(
memory(upfront(
– Cluster(capacity(
limita3ons(

11. Experiment(
R(
H2O(
Cloud(
HDFS( HDFS(
• Setup(
– 800(node(Hadoop(
(CDH3)(cluster(
– 5(node(H2O(cloud((24(
CPUs;(144GB(RAM)(
– R(as(a(client(
H2O(
Cloud(
• Import(failed(
– Data(snappy(
compressed(

12. Experiment(
R(
H2O(
Cloud(
HDFS( HDFS(
• Setup(
– 800(node(Hadoop(
(CDH3)(cluster(
– 5(node(H2O(cloud((24(
CPUs;(144GB(RAM)(
– R(as(a(client(
– GZIP’ed(data(
H2O(
Cloud(
• Import(too(slow(
– 1GB/hour(
– Not(parallelized(

13. Experiment(
R(
H2O(
Cloud(
HDFS( HDFS(
• Setup(
– 800(node(Hadoop((CDH3)(
cluster(
– 5(node(H2O(cloud((24(
CPUs;(144GB(RAM)(
– R(as(a(client(
– GZIP’ed(data(
– Cliff’s(fix((1(GB(from(1(
hour(to(10(minutes)(
H2O(
Cloud(
• Deep(Learning(failed(
– Skipping(rows(if(it(had(
missing(values(
– 99%(of(rows(had(missing(
values(

14. Experiment(
R(
H2O(
Cloud(
HDFS( HDFS(
• Setup(
– 800(node(Hadoop((CDH3)(
cluster(
– 5(node(H2O(cloud((24(
CPUs;(144GB(RAM)(
– R(as(a(client(
– GZIP’ed(data(
– Cliff’s(fix((1(GB(from(1(
hour(to(10(minutes)(
– Arno’s(fixes(
H2O(
Cloud(
• Deep(Learning(slow(

15. Experiment(
R(
H2O(
Cloud(
HDFS( HDFS(
• Setup(
– 800(node(Hadoop((CDH3)(
cluster(
– 5(node(H2O(cloud((24(
CPUs;(144GB(RAM)(
– R(as(a(client(
– GZIP’ed(data(
– Cliff’s(fix((1(GB(from(1(
hour(to(10(minutes)(
– Arno’s(fixes(&(sugges3ons(
– Reduced(data(
• 10(million(rows((60%(
training;(20%(valida3on;(
20%(test)(
H2O(
Cloud(

16. Experimental(Design(
Parameter' Range'
#(of(hidden(layers( (2,(4,(6,(8(
#(of(neurons( 200,(300,(400,(500,(600,(700(
ac3va3on(func3on( Rec3fier;(Tanh;(Maxout;(Rec3fierWithDropout(
feature(subset( All,(subset1(–(subset7(
test(data(set( All,(week4(–(week8(
L1/L2(regulariza3on( 0(D(1(
epoch( 500(
10(million(rows/1500(features((60%(training;(20%(valida3on;(20%(test)(
((

17. Results(
#'of'hidden'layers'
(Rec6fier,'2'layer,'200'neurons,'
500'epoch,''L1/L2'='0)'
Area'Under'ROC'Curve'(AUC)'
'
2( 0.762(
4( 0.821(
6( 0.839(
8' 0.839'
How(much(depth(is(required?(
Best(
performance(
with(6(layers(

18. Results(
How(much(depth(is(required?(
Best(
performance(
with(600(
neurons(
#(of(hidden(layersD6(

19. Results(
Ac6va6on'func6on'
(6'layers;'600'neurons)'
AUC'
Tanh( 0.801(
Rec3fier( 0.856(
Maxout( 0.826(
Rec6fierWithDropout' 0.865'
Which(ac3va3on(func3on(produces(best(result?(
Best(performance(
with(
Rec3fierWithDropout(

20. Results(
Feature'subset' AUC'
subset1( 0.836(
subset2( 0.847(
subset3' 0.849'
subset4( 0.844(
subset5( 0.834(
subset6( 0.786(
subset7' 0.751'
Which(subset(of(features(produces(best(result?(
Best(performance(
with(subset3;(
Worst(for(subset7(
(2/3rd(less(feature)(

21. Results(
Epoch:'500'
Hidden:'2'layers'
Neurons:'200'each'layer'
Subset7'
'
AUC'
Epoch:'500'
Hidden:'6'layers'
Neurons:'600'each'layer'
Subset7'
'
AUC'
0.751( 0.86(
Can(deep(network(improve(subset7?(
11%(improvement(in(
performance((with(
1/3rd(of(the(feature(
set(

22. Results(
Test'Set' AUC'
Week(4( 0.856(
Week(8( 0.861(
Week(12( 0.852(
Week(16( 0.858(
Week(20( 0.853(
Is(deep(learning(temporally(robust?(
Performance(within(
1%(difference(upto(20(
weeks(

23. Conclusions(
• Deep(Learning(using(H2O(is(beneficial(for(payment(fraud(
preven3on(
– Network(architecture(D(6(layers(with(600(neurons(each(performed(the(
best(
– Ac3va3on(func3on((D(Rec3fierWithDropout(performed(the(best(
– Improved(performance(with(limited(feature(set(&(a(deep(network(
(11%(improvement(with(a(third(of(the(original(feature(set,(6(hidden(
layers,(600(neurons(each)(
– Robust(to(temporal(varia3ons(

24. Conclusions(
• Lessons(learned(in(using(H2O(
– Slow(import(process((
– Issues(with(compressed(data,(missing(values,(sparse(data(
– Require(knowledge(of(performance(knobs(
– Fantas3c(support(from(H2O(team(
• Next(Steps(
– Mul3Dclass(classifica3on(
– Produc3onalize(

25. Thank(You!(