PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, I’ll present how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. I’ll elaborate on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. I’ll explain how we employ sophisticated machine learning tools – open source and in-house developed. I will also present results from experiments conducted on a very large graph data set containing millions of edges and vertices.

1. Large Scale Machine Learning for Fraud
Prevention
Disclaimer: views expressed here are my own and do not
necessarily represent the views of PayPal, its affiliates or
subsidiaries.

2. Agenda Introduction
Fraud Prevention
Algorithm
Experiments
Conclusion
©2016 PayPal Inc. Confidential and proprietary.

3. INTRODUCTION

4. © 2016 PayPal Inc. Confidential and proprietary.
About Me
• Software Engineer/Data Scientist/ML Researcher
• Ph. D Computer Science
• Research in Face Recognition, Phishing/Spam, Fraud Prevention
4

5. Vibrant
developer
ecosyste
m
Efficient
payment
processing
with low SLA
active customer
accounts
200M
Secure
data
storage
&
handling
Traditional
& NoSQL
databases
PayPal operates
one of the largest
PRIVATE
CLOUDS
in the world
We have transformed
core business
processes into robust
SERVICE-BASED
PLATFORMS
The power of
our platform
Our technology transformation enables us to:
• Process payments at tremendous scale
• Accelerate the innovation of new products
• Engage world-class developers & technologists
About PayPal

6. FRAUD PREVENTION

7. Fraud Prevention @ PayPal
Robust feature engineering, machine
learning and statistical models
Highly scalable and multi-layered
infrastructure software
Superior team of data scientists,
researchers, financial and intelligence
analysts
Images source:

8. Fraud Prevention @ PayPal
• Employs advanced machine learning and statistical models to flag
fraudulent behavior up-front
• More sophisticated algorithms after transaction is complete
Transaction Level
• Monitor account level activity to identify abusive behavior
• Abusive pattern include frequent payments, suspicious profile
changes
Account Level
• Monitor account-to-account interaction
• Frequent transfer of money from several accounts to one central
account
Network Level

9. Fraud Prevention – What are we up against?
Fraudsters are becoming increasingly smarter and adaptive
Need cost-effective solutions that can model complex attack
patterns not previously observed
Need scalable and computationally efficient prediction models

10. © 2016 PayPal Inc. Confidential and proprietary.
Fraud Prevention – What are we up against?
• Much harder to get performance lift on
our flagship models
• Need to re-look at all aspects of
traditional model building
• Need out-of-the-box thinking
10
Area we are missing (AUC 0.96)

11. © 2016 PayPal Inc. Confidential and proprietary.
Fraud Prevention – What can we do to build better models?
11
feature1 …. featureN ……… Target
(Label)
d1
d2
…
dM
…..
Better
feature
Better
labeling
Advanced ML
Algorithms
Bigger
better data

12. LSML ALGORITHMS – ACTIVE LEARNING,
DEEP LEARNING, GBT

13. © 2016 PayPal Inc. Confidential and proprietary.
Active Learning – What is it?
• Supervised learning algorithms require
data to be labeled
• Labelling is difficult, time-consuming
and expensive : Active Learning to the
rescue
• Idea – ML Algorithm can achieve better
accuracy if it is allowed to “choose the
data” from which it learns*
• Overcome labelling bottleneck by asking
queries (unlabeled data) to be labeled by
human
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Unlabeled
Data
Labeled Data
Human Annotator
Machine Learning Model
(Re)Build Model
Select Queries
Source*: Burr Settles

14. © 2016 PayPal Inc. Confidential and proprietary.
Active Learning – What is it?
• Scenarios
• Membership Query Synthesis – request labels for ‘any’
unlabeled instance in input space
• Stream-based Selective Sampling – unlabeled instance is drawn
one at a time & learner decides whether to discard or query
• Pool-based Sampling – instances are queried from a pool
according to informative-ness measure
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15. © 2016 PayPal Inc. Confidential and proprietary.
Active Learning – What is it?
• Query Strategy Frameworks
• Uncertainty Sampling
• Query-By-Committee
• Expected Model Change
• Expected Error Reduction
• Variance Reduction
• Density Weighted Methods
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16. © 2016 PayPal Inc. Confidential and proprietary.
Active Learning –Toy Example
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Toy data – 400 instances Model using random sampling
70% accuracy
Model using active learning
Uncertainty sampling – 90% accuracy

17. © 2016 PayPal Inc. Confidential and proprietary.
Active Learning For Fraud Prevention – Why is it unique?
17
• Data is unbalanced
• Fraud labelling require trained experts. Can’t be outsourced
• Fraud labelling is time consuming
• Fraud labelling require more than just individual instances. Require before
& after transactions
• Fraud labelling require data from other entities (ex: IP address)
• Fraud labelling require aggregate data
• Fraud tag mature at different times (ex: chargeback) & not instantaneous

18. © 2016 PayPal Inc. Confidential and proprietary.
Active Learning For Fraud Prevention – High Level Framework
18
Labeled
Data
Create Bags
Deep Learning
Model
GBT Model
(Re)Build Models
Unlabeled
Data
Predict
Query By Committee
Human Expert
Create
Statistics
Active
Feature
Engineering
Simulate
Features

19. © 2016 PayPal Inc. Confidential and proprietary.
Modeling Algorithm – Deep Learning
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Input Layer
Hidden Layers
Output Layer
• If a network has many layers of non-linearity, it is “deep”
• Need scalable platform
• Need lots of training data

20. © 2016 PayPal Inc. Confidential and proprietary.
Modeling Algorithm – Deep Learning
20
•NetworkTopology – Feed forward
•Key Parameters
• # of hidden layers
• # of neurons @ each hidden layer
• Regularization
• Activation function

21. © 2016 PayPal Inc. Confidential and proprietary.
Modeling Algorithm – Gradient BoostingTrees
21
• GBT = Gradient Descent + Boosting
• Fit an additive (ensemble) model in forward stage wise manner
• In each stage introduce a new model to compensate the shortcomings
of existing models

22. © 2016 PayPal Inc. Confidential and proprietary.
Modeling Algorithm – Gradient BoostingTrees
22
• Strengths
• No pre-processing required
• Robust
• Scalable
• Weaknesses
• Overfits (Need to find proper stopping point)
• Sensitive to noise
• Key Parameters
• # of trees
• Max depth
• Max observations
• Learning rate

23. EXPERIMENTS

24. © 2016 PayPal Inc. Confidential and proprietary.
Datasets
24
• Training Data
• 1 year
• 11 million transactions (1 million for active labelling)
• Test Data
• 4 months
• 4 million transactions
• # of features
• 500 - 600

25. © 2016 PayPal Inc. Confidential and proprietary.
Tools
25
• H2O
• Open source
• Scalable
• Robust
• Deep Learning & GBM implementations
• R
• Open source
• Active learning package

26. © 2016 PayPal Inc. Confidential and proprietary. 26
# of instances queried AUC (*weighted)
0 0.960
1000 0.961
10000 0.963
50000 0.971
100000 0.975
500000 0.977
1000000 0.979
Early Results – Active Learning Shows Promise…

27. CONCLUSIONS

28. © 2016 PayPal Inc. Confidential and proprietary.
Conclusions
28
• Deep learning & GBT has shown tremendous performance for fraud
detection.
• Active learning shows promise in improving performance of these
champion models
• Active learning also significantly reduce our labelling cost