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Active Learning for Fraud Prevention

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DataWorks Summit/Hadoop Summit
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Active Learning for Fraud Prevention

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Active Learning for Fraud Prevention
Agenda Introduction Fraud Prevention Algorithm Experiments Conclusion ©2016 PayPal Inc. Confidential and proprietary.
INTRODUCTION
© 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
developers +2.5 MILLIONpayments/year 4.9 BILLION payments/ second at peak ~30 0 active customer accounts 184 M petabytes of data 42 database calls/ quarter 4.5 T 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
FRAUD PREVENTION
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:
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
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
© 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)
© 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
ALGORITHM – ACTIVE LEARNING
© 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 13 Unlabeled Data Labeled Data Human Annotator Machine Learning Model (Re)Build Model Select Queries Source*: Burr Settles
© 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 14
© 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 15
© 2016 PayPal Inc. Confidential and proprietary. Active Learning –Toy Example 16 Toy data – 400 instances Model using random sampling 70% accuracy Model using active learning Uncertainty sampling – 90% accuracy
© 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
© 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
© 2016 PayPal Inc. Confidential and proprietary. Modeling Algorithm – Deep Learning 19 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
© 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
© 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
© 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
EXPERIMENTS
© 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
© 2016 PayPal Inc. Confidential and proprietary. Tools 25 • H2O • Open source • Scalable • Robust • Deep Learning & GBM implementations • R • Open source • Active learning package
© 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…
CONCLUSIONS
© 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

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Active Learning for Fraud Prevention

  • 1. Active Learning for Fraud Prevention
  • 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. developers +2.5 MILLIONpayments/year 4.9 BILLION payments/ second at peak ~30 0 active customer accounts 184 M petabytes of data 42 database calls/ quarter 4.5 T 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
  • 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
  • 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 13 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 14
  • 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 15
  • 16. © 2016 PayPal Inc. Confidential and proprietary. Active Learning –Toy Example 16 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 19 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
  • 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…
  • 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

Editor's Notes

  1. *All Q1 except for active customer accounts
  2. .