Fraud Detection with Amazon SageMaker
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This presentation was given at AWS Builders' Days in December 2018
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Fraud Detection with Amazon SageMaker
- 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Greg Share, Solutions Architect December 10th 2018 AWS Builders’ Day London Fraud Detection with Amazon Sagemaker
- 2. What we will cover in this session • Fraud Detection • Machine Learning @ Amazon • Amazon SageMaker • Using SageMaker to Detect Fraud
- 3. Payment Fraud is an ongoing concern for FS organizations In 2016… $9b of fraud losses in the US* $22.8b of fraud losses globally* In 2021… $32.96b of projected fraud losses globally* * From The Nilson Report, October 2017, Issue 1118 (https://nilsonreport.com/upload/content_promo/The_Nilson_Report_Issue_1118.pdf)
- 4. Types of Fraudulent Behaviour Endpoint Authentication ☞ stolen card or machine Layer 1
- 5. Endpoint Authentication ☞ stolen card or machine Layer 1 Anomaly within a session ☞ Irregular behaviour within a session—e.g. transfer before balance Layer 2 Types of Fraudulent Behaviour
- 6. Endpoint Authentication ☞ stolen card or machine Layer 1 Anomaly within a session ☞ Irregular behaviour within a session—e.g. transfer before balance Layer 2 Anomaly within an account ☞ Irregular transactions—e.g. spike in transfer and recipients Layer 3 Types of Fraudulent Behaviour
- 7. Endpoint Authentication ☞ stolen card or machine Layer 1 Anomaly within a session ☞ Irregular behaviour within a session—e.g. transfer before balance Layer 2 Anomaly within an account ☞ Irregular transactions—e.g. spike in transfer and recipients Layer 3 Anomaly within multiple channels of the same account ☞ Irregular transactions across channels—e.g. spike in transfer and recipients Layer 4 Types of Fraudulent Behaviour
- 8. Endpoint Authentication ☞ stolen card or machine Layer 1 Anomaly within a session ☞ Irregular behaviour within a session—e.g. transfer before balance Layer 2 Anomaly within an account ☞ Irregular transactions—e.g. spike in transfer and recipients Layer 3 Anomaly within multiple channels of the same account ☞ Irregular transactions across channels—e.g. spike in transfer and recipients Layer 4 Anomaly within multiple channels of multiple accounts ☞ Irregular transactions across channels and accounts Layer 5 Types of Fraudulent Behaviour
- 10. Rule-Based Fraud Detection – Shortcomings Static Rules Bug-Prone Complicated Cannot ScaleAlways Behind
- 11. Requirements for a modern solution Self Improving Easy to Maintain ScalableReal-timeDynamic Rules
- 12. Machine Learning – Supervised Learning Understand your data Algorithmically discover hidden patterns Generalize solution algorithm Apply solution to unseen patterns Make predictions
- 14. Our Mission: Put machine learning in the hands of every developer and data scientist Machine Learning at AWS
- 15. Machine Learning at Amazon: A long heritage Voice driven interactions Fulfillment automation & inventory management Personalized recommendations Drones Inventing entirely new customer experiences
- 17. Machine Learning at Amazon: A long heritage Voice driven interactions Fulfillment automation & inventory management Personalized recommendations Drones Inventing entirely new customer experiences
- 18. 1000s of customers running Machine Learning on AWS
- 19. The AWS Machine Learning Stack PLATFORMS APPLICATION SERVICES R E K O G N I T I O N R E K O G N I T I O N V I D E O P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X Amazon SageMaker Amazon Mechanical Turk FRAMEWORKS KERAS P3 NVIDIA Tesla V100 GPU accelerated for AI/ML training Machine Learning AMIs INFRASTRUCTURE & Greengrass ML Amazon Deep Learning AMIs Compute intensive instances for AI/ML Inference C5
- 20. The AWS Machine Learning Stack PLATFORMS APPLICATION SERVICES R E K O G N I T I O N R E K O G N I T I O N V I D E O P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X Amazon SageMaker Amazon Mechanical Turk FRAMEWORKS KERAS P3 NVIDIA Tesla V100 GPU accelerated for AI/ML training Machine Learning AMIs INFRASTRUCTURE & Greengrass ML Amazon Deep Learning AMIs Compute intensive instances for AI/ML Inference C5
- 21. The AWS Machine Learning Stack PLATFORMS APPLICATION SERVICES R E K O G N I T I O N R E K O G N I T I O N V I D E O P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X Amazon SageMaker Amazon Mechanical Turk FRAMEWORKS KERAS P3 NVIDIA Tesla V100 GPU accelerated for AI/ML training Machine Learning AMIs INFRASTRUCTURE & Greengrass ML Amazon Deep Learning AMIs Compute intensive instances for AI/ML Inference C5
- 22. The AWS Machine Learning Stack PLATFORMS APPLICATION SERVICES R E K O G N I T I O N R E K O G N I T I O N V I D E O P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X Amazon SageMaker Amazon Mechanical Turk FRAMEWORKS KERAS P3 NVIDIA Tesla V100 GPU accelerated for AI/ML training Machine Learning AMIs INFRASTRUCTURE & Greengrass ML Amazon Deep Learning AMIs Compute intensive instances for AI/ML Inference C5
- 23. ML is still too complicated for everyday developers Collect and prepare training data Choose and optimize your ML algorithm Set up and manage environments for training Train and tune model (trial and error) Deploy model in production Scale and manage the production environment
- 24. A managed service that provides the quickest and easiest way for data scientists and developers to get ML models from idea to production Amazon SageMaker
- 25. Amazon SageMaker Collect and prepare training data Choose and optimize your ML algorithm Set up and manage environments for training Train and tune model (trial and error) Deploy model in production Scale and manage the production environment Easily build, train, and deploy machine learning models
- 26. Amazon SageMaker Pre-built notebooks for common problems K-Means Clustering Principal Component Analysis Neural Topic Modelling Factorization Machines Linear Learner – Regression DeepAR Forecasting XGBoost Latent Dirichlet Allocation Image Classification Seq2Seq Linear Learner – Classification BlazingText Random Cut Forest ALGORITHMS Apache MXNet TensorFlow Caffe2, CNTK, PyTorch, Torch FRAMEWORKS S e t u p a n d m a n a g e e n v i r o n m e n t s f o r t r a i n i n g T r a i n a n d t u n e m o d e l ( t r i a l a n d e r r o r ) D e p l o y m o d e l i n p r o d u c t i o n S c a l e a n d m a n a g e t h e p r o d u c t i o n e n v i r o n m e n t Built-in, high- performance algorithms Build
- 27. Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization Build Train Deploy model in production Scale and manage the production environment Amazon SageMaker
- 28. Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high- performance algorithms One-click training Hyperparameter optimization Build Train Deploy Amazon SageMaker
- 29. Amazon Sagemaker for Fraud Detection
- 30. Model hosting (SM) Calculate features Reader Cleanser Processor Data Look-up Training Feature store Model training (SM) Model Client service Amazon EMR Real-time Fraud Detection in AWS with Amazon SageMaker
- 31. • Training algorithm / inference code is packaged in a Docker image published on Amazon ECR • SageMaker pulls the training algorithm image from Amazon ECR into the Model Training Service • Amazon SM downloads or streams the training data and runs the training algorithm on the data. • After training, Amazon SM uploads model artifacts to Amazon S3 • For inference, Amazon SM pulls the model artifacts and the inference image from Amazon ECR, into the Model Hosting Service • Amazon SM exposes an inference endpoint for client applications to send prediction requests to check Fraud • Ground truth data collected from the client application could be sent into the training bucket to retrain and update the model Deploying a Model on Amazon SageMaker
- 32. Linear Learner Regression: Estimate a real valued function Binary classification: Predict a 0/1 class
- 33. Demo
- 34. Resources: Demo Notebook: https://github.com/cyrusmvahid/sagemaker- demos/blob/master/credircard_fraud/linearlearner-blogpost- part2.ipynb Sagemaker Docs: https://docs.aws.amazon.com/sagemaker/latest/dg/how-it- works-training.html Kaggle Dataset: https://www.kaggle.com/mlg- ulb/creditcardfraud/
- 35. Thank you!