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AWS re:Invent 2016: Fraud Detection with Amazon Machine Learning on AWS (FIN301)

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In this session, we provide programmatic guidance on building tools and applications to detect and manage fraud and unusual activity specific to financial services institutions. Payment fraud is an ongoing concern for merchants and credit card issuers alike and these activities impact all industries, but are specifically detrimental to Financial Services. We provide a step-by-step walkthrough of a reference solution to detect and address credit card fraud in real time by using Apache Apex and Amazon Machine Learning capabilities. We also outline different resource and performance optimization options and how to work data security into the fraud detection workflow.

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AWS re:Invent 2016: Fraud Detection with Amazon Machine Learning on AWS (FIN301)

  1. 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Pawan Agnihotri, Principal Solutions Architect, Global Financial Services November 28, 2016 FIN301 Fraud Detection and Machine Learning on AWS
  2. 2. Payments fraud is an ongoing concerns for Financial Services (FS) organizations. $8.5b of fraud losses in the US* $21.8b of fraud losses globally* * From The Nilson Report (https://www.nilsonreport.com/constant_contact_promo.php?id_promo=8) $31.7b of projected fraud losses globally in 2020* In 2015…
  3. 3. Addresses endpoint authentication Layer 1 Analyzes session behavior Layer 2 Monitors account behavior for a channel Layer 3 Monitoring account behavior across multiple channels Layer 4 Monitoring multiple account behaviors across multiple channels Layer 5 Solving for Multiple Layers Simultaneously
  4. 4. Rule-Based Fraud Detection Over Limit High Rate Stolen Card ? DENY APPROVE
  5. 5. Rules Fall Short for Fraud Detection Static set of rules Difficult to manageHuman errors & bias Cannot scale
  6. 6. Solution Requirements • Process billions of transactions a day • Make decisions in milliseconds • Train with large amounts of data • Secure and Align to compliance requirements • Low cost • Flexible and Adaptable • Agile and Scalable
  7. 7. Overview of Machine Learning
  8. 8. Supervised Learning
  9. 9. Supervised Learning Input Outcome
  10. 10. Supervised Learning Input Outcome Input Input Input Outcome Outcome Outcome
  11. 11. Supervised Learning Input Outcome Input Input Input Outcome Outcome Outcome Supervised Learning Known Historical Data
  12. 12. Supervised Learning Input Outcome Input Input Input Outcome Outcome Outcome Supervised Learning Unseen Input Same Outcome Known Historical Data
  13. 13. Tools of the Trade
  14. 14. Amazon Simple Storage Service (S3) • Highly scalable object storage • Files are stored as objects and organized into high-level folders called buckets • Store and retrieve data from anywhere on the web • Native support data encryption at rest • Data in transit to and from the service is encrypted using SSL. • Pay for exactly what you use • Highly durable (99.999999999% design) • Limitlessly scalable
  15. 15. Amazon Elastic Map Reduce (EMR) • Managed platform • MapReduce, Apache Spark, Presto • Launch a cluster in minutes • Open source distribution & MapR distribution • Elasticity of the cloud • Built in security features • Support for encryption of data at rest and in transit • Pay by the hour and save with Spot • Flexibility to customize
  16. 16. An Example EMR Cluster Master Node r3.2xlarge Slave Group - Core c3.2xlarge Slave Group – Task m3.xlarge Slave Group – Task m3.2xlarge (EC2 Spot) HDFS (DataNode). YARN (NodeManager). NameNode (HDFS) ResourceManager (YARN)
  17. 17. Flexibility to add Hadoop applications to Amazon EMR Processing Databases Analytics
  18. 18. Amazon Machine Learning • Easy-to-use service built for developers • Robust, powerful, and technology-based • Ability to create models using your data • Deployable to production in seconds
  19. 19. Amazon Machine Learning Service
  20. 20. Amazon Machine Learning Service
  21. 21. Amazon Machine Learning Service
  22. 22. Amazon Machine Learning Service
  23. 23. Explore and Understand Your Data
  24. 24. Evaluate and Explore Model Performance
  25. 25. Putting It Together
  26. 26. Credit Card Transaction Dataset Customer Profile Store Profile Transaction Details
  27. 27. Amazon CloudWatch AWS CloudTrail AWS IAM Amazon RDS SSL/TLS Amazon Machine Learning SSL/TLS AWS Config AWS KMS EMR MLlib Corporate Data Center Amazon S3 Model Creation and Training – Reference Architecture AWS Direct Connect
  28. 28. IPSEC EMR Amazon RDS Amazon Machine Learning SSL/TLS SSL/TLS SSL/TLS SSL/TLS MLlib AWS Elastic Beanstalk App AWS Direct Connect Amazon CloudWatch AWS CloudTrail AWS IAM Amazon S3 AWS Config AWS KMS Online Fraud Detection – Reference Architecture Corporate Data Center
  29. 29. The Outcomes of the AWS Solution Cost: Solution price down from $100K to $10K Speed: Development down from months to days Resources: Focus shift from management to development
  30. 30. Next Evolution of the Platform
  31. 31. IPSEC EMR Amazon RDS Amazon Machine Learning SSL/TLS SSL/TLS SSL/TLS SSL/TLS MLlib AWS Elastic Beanstalk App AWS Direct Connect Amazon CloudWatch AWS CloudTrail AWS IAM Amazon S3 AWS Config AWS KMS Online Fraud Detection – Reference Architecture Corporate Data Center
  32. 32. Amazon RDS Amazon Machine Learning AWS Direct Connect AWS Lambda Amazon DynamoDB Amazon CloudWatch AWS CloudTrail AWS IAM AWS Config AWS KMS Amazon S3 Corporate Data Center Online Fraud Detection – Future State IPSEC SSL/TLS Amazon API Gateway
  33. 33. Other Sessions on Machine Learning CMP314 - Bringing Deep Learning to the Cloud with Amazon EC2 MAC206 - Machine Learning State of the Union MAC303 - Developing Classification and Recommendation Engines with Amazon EMR and Apache Spark
  34. 34. Thank you!
  35. 35. Remember to complete your evaluations!

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