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GPSTEC305-Machine Learning in Capital Markets

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Financial services companies are using machine learning to reduce fraud, streamline processes, and improve their bottom line. AWS provides tools that help them easily use AI tools like MXNet and Tensor Flow to perform predictive analytics, clustering, and more advanced data analyses. In this session, hear how IHS Markit has used machine learning on AWS to help global banking institutions manage their commodities portfolios. Learn how Amazon Machine Learning can take the hassle out of AI.

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GPSTEC305-Machine Learning in Capital Markets

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS re:INVENT Machine Learning in Capital Markets F r a n k T a r s i l l o , I H S M a r k i t P e t e r W i l l i a m s , A W S F i n a n c i a l S e r v i c e s N o v e m b e r 2 8 , 2 0 1 7
  2. 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Takeaways from today’s session 1. How is machine learning changing financial services? 2. Case study: IHS Markit’s commodity inventory solution 3. How do customers get started? 4. Best practices when building machine learning in the cloud
  3. 3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. How is machine learning changing financial services?
  4. 4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Artificial intelligence A system or service that can perform tasks that usually require human intelligence AA Hardware + learning algorithms Fit a network structure to the data Fit a function to the data AI ML DL
  5. 5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. T h e c h a l l e n g e : Scale Data Training Prediction PBs and EBs of existing data Aggressive migration Tons of GPUs Elastic capacity Pre-built images Tons of GPUs and CPUs Serverless At the edge, on IoT devices
  6. 6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Business group Use case ML models AWS tools Asset management Equity ratings Portfolio management Portfolio optimization Machine learning (clustering, SVM, logistic regression, Gaussian processes) Deep learning (embedding, matrix factorization, LSTM, conversational UI) Graphical models Amazon Machine Learning Deep Learning MXNet SparkML on Amazon EMR TensorFlow Banking Credit card/lending fraud Customer onboarding Digital banking Compliance Anti-money laundering, Counter-terrorism financing Due diligence Security Cyber-attack detection Data leak detection Virus detection Financial services use cases
  7. 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS has become the center of gravity for AI
  8. 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Case study
  9. 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Commodity inventory challenges Global warehouses are all different Diverse flow of paper documents, from warehouse inventory reports to bills of lading Digitize, classify, and aggregate the document flow Improve cost and risk management Localization diversity Eliminate error-prone manual operations
  10. 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Singapore Al 123.4 mT Vlissingen Al 234.5 mT Qingdao Al 567.8 mT Vendors Report generation Vendors continue to generate their reports as usual. Documents are redirected to a Markit server. Process Markit scrapes the critical data from the warehouse’s unique report using a variety of technologies, including OCR. Audit Markit stores a digital copy of the original document for auditing purposes. Disseminate The data is standardized for language and terminology, then compiled onto a customer portal. IHS Markit Trading firms Data access Dependent teams at the trading firms connect to the portal to pull a personalized view of the required data. Logistics teams Finance teams Compliance teams Schedulers/traders Commodity inventory solution Singapore Vlissingen 青岛
  11. 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. IHSM Product VPC IHSM Commodity Tracker IHSM Product VPC IHS Markit—cloud strategy Cloud-first strategy Cloud native vs. lift and shift Time-to-market focus Investment upfront Refreshing the estate Financial transparency A DevOps world Global scale Cost-savings in the end Global Physical network IHSM datacenter(s) Oracle SAN Existing IHSM product deployments Customer datacenter Services End user Services Encryption S3 SES Services Encryption Services Encryption RDS SESDynamicDB Shared services with scale (Iaas/SaaS/PaaS) Cloud enhancement Cloud enhancement
  12. 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Architecture approach Microservices design approach PaaS supporting orchestration and discovery OCR, classification, and analysis documents Cloud enablement with some lock-in API-driven Modern UI, SSO, preferences, notifications SDLC-Agile, CI/CD, high velocity of change Highly available (multi-AZ/global)
  13. 13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Machine learning enablement Document classification and templates to reduce exceptions Configuration data and models (Keras + TensorFlow) Use of AML-based images to tool vs. DIY AWS compute with GPU on-demand for trainer Data lake of document classifications (strategic) OCR Classification predictor QA reviewMLDoc APIs Document repository Classification trainer Corrected predictions
  14. 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. ML product maintenance P2 instances ML AMI ML AMI Production predictor (AWS Lambda) Job scheduler Train model with new data Design new model New document Prediction corrections Trained models Documents Results Data lake (Amazon S3)
  15. 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Result
  16. 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 80% of docs STP Focus human effort on the exceptions 60% improvement in timeliness Reduce risk by cutting the lag between data collection and use 50% increase in efficiency Empower teams to focus energy on value-creation activities Commodity tracker benefits
  17. 17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Getting started with ML
  18. 18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Machine Learning (Amazon ML) Easy-to-use, managed machine learning service built for developers Robust, powerful machine-learning technology based on Amazon’s internal systems Create models using your data that is already stored in the AWS cloud Deploy models to production in seconds
  19. 19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Easy to use and developer-friendly Use the intuitive, powerful service console to build and explore your initial models Data retrieval Model training, quality evaluation, fine-tuning Deployment and management Automate model lifecycle with fully featured APIs and SDKs Java, Python, .NET, JavaScript, Ruby, PHP Easily create smart iOS and Android applications with AWS Mobile SDK
  20. 20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Powerful machine learning technology Based on Amazon’s battle-hardened internal systems Not just the algorithms Smart data transformations Input data and model quality alerts Built-in industry best practices Grows with your needs Train on up to 100 GB of data Generate billions of predictions Obtain predictions in batches or real time
  21. 21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Integrated with the AWS data ecosystem Access data that is stored in Amazon S3, Amazon Redshift, or MySQL databases in Amazon RDS Output predictions to Amazon S3 for easy integration with your data flows Use AWS Identity and Access Management (IAM) for fine-grained data access permission policies
  22. 22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Fully managed model and prediction services End-to-end service with no servers to provision and manage One-click production model deployment Programmatically query model metadata to enable automatic retraining workflows Monitor prediction usage patterns with Amazon CloudWatch metrics
  23. 23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Three supported types of predictions Binary classification Predict the answer to a yes/no question Multiclass classification Predict the correct category from a list Regression Predict the value of a numeric variable
  24. 24. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Infrastructure Frameworks Platforms Amazon ML ECSSpark & EMR Kinesis Batch GPU MobileCPU IoT Services Amazon Lex (language) Amazon Polly (speech) Amazon Rekognition (vision) Apache MXNet Torch Cognitive toolkit KerasTheano Caffe2 & Caffe Tensor Flow AWS Deep Learning AMI Gluon A W S A I : Democratized artificial intelligence
  25. 25. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Key takeaways
  26. 26. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Key takeaways AWS machine-learning services can help transform your applications and improve your bottom line Get started today: - Try the AML tutorial at https://aws.amazon.com/aml/getting-started/
  27. 27. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Please remember to rate this session in the mobile app
  28. 28. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Thank you! F r a n k T a r s i l l o , f r a n k . t a r s i l l o @ i h s m a r k i t . c o m P e t e r W i l l i a m s , w i l l p e @ a m a z o n . c o m P l e a s e f i l l o u t a s u r v e y o f t h i s s e s s i o n : G P S T E C 3 0 5

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