FSI202 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, you'll hear how IHS Markit has used Machine Learning on AWS to help global banking institutions manage their commodities portfolios. You will also learn how the Amazon Machine Learning Service can take the hassle out of AI.
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FSI202 Machine Learning in Capital Markets
- 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Frank Tarsillo, IHS Markit Peter Williams, AWS Financial Services Partners August 14, 2017 Machine Learning in Capital Markets
- 2. Takeaways from today’s session I. How is machine learning changing Financial Services? II. Case Study: IHS Markit’s commodity inventory solution III. How do customers get started? IV. Best practices when building machine learning in the cloud
- 3. How is machine learning changing Financial Services?
- 4. Artificial Intelligence A system or service that can perform tasks usually requiring human intelligence. AA Hardware + learning algorithms Fit a network structure to the data Fit a function To the data AI ML DL
- 5. The challenge: SCALE Data Training Prediction
- 6. The challenge: SCALE Data Training Prediction PBs and EBs of existing data Aggressive migration
- 7. The challenge: SCALE Data Training Prediction PBs and EBs of existing data Aggressive migration Tons of GPUs Elastic capacity Pre-built images
- 8. The challenge: 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
- 9. 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 - Tensor Flow 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
- 10. AWS has become the Center of Gravity for AI
- 11. Case Study
- 12. 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
- 13. Singapore 青岛 Vlissingen 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
- 14. 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
- 15. 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)
- 16. 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
- 17. P2 Instances ML product maintenance 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)
- 18. Result
- 19. Commodity tracker benefits of docs STP 80% improvement in timeliness 60% increase in efficiency 50% Focus human effort on the exceptions. Reduce risk by cutting the lag between data collection and use. Empower teams to focus energy on value-creation activities.
- 20. Getting Started with ML
- 21. 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 already stored in the AWS cloud Deploy models to production in seconds
- 22. 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
- 23. 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
- 24. 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
- 25. 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
- 26. 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
- 27. AWS AI: Democratized artificial intelligence
- 30. Train model Evaluate and optimize Retrieve predictions 1 2 3 Building smart applications with Amazon ML
- 31. Building smart applications with Amazon ML - Create a datasource object pointing to your data - Explore and understand your data - Transform data and train your model Train model Evaluate and optimize Retrieve predictions 1 2 3
- 32. Create a datasource object >>> import boto >>> ml = boto.connect_machinelearning() >>> ds = ml.create_data_source_from_s3( data_source_id = ’my_datasource', data_spec = { 'DataLocationS3': 's3://bucket/input/data.csv', 'DataSchemaLocationS3': 's3://bucket/input/data.schema', ’compute_statistics’: True } )
- 33. Explore and understand your data
- 34. Train your model >>> import boto >>> ml = boto.connect_machinelearning() >>> model = ml.create_ml_model( ml_model_id = ’my_model', ml_model_type = 'REGRESSION', training_data_source_id = 'my_datasource')
- 35. Building smart applications with Amazon ML - Measure and understand model quality - Adjust model interpretation Train model Evaluate and optimize Retrieve predictions 1 2 3
- 39. Building smart applications with Amazon ML - Batch predictions - Real-time predictions Train model Evaluate and optimize Retrieve predictions 1 2 3
- 40. Batch predictions Asynchronous, large-volume prediction generation Request through service console or API Best for applications that deal with batches of data records >>> import boto >>> ml = boto.connect_machinelearning() >>> model = ml.create_batch_prediction( batch_prediction_id = 'my_batch_prediction’, batch_prediction_data_source_id = ’my_datasource’, ml_model_id = ’my_model', output_uri = 's3://examplebucket/output/’)
- 41. Real-time predictions Synchronous, low-latency, high-throughput prediction generation Request through service API, server, or mobile SDKs Best for interaction applications that deal with individual data records >>> import boto >>> ml = boto.connect_machinelearning() >>> ml.predict( ml_model_id = ’my_model', predict_endpoint = ’example_endpoint’, record = {’key1':’value1’, ’key2':’value2’}) { 'Prediction': { 'predictedValue': 13.284348, 'details': { 'Algorithm': 'SGD', 'PredictiveModelType': 'REGRESSION’ } } }