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Mining Intelligent Insights: AI/ML for Financial Services

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At Amazon, we’ve been investing deeply in artificial intelligence for over 20 years. Machine learning (ML) algorithms drive many of our internal systems. It's also core to the capabilities our customers experience – from the path optimization in our fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, our drone initiative Prime Air, and our new retail experience Amazon Go. This is just the beginning. Our mission is to share our learnings and ML capabilities as fully managed services, and put them into the hands of every developer and data scientist.

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Mining Intelligent Insights: AI/ML for Financial Services

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Américo de Paula Solutions Architecture Manager - LATAM Mining intelligent insights: AI/ML for Financial Services
  2. 2. Breakthrough advances Optimization and automation AI and ML enable innovation at scale… New features for existing products “After decades of false starts, artificial intelligence is on the verge of a breakthrough, with the latest progress propelled by machine learning.” McKinsey Global Institute, Artificial Intelligence The Next Digital Frontier? June 2017
  3. 3. …and could revolutionize Financial Services The Economist, May 25, 2017 “AI could contribute up to $15.7 trillion to the global economy in 2030…. Healthcare, automotive and financial services are the sectors with the greatest potential for product enhancement and disruption due to AI.” Sizing the prize: What’s the real value of AI for your business and how can you capitalise? PwC report, June 2017 Immense opportunities… …but huge risks of disruption
  4. 4. The potential impact of AI/ML is enterprise-wide Compliance, Surveillance, and Fraud Detection Pricing and Product Recommendation Document Processing Trading Customer Experience • Credit card/account fraud detection • Anti-money laundering/ Sanctions • Investigations optimization • Sales practices/ transaction surveillance • Compliance processes optimization • Regulatory mapping • Enhanced customer service through voice services and chatbots • Call center optimization • Personal financial management • Loan/Insurance underwriting • Sales/recommendations of financial products • Credit assessments • Contract ingestion and analytics • Financial information extraction • Common financial instrument taxonomy • Corporate actions • Portfolio management/ robo-advising • Algorithmic trading • Sentiment/news analysis • Geospatial image analysis • Predictive grid computing capacity management AI/ML use cases are gaining traction in Financial Services
  5. 5. But overall the industry has been slow to invest Source: McKinsey Global Institute, Artificial Intelligence The Next Digital Frontier? An ambivalent response to AI • Strong overall appetite for adopting AI • History of digital investment and strong foundation for integrating AI technologies • Large volumes of data to support model training and development • Comparatively low investment in AI
  6. 6. What is preventing the industry from moving ahead? AI/ML expertise is rare Building and scaling AI/ML technology is hard Deploying and operating models in production is time-consuming and expensive A lack of cost-effective, easy-to-use, and scalable AI/ML services
  7. 7. AWS offers a range of solutions to make AI/ML more accessible PollyLex Rekognition Deep Learning FrameworksMachine Learning PlatformsAmazon AI/ML Services Usability/simplicity: leverages AWS AI/ML expertise Greater control: customer-specific models Amazon ML Spark & EMR Kinesis Batch ECS Customization of offerings at scale More personal and efficient customer interactions Operational efficiencies Novel investment/ trading opportunities Benefits for Financial Services Institutions and others...
  8. 8. Our deep experience with AI/ML differentiates our services Product recommendation engine Robot-enabled fulfillment centers New product categories Amazon has invested in AI/ML since our inception, and we share our knowledge and capabilities with our customers 20171995 Natural language processing-supported contact centers ML-driven supply chain and capacity planning Checkout-free shopping using deep learning
  9. 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  10. 10. And today, enterprises across industries run AI/ML on AWS
  11. 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  12. 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS Machine Learning Stack Frameworks & Infrastructure AWS Deep Learning AMI GPU (P3 Instances) Mobile CPU (C5 Instances) IoT (Greengrass) Vision: Rekognition Image Rekognition Video Speech: Polly Transcribe Language: Lex Translate Comprehend Apache MXNet PyTorch Cognitive Toolkit Keras Caffe2 & Caffe TensorFlow Gluon Application Services Platform Services Amazon Machine Learning Mechanical Turk Spark & EMR Amazon SageMaker AWS DeepLens
  13. 13. Fraud.net is running AI/ML on AWS to predict financial crime “Amazon Machine Learning helps us reduce complexity and make sense of emerging fraud patterns. We can see correlations we wouldn’t have been able to see otherwise and answer questions it would have taken us way too long to answer ourselves. ” Fraud.net is the world’s leading crowdsourced fraud prevention platform, aggregating and analyzing large amounts of fraud data from thousands of online merchants in real time. The platform protects more than 2 percent of all U.S. e- commerce. - Oliver Clark, CTO, Fraud.net • To address its scalability needs, Fraud.net chose AWS to host its customer platform, relying on services including DynamoDB, Lambda, S3, and Redshift • Recently, Fraud.net started using Amazon Machine Learning, which helps its developers build models and enables the use of APIs to get predictions for applications without having to deploy prediction generation code • Fraud.net can now easily launch and train new machine-learning models to target evolving forms of fraud • Using AWS, Fraud.net can maintain its fast application response times of under 200 milliseconds and save its customers about $1 million a week through fraud detection and prevention
  14. 14. BuildFax uses Amazon ML to help insurers avoid losses “Amazon Machine Learning democratizes the process of building predictive models. It’s easy and fast to use and has machine-learning best practices encapsulated in the product, which lets us deliver results significantly faster than in the past. ” BuildFax aggregates dispersed building permit data from across the United States and provides it to other businesses, especially insurance companies, and economic analysts. The company also tracks trends like housing remodels and new commercial construction. - Joe Emison, Founder & Chief Technology Officer, BuildFax • BuildFax’s core customer base is insurance companies, which spend billions of dollars annually on roof losses • The company initially built predictive models based on ZIP codes and other general data, but building the models was complex and the results did not provide enough differentiators • BuildFax now uses Amazon Machine Learning to provide roof-age and job-cost estimations for insurers and builders, with property-specific values that don’t need to rely on broad, ZIP code-level estimate • Models that previously took six months or longer to create are now complete in four weeks or fewer
  15. 15. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Adam Wenchel, VP of AI
  16. 16. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Remember the infrastructure Built on AWS ML takes time, and technical debt Democratize AI, responsibly Maximize scarce experts’ productivity There is a lot more than the ML model Machine Learning @ Capital One
  17. 17. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. The road to production is long and arduous Prepare for the journey
  18. 18. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Model selection and Hyper- parameter Tuning GPU Optimization Self-service Production is a hard place Continuous Monitoring Rapid ML model refit/deploy Capital One AI: The Road to Production
  19. 19. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Machine Learning Technology @ Capital One Built on AWS
  20. 20. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon AI AI Platforms AI Engines & InfraAI Services Capital One AI: Democratizing ML in a Well-Managed Way Experiment Management Logging Versioning Reproducibility Monitoring Optimize & Scale Model Optimization GPU saturation Data/Compute coordination CapitalOne
  21. 21. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Organizing for Success You want a Data Science Team, then what ?
  22. 22. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. People Machine Learning is inherently Interdisciplinary Physical Scientists Analysts Architects/ Integrators Computer Scientists
  23. 23. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. People Physical Scientists Analysts Architects/ Integrators Computer Scientists Used to solving problems by applying ML to sensors data Machine Learning is inherently Interdisciplinary
  24. 24. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. People Machine Learning is inherently Interdisciplinary Physical Scientists Analysts Architects/ Integrators Computer Scientists Used to solving problems by applying ML to sensors data Understand Business Problem Framing
  25. 25. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. People Machine Learning is inherently Interdisciplinary Physical Scientists Analysts Architects/ Integrators Computer Scientists Used to solving problems by applying ML to sensors data Understand Business Problem Framing Understand Computer Logic and ML Science
  26. 26. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. People Machine Learning is inherently Interdisciplinary Physical Scientists Analysts Architects/ Integrators Computer Scientists Used to solving problems by applying ML to sensors data Understand Business Problem Framing Understand Computer Logic and ML Science Can put everything together
  27. 27. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Capital One AI: How We Organize for Success Create a positive work environment for ML talent Centralize new technologiesAttract and motivate the right talent
  28. 28. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  29. 29. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Opportunity: Transform Financial Services Increase efficiency, creating value for our customers New ways of empowering customers to take control of their financial lives Create amazing customer experiences
  30. 30. @dmbanga @apwenchel© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  31. 31. Nima Najafi Scotiabank Senior Manager Data Science & Model Innovation Optimizing Payments Collections with Containers and Machine Learning
  32. 32. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Who is Scotiabank? • Third-largest bank in Canada • On Top 10 List Of World's Strongest Banks • Providing services in North America, Latin America, the Caribbean and Central America, and Asia-Pacific • 88,000 employees • $907B assets (as at July 31, 2016) • Dedicated to helping its 23 million customers become better off through a broad range of advice, products, and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets
  33. 33. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Business problem: Credit card portfolio has been growing in the past years, requiring more effective collections to handle the growing volume. Bank strategy: Grow digital presence in order to better serve our customers and play a leading role in transforming the banking sector for the digital age. What were we trying to solve? Acquisition Account management Collections
  34. 34. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Why start with payments collections? Quarter All Credit Cards Other 2017 Q2 2.21 2.47 1.96 2017 Q1 2.18 2.42 1.97 2016 Q4 2.16 2.37 1.97 2016 Q3 2.08 2.29 1.91 2016 Q2 2.04 2.20 1.89 2016 Q1 1.99 2.16 1.83 US delinquency rates In Canada, the average consumer balance for credit cards grew by 2.07% from Q2 2016 to Q2 2017. Canadian delinquency rates
  35. 35. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. What came before deep learning? • Models were more basic (logistic regression/linear regression) • Process was manual and time consuming • Complete data sources weren’t readily available • Less accurate predictions • Additional opportunities for financial savings
  36. 36. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Deep learning model Written in Python Takes ~300 credit bureau and internal variables as inputs Hyper-parameters (learning rate, batch size, #layers, #npl, #epochs, activation functions, ...) were selected via grid search 10-fold cross validation with stratification Percentage of charge-offs captured in bottom 10% Out-of-time dataset DeepLearni.ng model Incumbent (in-house) model Lift Nov-14 54.5% 45.3% 20.3% Jan-15 55.0% 46.2% 19.1% May-15 55.4% 46.8% 18.2% Aug-15 50.1% 42.1% 19.0%
  37. 37. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Results! • Create/orchestrate/terminate the application from Amazon EC2 Container Registry (Amazon ECR) to Amazon EC2 Container Service (Amazon ECS) • Synchronize the data between Amazon Simple Storage Service (Amazon S3) and GitHub LFS • Secure the cluster through IAM roles (cluster only accepts traffic from Scotiabank IPs) • Application is fully containerized and versioned • Changes to the cluster infrastructure are managed by pull requests only • Rapid development and traceability through “everything through code” paradigm
  38. 38. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Success story amzn.to/FSV305-WSJ
  39. 39. Alexa... 25,000+ skills
  40. 40. The Alexa Family
  41. 41. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. A Little About Ally… …A digitally disruptive, diversified financial services firm with a full suite of financial products and services including banking, auto finance, and mortgage offerings. Beyond its services, Ally is known for its unique culture, straight forward approach and customer-centric business philosophy.
  42. 42. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Ally Business Lines • Full-spectrum provider • New, used, leasing • Floor plan and commercial services • Dealer online services • Auto-focused insurance business • Direct banking platform • Deposit products: savings, checking, CDs, IRAs • Mobile banking • Credit card • Mortgage • Financing for mid-market companies in technology, healthcare, retail, and automotive • Digital portfolio management platform • Ally Invest Auto Finance Ally Bank Corporate Finance Wealth Management & Online Brokerage
  43. 43. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. The Design Journey
  44. 44. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  45. 45. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. * Solved through fine-tuning utterances
  46. 46. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Technology @ Work Amazon Alexa Amazon Developer Portal Voice Interaction Model Ally Interactive API (AWS Lambda) Transactions Accounts Welcome Transfers Rates CurrenSee Amazon CloudWatch AWS X-Ray AWS IAM AWS KMS Shared Services AuthN Token Secure Gateway Ally Microservices Enrollment APIs Step Up Auth AuthN APIs Banking APIs Notify Services
  47. 47. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. The Ally Skill "ALEXA, OPEN ALLY” “GET MY BALANCE” “HOW MUCH DID I SPEND LAST MONDAY?” “ALEXA, TRANSFER $10 FROM MY ALLY BANK [PRODUCT NAME] ACCOUNT TO MY [ELIGIBLE EXTERNAL ACCOUNT]” TO VERIFY IT'S YOU, TELL ME YOUR 6-DIGIT PASSCODE “WHAT'S TODAY’S RATE FOR A 12 MONTH HIGH YIELD CD” “CONVERT $300 INTO CURRENSEE”“ALEXA, ASK ALLY FOR HELP.”
  48. 48. Go to amzn.to/takeselfie
  49. 49. Américo de Paula Solutions Architecture Manager americop@amazon.com Worldwide | N. America | LATAM | UK/IR | EMEA | APAC | Japan | China

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