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Machine Learning in Financial Services: Real-World Use Cases

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Machine learning (ML) services on AWS create opportunities to automate and accelerate labor- and time-intensive processes, analyze large datasets, discover business insights, and mitigate security and market risks. Attend this talk to learn real-world ML use cases that financial services enterprises have built and deployed in the AWS Cloud, such as modernization of omni-channel call centers, analysis of unstructured datasets, automated fraud detection in payments, and predictive analytics for market risk mitigation.

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Machine Learning in Financial Services: Real-World Use Cases

  1. 1. © 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark John Kain, AWS FSI Business Development Capital Markets SIBOS 2019 Machine learning in Financial Services Real-world use case
  2. 2. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved Please remember that past performance may not be indicative of future results
  3. 3. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved Data every 5 years There is more data than people think 15 years live for Data platforms need to 1,000x scale >10x grows
  4. 4. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved 40% of digital transformation initiatives supported by AI in 2019 —IDC 2018 InnovationDecision making Customer experience Business operations Competitive advantage Data is the centerpiece for Digital Transformation
  5. 5. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved More (and better) customer data Ideation and experimentation Rapid product developmentContinuous deployment Enhanced customer experience Easy provisioning of resources Cloud technologies are accelerating this transformation
  6. 6. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved conversational chat bots | call transcription | intelligent routing | sentiment analysis VoC analytics | text-to speech | multilingual omni-channel communication 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 recommendation technology used by Amazon.com | context-aware recommendations sentiment analysis | VoC analytics | predict business outcomes P E R S O N A L I Z E C O M P R E H E N DR E K O G N I T I O N I M A G E R E K O G N I T I O N V I D E O Making machine learning accessible without data scientists F O R E C A S T
  7. 7. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved One-click model training and deployment Train once run anywhere 10x better algorithm performance 2x performance increases from model optimization with Neo 70% cost reduction for data labeling using Ground Truth 75% cost reduction for inference with Elastic Inference REDUCE COSTS IN C R E A SE P E R F O R M A N C E IMPROVE EASE OF USE AMAZON SAGEMAKER And increasing the effectiveness of data science teams
  8. 8. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved We’re on the cusp of a new age in Financial Services Streamlined payments What consumers see:
  9. 9. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved Compliance, surveillance, and fraud detection Document processing Pricing and product recommendation Trading and analytics AI/ML creates the next edge for Financial Institutions Customer experience • Account opening/ fraud detection • Sales practices/ transaction surveillance • AML/Sanctions • Investigations optimization • Regulatory mapping • Common financial instrument taxonomy • Contract ingestion and analytics • Financial information extraction • Corporate actions • Loan/Insurance underwriting • Sales/recommendations of financial products • Credit assessments • Portfolio management/ robo-advising • Algorithmic trading • Sentiment/news analysis • Image analysis • Grid computing scheduling • Enhanced customer service through mobile apps and chatbots • Call center optimization • Personal financial management
  10. 10. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved Accelerating investigation timelines FINRA uses Amazon Comprehend to process and review millions of documents with unstructured data, helping flag records of interest that should be reviewed by human investigators.
  11. 11. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved Preventing fraudulent attacks in real-time Using Amazon SageMaker, NuData Security prevents credit card fraud by analyzing anonymized user data to detect anomalous activity before a fraudulent transaction occurs. With SageMaker, NuData reduced machine learning development time by 60%, simplified their machine learning architecture by 95%, and worked with a large bank to passively block nearly 100% of fraudulent attempt traffic within the bank’s consumer friction tolerance.
  12. 12. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved Enforcing compliance at scale Coinbase uses machine learning models on Amazon SageMaker to help with fraud prevention, identity verification, and large-scale compliance. Using Amazon SageMaker reduced model training times from 20 hours to 10 minutes.
  13. 13. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved Fueling product innovation Using Amazon SageMaker, Intuit developed machine learning models that can pull a year’s worth of bank transactions to find deductible business expenses for customers. Using SageMaker, Intuit reduced machine learning deployment time by 90%, from 6 months to 1 week.
  14. 14. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved Improving customer communications FICO uses Amazon Polly to power a range of voice applications that improve the customer experience.
  15. 15. © 2019 Amazon Web Services, Inc. or its affiliates. All rights reserved “Go as far as you can see; when you get there, you’ll be able to see farther.” J.P. Morgan
  16. 16. Thank you!

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