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Reinventing Payments at HSBC with a Unified Platform for Data and AI in the Cloud

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Reinventing Payments at HSBC with a Unified Platform for Data and AI in the Cloud

  1. 1. Reinventing Payments at HSBC with a Unified Platform for Data and AI in the Cloud
  2. 2. HSBC—the world’s leading international bank 3,900 Offices DATA ASSETS 39 million Customers that bank with HSBC 66 Locations in countries and territories $53.8 billion In reported revenue $2.56 trillion Total assets 235,000 Employees around the world 56PB 77PB 169PB 2014 2015 2018 Data centers in 21 countries 94,000+ Servers
  3. 3. We’re on a mission to reinvent payments
  4. 4. This is how millions of people split checks, pay bills, give gifts, and buy coffee… …but it’s slow, unsafe and impersonal
  5. 5. So we invented a better way: social payments Send Money Securely & Instantly Pay People & Businesses Share Moments with Friends
  6. 6. We’ve seen massive adoption 1,800,000 1,600,000 1,400,000 1,200,000 1,000,000 800,000 600,000 400,000 200,000 0 Numberofregisteredaccounts PayMe Release February 2017 2/17 3/17 4/17 5/17 6/17 7/17 8/18 9/17 10/17 11/17 12/17 1/18 2/18 3/18 4/18 5/18 6/18 7/18 8/18 9/18 10/18 11/18 12/18 1/19 2/19 3/19 4/19 5/19 6/19 600k One year 1MM 16 months 1.4MM 2 years 1.7MM Today #1 in P2P with 60% share of P2P market in Q1 2019 Source: HKMA
  7. 7. How did we achieve this? DATA-DRIVEN PRODUCT INNOVATION
  8. 8. Machine Learning Classifies Payments (word-to-vec & emoji-to-vec Transaction_Amt Transaction_Message Class. 235.20 82.00 Dinner date 305.50 Car repair 1945.00 Disneyland tickets 45.00 Jen’s Birthday dinner 15.25 from party 1285.00 Beach rental 655.00 Apartment monthly bills 455.60 for New year’s party 67.00 31 Oct lunch with Michael 200.00 泰國菜 及 飲品 50.00 Borrow money for trip Dining with friends C2B payments Intra-household Purchases for friends Bill payment for friends Nano loan from friends Pay rent Sharing taxi ride Wedding Using machine learning to discover new features Identified 12 L1 Categories (so far!)
  9. 9. Machine Learning Classifies Payments (word-to-vec & emoji-to-vec Transaction_Amt Transaction_Message Class. 235.20 82.00 Dinner date 305.50 Car repair 1945.00 Disneyland tickets 45.00 Jen’s Birthday dinner 15.25 from party 1285.00 Beach rental 655.00 Apartment monthly bills 455.60 for New year’s party 67.00 31 Oct lunch with Michael 200.00 泰國菜 及 飲品 50.00 Borrow money for trip Dining with friends C2B payments Intra-household Purchases for friends Bill payment for friends Nano loan from friends Pay rent Sharing taxi ride Wedding Using machine learning to discover new features Identified 12 L1 Categories (so far!)
  10. 10. PayMe Sample User, Network in Jan 2019, 2 degrees of separation Using network science to understand our customers
  11. 11. 4 neighbours in common with Sample User Prospect A (18%) P2P Transactions Only one neighbour in common Prospect B (4%) 4.5x more likely than Prospect B to transact with Sample User within 3 months PayMe Sample User Using network science to understand our customers
  12. 12. But to succeed with machine learning and data science you need to solve for data Diverse data sources and datasets Timely insights Maintain security and data confidentiality Self-service analytics
  13. 13. Bringing this all together was more challenging than we thought...
  14. 14. API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage Our journey to ML-driven product innovation
  15. 15. DATA SCIENTISTS Our journey to ML-driven product innovation API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage
  16. 16. Our journey to ML-driven product innovation DATA SCIENTISTS API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage DATA ANALYSTS
  17. 17. Our journey to ML-driven product innovation DATA SCIENTISTS API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage DATA ANALYSTS DATA ENGINEERS
  18. 18. Our journey to ML-driven product innovation DATA SCIENTISTS API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage DATA ANALYSTS DATA ENGINEERS CHALLENGE #1: DATA Manually exporting and masking data Data is down sampled and weeks old on delivery
  19. 19. Our journey to ML-driven product innovation DATA SCIENTISTS API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage DATA ANALYSTS DATA ENGINEERS CHALLENGE #1: DATA Manually exporting and masking data Data is down sampled and weeks old on delivery CHALLENGE #2: ML Managing custom environments Slow iterations
  20. 20. Our journey to ML-driven product innovation DATA SCIENTISTS API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage DATA ANALYSTS DATA ENGINEERS CHALLENGE #1: DATA Manually exporting and masking data Data is down sampled and weeks old on delivery CHALLENGE #2: ML Managing custom environments Slow iterations CHALLENGE #3: INSIGHTS Stale analytics Not-predictive
  21. 21. PRODUCTION Real-time Data Masking No more forms or manual exports From 24 hours to immediate delivery No more stale or down sampled data HK_ID 203396(3) 349229(4) LAST NAME AGUILAR BENSON HK_ID 681000(8) 710043(1) LAST NAME ANSKEKSL BKJHHEIEDK Conquering our data challenge with Databricks API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage DISCOVERY
  22. 22. PRODUCTION Creating an agile environment for production ML API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage Collaborative Data Science & Engineering DATA MASKING DISCOVERY Data discovery Iterative feature engineering Model development Data pipeline development
  23. 23. Creating an agile environment for production ML c API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage Production Pipelines Collaborative Data Science & Engineering DATA MASKING PRODUCTION DISCOVERY MODEL DEPLOYMENT Model execution Data pipeline execution Data discovery Iterative feature engineering Model development Data pipeline development
  24. 24. Enabling business insights on all of our data API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage Production Pipelines Collaborative Data Science & Engineering DATA MASKING Richer business insights Full data now available Insights on streaming data as it arrives From descriptive to predictive analytics 14+ read replica databases to 1 unified lake PRODUCTION DISCOVERY MODEL DEPLOYMENT
  25. 25. Delivering product innovation with a unified approach to data analytics and AI API Azure Event Hub Azure SQL Azure Database for MySQL Azure Storage UNIFIED DATA ANALYTICS PLATFORM Secure, Analytics Ready Data Machine Learning Data Science and Analytics DATA SCIENTISTS DATA ANALYSTSDATA ENGINEERS
  26. 26. Reinventing Payments at HSBC Improved data timeliness data is available for advanced analytics, almost at the time it’s created Improved processing time from 6 hours to 6 seconds for certain complex analytics Productive Data teams same platform for collaboration, everyone still use preferred tools+libraries Data driven roadmap timely data and accurate analytics enable a data driven roadmap Not only analytics the platform enables a proposition where Insights is an integral part of the product offering

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